fix

.github/workflows/docker-build.yml

@@ -195,7 +195,6 @@ jobs:
             type=ref,event=pr
             type=semver,pattern={{version}}
             type=semver,pattern={{major}}.{{minor}}
-            type=raw,value=latest,enable={{is_default_branch}}
           labels: |
             org.opencontainers.image.licenses=${{ env.EOS_LICENSE }}
           annotations: |

.github/workflows/stale.yml

@@ -1,35 +0,0 @@
-name: "Close stale pull requests/issues"
-on:
-  schedule:
-  - cron: "16 00 * * *"
-
-permissions:
-  contents: read
-
-jobs:
-  stale:
-    name: Find Stale issues and PRs
-    runs-on: ubuntu-22.04
-    if: github.repository == 'Akkudoktor-EOS/EOS'
-    permissions:
-      pull-requests: write # to comment on stale pull requests
-      issues: write        # to comment on stale issues
-
-    steps:
-    - uses: actions/stale@5bef64f19d7facfb25b37b414482c7164d639639 # v9.1.0
-      with:
-        stale-pr-message: 'This pull request has been marked as stale because it has been open (more
-          than) 90 days with no activity. Remove the stale label or add a comment saying that you
-          would like to have the label removed otherwise this pull request will automatically be
-          closed in 30 days. Note, that you can always re-open a closed pull request at any time.'
-        stale-issue-message: 'This issue has been marked as stale because it has been open (more
-          than) 90 days with no activity. Remove the stale label or add a comment saying that you
-          would like to have the label removed otherwise this issue will automatically be closed in
-          30 days. Note, that you can always re-open a closed issue at any time.'
-        days-before-stale: 90
-        days-before-close: 30
-        stale-issue-label: 'stale'
-        stale-pr-label: 'stale'
-        exempt-pr-labels: 'in progress'
-        exempt-issue-labels: 'feature request, enhancement'
-        operations-per-run: 400

.gitignore

@@ -179,7 +179,7 @@ cython_debug/
 #  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
 #  and can be added to the global gitignore or merged into this file.  For a more nuclear
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
-.idea/
+#.idea/
 
 # General
 .DS_Store

.gitlint

@@ -1,35 +0,0 @@
-[general]
-# verbosity should be a value between 1 and 3, the commandline -v flags take precedence over this
-verbosity = 3
-
-regex-style-search=true
-
-# Ignore rules, reference them by id or name (comma-separated)
-ignore=title-trailing-punctuation, T3
-
-# Enable specific community contributed rules
-contrib=contrib-title-conventional-commits,CC1
-
-# Set the extra-path where gitlint will search for user defined rules
-extra-path=scripts/gitlint
-
-[title-max-length]
-line-length=80
-
-[title-min-length]
-min-length=5
-
-[ignore-by-title]
-# Match commit titles starting with "Release"
-regex=^Release(.*)
-ignore=title-max-length,body-min-length
-
-[ignore-by-body]
-# Match commits message bodies that have a line that contains 'release'
-regex=(.*)release(.*)
-ignore=all
-
-[ignore-by-author-name]
-# Match commits by author name (e.g. ignore dependabot commits)
-regex=dependabot
-ignore=all

.pre-commit-config.yaml

@@ -1,9 +1,8 @@
 # Exclude some file types from automatic code style
 exclude: \.(json|csv)$
 repos:
-  # --- Basic sanity checks ---
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v6.0.0
+    rev: v5.0.0
     hooks:
       - id: check-merge-conflict
       - id: check-toml
@@ -11,70 +10,26 @@ repos:
       - id: end-of-file-fixer
       - id: trailing-whitespace
       - id: check-merge-conflict
-        exclude: '\.rst$'  # Exclude .rst files from whitespace cleanup
-
-  # --- Import sorting ---
+        exclude: '\.rst$'  # Exclude .rst files
   - repo: https://github.com/PyCQA/isort
-    rev: 7.0.0
+    rev: 6.0.0
     hooks:
       - id: isort
-
-  # --- Linting + Formatting via Ruff ---
+        name: isort
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.14.1
+    rev: v0.9.6
     hooks:
-      # Run the linter and fix simple isssues automatically
+      # Run the linter and fix simple issues automatically
       - id: ruff
         args: [--fix]
-      # Run the formatter
+      # Run the formatter.
       - id: ruff-format
-
-  # --- Static type checking ---
   - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v1.18.2
+    rev: 'v1.15.0'
     hooks:
       - id: mypy
         additional_dependencies:
-          - types-requests==2.32.4.20250913
-          - pandas-stubs==2.3.2.250926
-          - tokenize-rt==3.2.0
-        pass_filenames: false
-
-  # --- Markdown linter ---
-  - repo: https://github.com/jackdewinter/pymarkdown
-    rev: v0.9.32
-    hooks:
-      - id: pymarkdown
-        files: ^docs/
-        exclude: ^docs/_generated
-        args:
-          - --config=docs/pymarkdown.json
-          - scan
-
-  # --- Commit message linting ---
-  # - Local cross-platform hooks
-  - repo: local
-    hooks:
-      # Validate commit messages (using Python wrapper)
-      - id: commitizen-commit
-        name: Commitizen (venv-aware)
-        entry: python3 scripts/cz_check_commit_message.py
-        language: system
-        stages: [commit-msg]
-        pass_filenames: false
-
-      # Branch name check on push (using Python wrapper)
-      - id: commitizen-branch
-        name: Commitizen branch check
-        entry: python3 scripts/cz_check_branch.py
-        language: system
-        stages: [pre-push]
-        pass_filenames: false
-
-      # Validate new commit messages before push (using Python wrapper)
-      - id: commitizen-new-commits
-        name: Commitizen (check new commits only, .venv aware)
-        entry: python3 -m scripts.cz_check_new_commits
-        language: system
-        stages: [pre-push]
+          - "types-requests==2.32.0.20241016"
+          - "pandas-stubs==2.2.3.241009"
+          - "numpy==2.1.3"
         pass_filenames: false

CHANGELOG.md

@@ -1,277 +0,0 @@
-# Changelog
-
-All notable changes to the akkudoktoreos project will be documented in this file.
-
-The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
-and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
-
-## 0.2.0 (2025-11-09)
-
-The most important new feature is **automatic optimization**.
-EOS can now independently perform optimization at regular intervals.
-This is based on the configured system parameters and forecasts, and also uses supplied
-measurement data, such as the current battery SoC.
-The result is an energy-management plan as well as the optimization output.
-The existing optimization interface using `POST /optimize` remains available and can still
-be used as before.
-
-In addition, bugs were fixed and new features were added:
-
-- Automatic optimization creates a **default configuration** if none is provided.
-  This is intended to make it easier to create a custom configuration by adapting the default.
-- The parameters of the genetic optimization algorithm (number of generations, etc.) are now
-  configurable.
-- For home appliances, start windows can now be specified (experimental).
-- Configuration files from previous versions are converted to the current format on first launch.
-- There are now measurement keys that are permanently assigned to a specific device simulation.
-  This simplifies providing measurement values for device simulations (e.g. battery SoC).
-- The infrastructure and first applications for **feed-in tariff forecasting**
-  (currently only fixed tariffs) are now integrated.
-- EOSdash has been expanded with new tabs for displaying the **energy-management plan**
-  and **predictions**.
-- The documentation has been updated and expanded in many places.
-
-### Feat
-
-- Energy-management plan generation based on S2 standard instructions
-- Feed-in-tariff prediction support (incl. tests & docs)
-- `LoadAkkudoktorAdjusted` load prediction variant
-- Standardized measurement keys for battery/EV SoC
-- Measurement keys configurable via EOS configuration
-- Setup default device configuration for automatic optimization
-- Health endpoints show version + last optimization timestamps
-- Configuration of genetic algorithm parameters
-- Configuration options for home-appliance time windows
-- Mitigation of legacy configuration
-- Config backup enhancements:
-
-  - Timestamp-based backup IDs
-  - API to list backups
-  - API to revert to a specific backup
-  - EOSdash Admin tab integration
-
-- Pendulum date types via `pydantic_extra_types.pendulum_dt`
-- `Time`, `TimeWindow`, `TimeWindowSequence`, and `to_time` helpers in `datetimeutil`
-- Extended `DataRecord` with configurable field-like semantics
-- EOSdash: Solution view now displays genetic optimization results and aggregated totals
-- EOSdash UI:
-
-  - Plan tab
-  - Predictions tab
-  - Cache management in Admin tab
-  - About tab
-
-- Pydantic merge model tests
-- Developer profiling entry in Makefile
-- Changelog & docs updated for commitizen release flow
-- Developer documentation updated
-- Improved install & development documentation
-
-### Changed
-
-- Battery simulation
-
-  - Performance improvements
-  - Charge + start times now reflect realistic simulation
-
-- Appliance simulation:
-
-  - Time windows may roll over to next day
-
-- Revised load prediction by splitting original `LoadAkkudoktor` into:
-
-  - `LoadAkkudoktor`
-  - `LoadAkkudoktorAdjusted`
-
-### Fixed
-
-- Correct URL/path for Akkudoktor forum in README
-- Automatic optimization:
-
-  - Reuses previous start solution
-  - Interval execution + locking + new endpoints
-  - Properly loads required data
-  - EV charge-rate migration for proper availability
-
-- Genetic common settings consistently available
-- Config markdown generation
-- Recognize environment variables on EOS server startup
-- Remove `0.0.0.0 → localhost` translation on Windows
-- Allow hostnames as well as IPs
-- Access Pydantic model fields via class instead of instance
-- Down-sampling in `key_to_array`
-- `/v1/admin/cache/clear` clears all cache files; added `/clear-expired`
-- Use `tzfpy` instead of timezonefinder for more accurate EU timezones
-- Explicit provider settings in config instead of union
-- ClearOutside weather prediction irradiance calculation
-- Test config file priority without `config_eos` fixture
-- Complete optimization sample-request documentation
-- Replace gitlint with commitizen
-- Synchronize pre-commit config with real dependencies
-- Add missing `babel` to requirements
-- Fix documentation, tests, and implementation around optimization + predictions
-
-### Chore
-
-- Use memory cache for inverter interpolation
-- Refactor genetic modules (split config, remove device singleton)
-- Rename memory cache to `CacheEnergyManagementStore`
-- Use class properties for config/EMS/prediction mixins
-- Skip matplotlib debug logs
-- Auto-sync Bokeh JS CDN version
-- Rename `hello.py` → `about.py` in EOSdash
-- Remove EOSdash demo page
-- Split server test from system test
-- Move doc utils to `generate_config_md.py`
-- Improve documentation for pydantic merge models
-- Remove pendulum warning from README
-- Drop GitHub Discussions from contributing docs
-- Rename or reorganize files / classes during refactors
-
-### BREAKING CHANGES
-
-EOS configuration + v1 API have changed:
-
-- `available_charge_rates_percent` removed → replaced by `charge_rate`
-- Optimization parameter `hours` → renamed to `horizon_hours`
-- Device config must explicitly list devices + properties
-- Prediction providers now explicit (instead of union)
-- Measurement keys provided as lists
-- Feed-in-tariff providers must be explicitly configured
-- `/v1/measurement/loadxxx` endpoints removed → use generic measurement endpoints
-- `/v1/admin/cache/clear` now clears **all*- cache files;
-  `/v1/admin/cache/clear-expired` only clears expired entries
-
-## v0.1.0 (2025-09-30)
-
-### Feat
-
-- added Changelog for 0.0.0 and 0.1.0
-
-## v0.0.0 (2025-09-30)
-
-This version represents one year of development of EOS (Energy Optimization System). From this point forward, release management will be introduced.
-
-### Feat
-
-#### Core Features
-- energy Management System (EMS) with battery optimization
-- PV (Photovoltaic) forecast integration with multiple providers
-- load prediction and forecasting capabilities
-- electricity price integration
-- VRM API integration for load and PV forecasting
-- battery State of Charge (SoC) prediction and optimization
-- inverter class with AC/DC charging logic
-- electric vehicle (EV) charging optimization with configurable currents
-- home appliance scheduling optimization
-- horizon validation for shading calculations
-
-#### API & Server
-- migration from Flask to FastAPI
-- RESTful API with comprehensive endpoints
-- EOSdash web interface for configuration and visualization
-- Docker support with multi-architecture builds
-- web-based visualization with interactive charts
-- OpenAPI/Swagger documentation
-- configurable server settings (port, host)
-
-#### Configuration & Data Management
-- JSON-based configuration system with nested support
-- configuration validation with Pydantic
-- device registry for managing multiple devices
-- persistent caching for predictions and prices
-- manual prediction updates
-- timezone support with automatic detection
-- configurable VAT rates for electricity prices
-
-#### Optimization
-- DEAP-based genetic algorithm optimization
-- multi-objective optimization (cost, battery usage, self-consumption)
-- 48-hour prediction and optimization window
-- AC/DC charging decision optimization
-- discharge hour optimization
-- start solution enforcement
-- fitness visualization with violin plots
-- self-consumption probability interpolator
-
-#### Testing & Quality
-- comprehensive test suite with pytest
-- unit tests for major components (EMS, battery, inverter, load, optimization)
-- integration tests for server endpoints
-- pre-commit hooks for code quality
-- type checking with mypy
-- code formatting with ruff and isort
-- markdown linting
-
-#### Documentation
-- conceptual documentation
-- API documentation with Sphinx
-- ReadTheDocs integration
-- Docker setup instructions
-- contributing guidelines
-- English README translation
-
-#### Providers & Integrations
-- PVForecast.Akkudoktor provider
-- BrightSky weather provider
-- ClearOutside weather provider
-- electricity price provider
-
-### Refactor
-
-- optimized Inverter class for improved SCR calculation performance
-- improved caching mechanisms for better performance
-- enhanced visualization with proper timestamp handling
-- updated dependency management with automatic Dependabot updates
-- restructured code into logical submodules
-- package directory structure reorganization
-- improved error handling and logging
-- Windows compatibility improvements
-
-### Fix
-
-- cross-site scripting (XSS) vulnerabilities
-- ReDoS vulnerability in duration parsing
-- timezone and daylight saving time handling
-- BrightSky provider with None humidity data
-- negative values in load mean adjusted calculations
-- SoC calculation bugs
-- AC charge efficiency in price calculations
-- optimization timing bugs
-- Docker BuildKit compatibility
-- float value handling in user horizon configuration
-- circular runtime import issues
-- load simulation data return issues
-- multiple optimization-related bugs
-
-### Build
-
-- Python version requirement updated to 3.10+
-- added Bandit security checks
-- improved credential management with environment variables
-
-#### Dependencies
-Major dependencies included in this release:
-- FastAPI 0.115.14
-- Pydantic 2.11.9
-- NumPy 2.3.3
-- Pandas 2.3.2
-- Scikit-learn 1.7.2
-- Uvicorn 0.36.0
-- Bokeh 3.8.0
-- Matplotlib 3.10.6
-- PVLib 0.13.1
-- Python-FastHTML 0.12.29
-
-### Notes
-
-#### Development Notes
-This version encompasses all development from the initial commit (February 16, 2024) through September 29, 2025. The project evolved from a basic energy optimization concept to a comprehensive energy management system with:
-- 698+ commits
-- multiple contributor involvement
-- continuous integration/deployment setup
-- automated dependency updates
-- comprehensive testing infrastructure
-
-#### Migration Notes
-As this is the initial versioned release, no migration is required. Future releases will include migration guides as needed.

CONTRIBUTING.md

@@ -6,7 +6,7 @@ The `EOS` project is in early development, therefore we encourage contribution i
 
 ## Documentation
 
-Latest development documentation can be found at [Akkudoktor-EOS](https://akkudoktor-eos.readthedocs.io/en/latest/).
+Latest development documentation can be found at [Akkudoktor-EOS](https://akkudoktor-eos.readthedocs.io/en/main/).
 
 ## Bug Reports
 
@@ -14,20 +14,20 @@ Please report flaws or vulnerabilities in the [GitHub Issue Tracker](https://git
 
 ## Ideas & Features
 
-Issues in the [GitHub Issue Tracker](https://github.com/Akkudoktor-EOS/EOS/issues) are also fine
-to discuss ideas and features.
+Please first discuss the idea in a [GitHub Discussion](https://github.com/Akkudoktor-EOS/EOS/discussions) or the [Akkudoktor Forum](https://www.akkudoktor.net/forum/diy-energie-optimierungssystem-opensource-projekt/) before opening an issue.
 
-You may first discuss the idea in the [Akkudoktor Forum](https://www.akkudoktor.net/forum/diy-energie-optimierungssystem-opensource-projekt/) before opening an issue.
+There are just too many possibilities and the project would drown in tickets otherwise.
 
 ## Code Contributions
 
 We welcome code contributions and bug fixes via [Pull Requests](https://github.com/Akkudoktor-EOS/EOS/pulls).
-To make collaboration easier, we require pull requests to pass code style, unit tests, and commit
-message style checks.
+To make collaboration easier, we require pull requests to pass code style and unit tests.
+
 
 ### Setup development environment
 
 Setup virtual environment, then activate virtual environment and install development dependencies.
+See also [README.md](README.md).
 
 ```bash
 python -m venv .venv
@@ -60,7 +60,6 @@ To run formatting automatically before every commit:
 
 ```bash
 pre-commit install
-pre-commit install --hook-type commit-msg --hook-type pre-push
 ```
 
 Or run them manually:
@@ -76,18 +75,3 @@ Use `pytest` to run tests locally:
 ```bash
 python -m pytest -vs --cov src --cov-report term-missing tests/
 ```
-
-### Commit message style
-
-Our commit message checks use
-[`commitizen`](https://commitizen-tools.github.io/commitizen/#pre-commit-integration). The checks
-enforce the [`Conventional Commits`](https://www.conventionalcommits.org) commit message style.
-
-You may use [`commitizen`](https://commitizen-tools.github.io/commitizen) also to create a
-commit message and commit your change.
-
-## Thank you!
-
-And last but not least thanks to all our contributors
-
-[![Contributors](https://contrib.rocks/image?repo=Akkudoktor-EOS/EOS)](https://github.com/Akkudoktor-EOS/EOS/graphs/contributors)

Dockerfile

@@ -1,4 +1,3 @@
-# syntax=docker/dockerfile:1.7
 ARG PYTHON_VERSION=3.12.7
 FROM python:${PYTHON_VERSION}-slim
 
@@ -10,16 +9,6 @@ ENV EOS_CACHE_DIR="${EOS_DIR}/cache"
 ENV EOS_OUTPUT_DIR="${EOS_DIR}/output"
 ENV EOS_CONFIG_DIR="${EOS_DIR}/config"
 
-# Overwrite when starting the container in a production environment
-ENV EOS_SERVER__EOSDASH_SESSKEY=s3cr3t
-
-# Set environment variables to reduce threading needs
-ENV OPENBLAS_NUM_THREADS=1
-ENV OMP_NUM_THREADS=1
-ENV MKL_NUM_THREADS=1
-ENV PIP_PROGRESS_BAR=off
-ENV PIP_NO_COLOR=1
-
 WORKDIR ${EOS_DIR}
 
 RUN adduser --system --group --no-create-home eos \
@@ -35,26 +24,13 @@ RUN adduser --system --group --no-create-home eos \
 COPY requirements.txt .
 
 RUN --mount=type=cache,target=/root/.cache/pip \
-    pip install --no-cache-dir -r requirements.txt
+    pip install -r requirements.txt
 
 COPY pyproject.toml .
-RUN mkdir -p src && pip install --no-cache-dir -e .
+RUN mkdir -p src && pip install -e .
 
 COPY src src
 
-# Create minimal default configuration for Docker to fix EOSDash accessibility (#629)
-# This ensures EOSDash binds to 0.0.0.0 instead of 127.0.0.1 in containers
-RUN echo '{\n\
-  "server": {\n\
-    "host": "0.0.0.0",\n\
-    "port": 8503,\n\
-    "startup_eosdash": true,\n\
-    "eosdash_host": "0.0.0.0",\n\
-    "eosdash_port": 8504\n\
-  }\n\
-}' > "${EOS_CONFIG_DIR}/EOS.config.json" \
-    && chown eos:eos "${EOS_CONFIG_DIR}/EOS.config.json"
-
 USER eos
 ENTRYPOINT []
 

Makefile

@@ -1,5 +1,5 @@
 # Define the targets
-.PHONY: help venv pip install dist test test-full test-system test-ci test-profile docker-run docker-build docs read-docs clean format gitlint mypy run run-dev run-dash run-dash-dev bumps
+.PHONY: help venv pip install dist test test-full docker-run docker-build docs read-docs clean format mypy run run-dev
 
 # Default target
 all: help
@@ -11,27 +11,20 @@ help:
 	@echo "  pip          - Install dependencies from requirements.txt."
 	@echo "  pip-dev      - Install dependencies from requirements-dev.txt."
 	@echo "  format       - Format source code."
-	@echo "  gitlint      - Lint last commit message."
 	@echo "  mypy         - Run mypy."
 	@echo "  install      - Install EOS in editable form (development mode) into virtual environment."
 	@echo "  docker-run   - Run entire setup on docker"
 	@echo "  docker-build - Rebuild docker image"
 	@echo "  docs         - Generate HTML documentation (in build/docs/html/)."
 	@echo "  read-docs    - Read HTML documentation in your browser."
-	@echo "  gen-docs     - Generate openapi.json and docs/_generated/*."
-	@echo "  clean-docs   - Remove generated documentation."
+	@echo "  gen-docs     - Generate openapi.json and docs/_generated/*.""
+	@echo "  clean-docs   - Remove generated documentation.""
 	@echo "  run          - Run EOS production server in virtual environment."
 	@echo "  run-dev      - Run EOS development server in virtual environment (automatically reloads)."
 	@echo "  run-dash     - Run EOSdash production server in virtual environment."
 	@echo "  run-dash-dev - Run EOSdash development server in virtual environment (automatically reloads)."
-	@echo "  test         - Run tests."
-	@echo "  test-full    - Run tests with full optimization."
-	@echo "  test-system  - Run tests with system tests enabled."
-	@echo "  test-ci      - Run tests as CI does. No user config file allowed."
-	@echo "  test-profile - Run single test optimization with profiling."
 	@echo "  dist         - Create distribution (in dist/)."
 	@echo "  clean        - Remove generated documentation, distribution and virtual environment."
-	@echo "  bump         - Bump version to next release version."
 
 # Target to set up a Python 3 virtual environment
 venv:
@@ -50,7 +43,7 @@ pip-dev: pip
 	@echo "Dependencies installed from requirements-dev.txt."
 
 # Target to install EOS in editable form (development mode) into virtual environment.
-install: pip-dev
+install: pip
 	.venv/bin/pip install build
 	.venv/bin/pip install -e .
 	@echo "EOS installed in editable form (development mode)."
@@ -79,11 +72,6 @@ read-docs: docs
 	@echo "Read the documentation in your browser"
 	.venv/bin/python -m webbrowser build/docs/html/index.html
 
-# Clean Python bytecode
-clean-bytecode:
-	find . -type d -name "__pycache__" -exec rm -r {} +
-	find . -type f -name "*.pyc" -delete
-
 # Clean target to remove generated documentation and documentation artefacts
 clean-docs:
 	@echo "Searching and deleting all '_autosum' directories in docs..."
@@ -103,7 +91,7 @@ run:
 
 run-dev:
 	@echo "Starting EOS development server, please wait..."
-	.venv/bin/python -m akkudoktoreos.server.eos --host localhost --port 8503 --log_level DEBUG --startup_eosdash false --reload true
+	.venv/bin/python -m akkudoktoreos.server.eos --host localhost --port 8503 --reload true
 
 run-dash:
 	@echo "Starting EOSdash production server, please wait..."
@@ -111,7 +99,7 @@ run-dash:
 
 run-dash-dev:
 	@echo "Starting EOSdash development server, please wait..."
-	.venv/bin/python -m akkudoktoreos.server.eosdash --host localhost --port 8504 --log_level DEBUG --reload true
+	.venv/bin/python -m akkudoktoreos.server.eosdash --host localhost --port 8504 --reload true
 
 # Target to setup tests.
 test-setup: pip-dev
@@ -122,34 +110,15 @@ test:
 	@echo "Running tests..."
 	.venv/bin/pytest -vs --cov src --cov-report term-missing
 
-# Target to run tests as done by CI on Github.
-test-ci:
-	@echo "Running tests as CI..."
-	.venv/bin/pytest --full-run --check-config-side-effect -vs --cov src --cov-report term-missing
-
-# Target to run tests including the system tests.
-test-system:
-	@echo "Running tests incl. system tests..."
-	.venv/bin/pytest --system-test -vs --cov src --cov-report term-missing
-
 # Target to run all tests.
 test-full:
 	@echo "Running all tests..."
 	.venv/bin/pytest --full-run
 
-# Target to run tests including the single test optimization with profiling.
-test-profile:
-	@echo "Running single test optimization with profiling..."
-	.venv/bin/python tests/single_test_optimization.py --profile
-
 # Target to format code.
 format:
 	.venv/bin/pre-commit run --all-files
 
-# Target to trigger gitlint using pre-commit for the latest commit messages
-gitlint:
-	.venv/bin/cz check --rev-range main..HEAD
-
 # Target to format code.
 mypy:
 	.venv/bin/mypy
@@ -160,17 +129,3 @@ docker-run:
 
 docker-build:
 	@docker compose build --pull
-
-# Bump Akkudoktoreos version
-VERSION ?= 0.2.0
-NEW_VERSION ?= $(VERSION)+dev
-
-bump: pip-dev
-	@echo "Bumping akkudoktoreos version from $(VERSION) to $(NEW_VERSION) (dry-run: $(EXTRA_ARGS))"
-	.venv/bin/python scripts/convert_lightweight_tags.py
-	.venv/bin/python scripts/bump_version.py $(VERSION) $(NEW_VERSION) $(EXTRA_ARGS)
-
-bump-dry: pip-dev
-	@echo "Bumping akkudoktoreos version from $(VERSION) to $(NEW_VERSION) (dry-run: --dry-run)"
-	.venv/bin/python scripts/convert_lightweight_tags.py
-	.venv/bin/python scripts/bump_version.py $(VERSION) $(NEW_VERSION) --dry-run

README.md

@@ -1,158 +1,106 @@
-![AkkudoktorEOS](./docs/_static/logo.png#gh-light-mode-only)
-![AkkudoktorEOS](./docs/_static/logo_dark.png#gh-dark-mode-only)
+# Energy System Simulation and Optimization
 
-**Build optimized energy management plans for your home automation**
+This project provides a comprehensive solution for simulating and optimizing an energy system based on renewable energy sources. With a focus on photovoltaic (PV) systems, battery storage (batteries), load management (consumer requirements), heat pumps, electric vehicles, and consideration of electricity price data, this system enables forecasting and optimization of energy flow and costs over a specified period.
 
-AkkudoktorEOS is a comprehensive solution for simulating and optimizing energy systems based on
-renewable sources. Optimize your photovoltaic systems, battery storage, load management, and
-electric vehicles while considering real-time electricity pricing.
+Documentation can be found at [Akkudoktor-EOS](https://akkudoktor-eos.readthedocs.io/en/latest/).
 
-## Why use AkkudoktorEOS?
+## Getting Involved
 
-AkkudoktorEOS can be used to build energy management plans that are optimized for your specific
-setup of PV system, battery, electric vehicle, household load and electricity pricing. It can
-be integrated into home automation systems such as NodeRED, Home Assistant, EVCC.
-
-## 🏘️ Community
-
-We are an open-source community-driven project and we love to hear from you. Here are some ways to
-get involved:
-
-- [GitHub Issue Tracker](https://github.com/Akkudoktor-EOS/EOS/issues): discuss ideas and features,
-and report bugs.
-
-- [Akkudoktor Forum](https://www.akkudoktor.net/c/der-akkudoktor/eos): get direct suppport from the
-cummunity.
-
-## What do people build with AkkudoktorEOS
-
-The community uses AkkudoktorEOS to minimize grid energy consumption and to maximize the revenue
-from grid energy feed in with their home automation system.
-
-- Andreas Schmitz, [the Akkudoktor](https://www.youtube.com/@Akkudoktor), uses
-  EOS integrated in his NodeRED home automation system for
-  [OpenSource Energieoptimierung](https://www.youtube.com/watch?v=sHtv0JCxAYk).
-- Jörg, [meintechblog](https://www.youtube.com/@meintechblog), uses EOS for
-  day-ahead optimization for time-variable energy prices. See:
-  [So installiere ich EOS von Andreas Schmitz](https://www.youtube.com/watch?v=9XCPNU9UqSs)
-
-## Why not use AkkudoktorEOS?
-
-AkkudoktorEOS does not control your home automation assets. It must be integrated into a home
-automation system. If you do not use a home automation system or you feel uncomfortable with
-the configuration effort needed for the integration you should better use other solutions.
-
-## Quick Start
-
-Run EOS with Docker (access dashboard at `http://localhost:8504`):
-
-```bash
-docker run -d \
-  --name akkudoktoreos \
-  -p 8503:8503 \
-  -p 8504:8504 \
-  -e OPENBLAS_NUM_THREADS=1 \
-  -e OMP_NUM_THREADS=1 \
-  -e MKL_NUM_THREADS=1 \
-  -e EOS_SERVER__HOST=0.0.0.0 \
-  -e EOS_SERVER__EOSDASH_HOST=0.0.0.0 \
-  -e EOS_SERVER__EOSDASH_PORT=8504 \
-  --ulimit nproc=65535:65535 \
-  --ulimit nofile=65535:65535 \
-  --security-opt seccomp=unconfined \
-  akkudoktor/eos:latest
-```
-
-## System Requirements
-
-- **Python**: 3.11 or higher
-- **Architecture**: amd64, aarch64 (armv8)
-- **OS**: Linux, Windows, macOS
-
-> **Note**: Other architectures (armv6, armv7) require manual compilation of dependencies with Rust and GCC.
+See [CONTRIBUTING.md](CONTRIBUTING.md).
 
 ## Installation
 
-### Docker (Recommended)
+The project requires Python 3.10 or newer. Official docker images can be found at [akkudoktor/eos](https://hub.docker.com/r/akkudoktor/eos).
 
-```bash
-docker pull akkudoktor/eos:latest
-docker compose up -d
-```
+Following sections describe how to locally start the EOS server on `http://localhost:8503`.
 
-Access the API at `http://localhost:8503` (docs at `http://localhost:8503/docs`)
+### Run from source
 
-### From Source
+Install dependencies in virtual environment:
 
-```bash
-git clone https://github.com/Akkudoktor-EOS/EOS.git
-cd EOS
-```
-
-**Linux:**
+Linux:
 
 ```bash
 python -m venv .venv
 .venv/bin/pip install -r requirements.txt
 .venv/bin/pip install -e .
-.venv/bin/python -m akkudoktoreos.server.eos
 ```
 
-**Windows:**
+Windows:
 
 ```cmd
 python -m venv .venv
 .venv\Scripts\pip install -r requirements.txt
 .venv\Scripts\pip install -e .
-.venv\Scripts\python -m akkudoktoreos.server.eos
 ```
 
+Finally, start the EOS server:
+
+Linux:
+
+```bash
+.venv/bin/python src/akkudoktoreos/server/eos.py
+```
+
+Windows:
+
+```cmd
+.venv\Scripts\python src/akkudoktoreos/server/eos.py
+```
+
+### Docker
+
+```bash
+docker compose up
+```
+
+If you are running the EOS container on a system hosting multiple services, such as a Synology NAS, and want to allow external network access to EOS, please ensure that the default exported ports (8503, 8504) are available on the host. On Synology systems, these ports might already be in use (refer to [this guide](https://kb.synology.com/en-me/DSM/tutorial/What_network_ports_are_used_by_Synology_services)). If the ports are occupied, you will need to reconfigure the exported ports accordingly.
+
 ## Configuration
 
-EOS uses `EOS.config.json` for configuration. If the file doesn't exist, a default configuration is
-created automatically.
+This project uses the `EOS.config.json` file to manage configuration settings.
 
-### Custom Configuration Directory
+### Default Configuration
 
-```bash
-export EOS_DIR=/path/to/your/config
-```
+A default configuration file `default.config.json` is provided. This file contains all the necessary configuration keys with their default values.
 
-### Configuration Methods
+### Custom Configuration
 
-1. **EOSdash** (Recommended) - Web interface at `http://localhost:8504`
-2. **Manual** - Edit `EOS.config.json` directly
-3. **API** - Use the [Server API](https://petstore3.swagger.io/?url=https://raw.githubusercontent.com/Akkudoktor-EOS/EOS/refs/heads/main/openapi.json)
+Users can specify a custom configuration directory by setting the environment variable `EOS_DIR`.
 
-See the [documentation](https://akkudoktor-eos.readthedocs.io/) for all configuration options.
+- If the directory specified by `EOS_DIR` contains an existing `config.json` file, the application will use this configuration file.
+- If the `EOS.config.json` file does not exist in the specified directory, the `default.config.json` file will be copied to the directory as `EOS.config.json`.
 
-## Port Configuration
+### Configuration Updates
 
-**Default ports**: 8503 (API), 8504 (Dashboard)
+If the configuration keys in the `EOS.config.json` file are missing or different from those in `default.config.json`, they will be automatically updated to match the default settings, ensuring that all required keys are present.
 
-If running on shared systems (e.g., Synology NAS), these ports may conflict with system services. Reconfigure port mappings as needed:
+## Classes and Functionalities
 
-```bash
-docker run -p 8505:8503 -p 8506:8504 ...
-```
+This project uses various classes to simulate and optimize the components of an energy system. Each class represents a specific aspect of the system, as described below:
 
-## API Documentation
+- `Battery`: Simulates a battery storage system, including capacity, state of charge, and now charge and discharge losses.
 
-Interactive API docs available at:
-- Swagger UI: `http://localhost:8503/docs`
-- OpenAPI Spec: [View Online](https://petstore3.swagger.io/?url=https://raw.githubusercontent.com/Akkudoktor-EOS/EOS/refs/heads/main/openapi.json)
+- `PVForecast`: Provides forecast data for photovoltaic generation, based on weather data and historical generation data.
 
-## Resources
+- `Load`: Models the load requirements of a household or business, enabling the prediction of future energy demand.
 
-- [Full Documentation](https://akkudoktor-eos.readthedocs.io/)
-- [Installation Guide (German)](https://www.youtube.com/watch?v=9XCPNU9UqSs)
+- `Heatpump`: Simulates a heat pump, including its energy consumption and efficiency under various operating conditions.
 
-## Contributing
+- `Strompreis`: Provides information on electricity prices, enabling optimization of energy consumption and generation based on tariff information.
 
-We welcome contributions! See [CONTRIBUTING](CONTRIBUTING.md) for guidelines.
+- `EMS`: The Energy Management System (EMS) coordinates the interaction between the various components, performs optimization, and simulates the operation of the entire energy system.
 
-[![Contributors](https://contrib.rocks/image?repo=Akkudoktor-EOS/EOS)](https://github.com/Akkudoktor-EOS/EOS/graphs/contributors)
+These classes work together to enable a detailed simulation and optimization of the energy system. For each class, specific parameters and settings can be adjusted to test different scenarios and strategies.
 
-## License
+### Customization and Extension
 
-This project is licensed under the Apache License 2.0 - see the LICENSE file for details.
+Each class is designed to be easily customized and extended to integrate additional functions or improvements. For example, new methods can be added for more accurate modeling of PV system or battery behavior. Developers are invited to modify and extend the system according to their needs.
+
+## Server API
+
+See the Swagger API documentation for detailed information: [EOS OpenAPI Spec](https://petstore3.swagger.io/?url=https://raw.githubusercontent.com/Akkudoktor-EOS/EOS/refs/heads/main/openapi.json)
+
+## Further resources
+
+- [Installation guide (de)](https://meintechblog.de/2024/09/05/andreas-schmitz-joerg-installiert-mein-energieoptimierungssystem/)

docker-compose.yaml

@@ -5,7 +5,6 @@ networks:
 services:
   eos:
     image: "akkudoktor/eos:${EOS_VERSION}"
-    container_name: "akkudoktoreos"
     read_only: true
     build:
       context: .
@@ -15,43 +14,12 @@ services:
     env_file:
       - .env
     environment:
-      - OPENBLAS_NUM_THREADS=1
-      - OMP_NUM_THREADS=1
-      - MKL_NUM_THREADS=1
-      - PIP_PROGRESS_BAR=off
-      - PIP_NO_COLOR=1
       - EOS_CONFIG_DIR=config
       - EOS_SERVER__EOSDASH_SESSKEY=s3cr3t
-      - EOS_SERVER__HOST=0.0.0.0
-      - EOS_SERVER__PORT=8503
-      - EOS_SERVER__EOSDASH_HOST=0.0.0.0
-      - EOS_SERVER__EOSDASH_PORT=8504
-    ulimits:
-      nproc: 65535
-      nofile: 65535
-    security_opt:
-      - seccomp:unconfined
-    restart: unless-stopped
+      - EOS_PREDICTION__LATITUDE=52.2
+      - EOS_PREDICTION__LONGITUDE=13.4
+      - EOS_ELECPRICE__PROVIDER=ElecPriceAkkudoktor
+      - EOS_ELECPRICE__CHARGES_KWH=0.21
     ports:
-      # Configure what ports to expose on host
-      - "${EOS_SERVER__PORT}:8503"
-      - "${EOS_SERVER__EOSDASH_PORT}:8504"
-
-    # Volume mount configuration (optional)
-    # IMPORTANT: When mounting local directories, the default config won't be available.
-    # You must create an EOS.config.json file in your local config directory with:
-    # {
-    #   "server": {
-    #     "host": "0.0.0.0",        # Required for Docker container accessibility
-    #     "port": 8503,
-    #     "startup_eosdash": true,
-    #     "eosdash_host": "0.0.0.0", # Required for Docker container accessibility
-    #     "eosdash_port": 8504
-    #   }
-    # }
-    #
-    # Example volume mounts (uncomment to use):
-    # volumes:
-    #   - ./config:/opt/eos/config     # Mount local config directory
-    #   - ./cache:/opt/eos/cache       # Mount local cache directory
-    #   - ./output:/opt/eos/output     # Mount local output directory
+      - "${EOS_SERVER__PORT}:${EOS_SERVER__PORT}"
+      - "${EOS_SERVER__EOSDASH_PORT}:${EOS_SERVER__EOSDASH_PORT}"

docs/_generated/openapi.md

@@ -1,6 +1,6 @@
 # Akkudoktor-EOS
 
-**Version**: `v0.2.0`
+**Version**: `0.0.1`
 
 **Description**: This project provides a comprehensive solution for simulating and optimizing an energy system based on renewable energy sources. With a focus on photovoltaic (PV) systems, battery storage (batteries), load management (consumer requirements), heat pumps, electric vehicles, and consideration of electricity price data, this system enables forecasting and optimization of energy flow and costs over a specified period.
 
@@ -24,12 +24,10 @@ If no prediction values are available the missing ones at the start of the serie
 filled with the first available prediction value.
 
 Note:
-    Use '/v1/prediction/list?key=loadforecast_power_w' instead.
+    Use '/v1/prediction/list?key=load_mean_adjusted' instead.
     Load energy meter readings to be added to EOS measurement by:
-    '/v1/measurement/value' or
-    '/v1/measurement/series' or
-    '/v1/measurement/dataframe' or
-    '/v1/measurement/data'
+    '/v1/measurement/load-mr/value/by-name' or
+    '/v1/measurement/value'
 ```
 
 **Request Body**:
@@ -68,7 +66,7 @@ Note:
     Set LoadAkkudoktor as provider, then update data with
     '/v1/prediction/update'
     and then request data with
-    '/v1/prediction/list?key=loadforecast_power_w' instead.
+    '/v1/prediction/list?key=load_mean' instead.
 ```
 
 **Parameters**:
@@ -89,25 +87,16 @@ Note:
 
 Fastapi Optimize
 
-```
-Deprecated: Optimize.
-
-Endpoint to handle optimization.
-
-Note:
-    Use automatic optimization instead.
-```
-
 **Parameters**:
 
 - `start_hour` (query, optional): Defaults to current hour of the day.
 
-- `ngen` (query, optional): Number of indivuals to generate for genetic algorithm.
+- `ngen` (query, optional): No description provided.
 
 **Request Body**:
 
 - `application/json`: {
-  "$ref": "#/components/schemas/GeneticOptimizationParameters"
+  "$ref": "#/components/schemas/OptimizationParameters"
 }
 
 **Responses**:
@@ -202,40 +191,28 @@ Returns:
 
 Fastapi Admin Cache Clear Post
 
-```
-Clear the cache.
-
-Deletes all cache files.
-
-Returns:
-    data (dict): The management data after cleanup.
-```
-
-**Responses**:
-
-- **200**: Successful Response
-
----
-
-## POST /v1/admin/cache/clear-expired
-
-**Links**: [local](http://localhost:8503/docs#/default/fastapi_admin_cache_clear_expired_post_v1_admin_cache_clear-expired_post), [eos](https://petstore3.swagger.io/?url=https://raw.githubusercontent.com/Akkudoktor-EOS/EOS/refs/heads/main/openapi.json#/default/fastapi_admin_cache_clear_expired_post_v1_admin_cache_clear-expired_post)
-
-Fastapi Admin Cache Clear Expired Post
-
 ```
 Clear the cache from expired data.
 
 Deletes expired cache files.
 
+Args:
+    clear_all (Optional[bool]): Delete all cached files. Default is False.
+
 Returns:
     data (dict): The management data after cleanup.
 ```
 
+**Parameters**:
+
+- `clear_all` (query, optional): No description provided.
+
 **Responses**:
 
 - **200**: Successful Response
 
+- **422**: Validation Error
+
 ---
 
 ## POST /v1/admin/cache/load
@@ -363,25 +340,6 @@ Returns:
 
 ---
 
-## GET /v1/config/backup
-
-**Links**: [local](http://localhost:8503/docs#/default/fastapi_config_backup_get_v1_config_backup_get), [eos](https://petstore3.swagger.io/?url=https://raw.githubusercontent.com/Akkudoktor-EOS/EOS/refs/heads/main/openapi.json#/default/fastapi_config_backup_get_v1_config_backup_get)
-
-Fastapi Config Backup Get
-
-```
-Get the EOS configuration backup identifiers and backup metadata.
-
-Returns:
-    dict[str, dict[str, Any]]: Mapping of backup identifiers to metadata.
-```
-
-**Responses**:
-
-- **200**: Successful Response
-
----
-
 ## PUT /v1/config/file
 
 **Links**: [local](http://localhost:8503/docs#/default/fastapi_config_file_put_v1_config_file_put), [eos](https://petstore3.swagger.io/?url=https://raw.githubusercontent.com/Akkudoktor-EOS/EOS/refs/heads/main/openapi.json#/default/fastapi_config_file_put_v1_config_file_put)
@@ -420,31 +378,6 @@ Returns:
 
 ---
 
-## PUT /v1/config/revert
-
-**Links**: [local](http://localhost:8503/docs#/default/fastapi_config_revert_put_v1_config_revert_put), [eos](https://petstore3.swagger.io/?url=https://raw.githubusercontent.com/Akkudoktor-EOS/EOS/refs/heads/main/openapi.json#/default/fastapi_config_revert_put_v1_config_revert_put)
-
-Fastapi Config Revert Put
-
-```
-Revert the configuration to a EOS configuration backup.
-
-Returns:
-    configuration (ConfigEOS): The current configuration after revert.
-```
-
-**Parameters**:
-
-- `backup_id` (query, required): EOS configuration backup ID.
-
-**Responses**:
-
-- **200**: Successful Response
-
-- **422**: Validation Error
-
----
-
 ## GET /v1/config/{path}
 
 **Links**: [local](http://localhost:8503/docs#/default/fastapi_config_get_key_v1_config__path__get), [eos](https://petstore3.swagger.io/?url=https://raw.githubusercontent.com/Akkudoktor-EOS/EOS/refs/heads/main/openapi.json#/default/fastapi_config_get_key_v1_config__path__get)
@@ -497,13 +430,7 @@ Returns:
 **Request Body**:
 
 - `application/json`: {
-  "anyOf": [
-    {},
-    {
-      "type": "null"
-    }
-  ],
-  "description": "The value to assign to the specified configuration path (can be None).",
+  "description": "The value to assign to the specified configuration path.",
   "title": "Value"
 }
 
@@ -515,38 +442,6 @@ Returns:
 
 ---
 
-## GET /v1/energy-management/optimization/solution
-
-**Links**: [local](http://localhost:8503/docs#/default/fastapi_energy_management_optimization_solution_get_v1_energy-management_optimization_solution_get), [eos](https://petstore3.swagger.io/?url=https://raw.githubusercontent.com/Akkudoktor-EOS/EOS/refs/heads/main/openapi.json#/default/fastapi_energy_management_optimization_solution_get_v1_energy-management_optimization_solution_get)
-
-Fastapi Energy Management Optimization Solution Get
-
-```
-Get the latest solution of the optimization.
-```
-
-**Responses**:
-
-- **200**: Successful Response
-
----
-
-## GET /v1/energy-management/plan
-
-**Links**: [local](http://localhost:8503/docs#/default/fastapi_energy_management_plan_get_v1_energy-management_plan_get), [eos](https://petstore3.swagger.io/?url=https://raw.githubusercontent.com/Akkudoktor-EOS/EOS/refs/heads/main/openapi.json#/default/fastapi_energy_management_plan_get_v1_energy-management_plan_get)
-
-Fastapi Energy Management Plan Get
-
-```
-Get the latest energy management plan.
-```
-
-**Responses**:
-
-- **200**: Successful Response
-
----
-
 ## GET /v1/health
 
 **Links**: [local](http://localhost:8503/docs#/default/fastapi_health_get_v1_health_get), [eos](https://petstore3.swagger.io/?url=https://raw.githubusercontent.com/Akkudoktor-EOS/EOS/refs/heads/main/openapi.json#/default/fastapi_health_get_v1_health_get)
@@ -563,55 +458,6 @@ Health check endpoint to verify that the EOS server is alive.
 
 ---
 
-## GET /v1/logging/log
-
-**Links**: [local](http://localhost:8503/docs#/default/fastapi_logging_get_log_v1_logging_log_get), [eos](https://petstore3.swagger.io/?url=https://raw.githubusercontent.com/Akkudoktor-EOS/EOS/refs/heads/main/openapi.json#/default/fastapi_logging_get_log_v1_logging_log_get)
-
-Fastapi Logging Get Log
-
-```
-Get structured log entries from the EOS log file.
-
-Filters and returns log entries based on the specified query parameters. The log
-file is expected to contain newline-delimited JSON entries.
-
-Args:
-    limit (int): Maximum number of entries to return.
-    level (Optional[str]): Filter logs by severity level (e.g., DEBUG, INFO).
-    contains (Optional[str]): Return only logs that include this string in the message.
-    regex (Optional[str]): Return logs that match this regular expression in the message.
-    from_time (Optional[str]): ISO 8601 timestamp to filter logs not older than this.
-    to_time (Optional[str]): ISO 8601 timestamp to filter logs not newer than this.
-    tail (bool): If True, fetch the most recent log entries (like `tail`).
-
-Returns:
-    JSONResponse: A JSON list of log entries.
-```
-
-**Parameters**:
-
-- `limit` (query, optional): Maximum number of log entries to return.
-
-- `level` (query, optional): Filter by log level (e.g., INFO, ERROR).
-
-- `contains` (query, optional): Filter logs containing this substring.
-
-- `regex` (query, optional): Filter logs by matching regex in message.
-
-- `from_time` (query, optional): Start time (ISO format) for filtering logs.
-
-- `to_time` (query, optional): End time (ISO format) for filtering logs.
-
-- `tail` (query, optional): If True, returns the most recent lines (tail mode).
-
-**Responses**:
-
-- **200**: Successful Response
-
-- **422**: Validation Error
-
----
-
 ## PUT /v1/measurement/data
 
 **Links**: [local](http://localhost:8503/docs#/default/fastapi_measurement_data_put_v1_measurement_data_put), [eos](https://petstore3.swagger.io/?url=https://raw.githubusercontent.com/Akkudoktor-EOS/EOS/refs/heads/main/openapi.json#/default/fastapi_measurement_data_put_v1_measurement_data_put)
@@ -676,6 +522,82 @@ Get a list of available measurement keys.
 
 ---
 
+## GET /v1/measurement/load-mr/series/by-name
+
+**Links**: [local](http://localhost:8503/docs#/default/fastapi_measurement_load_mr_series_by_name_get_v1_measurement_load-mr_series_by-name_get), [eos](https://petstore3.swagger.io/?url=https://raw.githubusercontent.com/Akkudoktor-EOS/EOS/refs/heads/main/openapi.json#/default/fastapi_measurement_load_mr_series_by_name_get_v1_measurement_load-mr_series_by-name_get)
+
+Fastapi Measurement Load Mr Series By Name Get
+
+```
+Get the meter reading of given load name as series.
+```
+
+**Parameters**:
+
+- `name` (query, required): Load name.
+
+**Responses**:
+
+- **200**: Successful Response
+
+- **422**: Validation Error
+
+---
+
+## PUT /v1/measurement/load-mr/series/by-name
+
+**Links**: [local](http://localhost:8503/docs#/default/fastapi_measurement_load_mr_series_by_name_put_v1_measurement_load-mr_series_by-name_put), [eos](https://petstore3.swagger.io/?url=https://raw.githubusercontent.com/Akkudoktor-EOS/EOS/refs/heads/main/openapi.json#/default/fastapi_measurement_load_mr_series_by_name_put_v1_measurement_load-mr_series_by-name_put)
+
+Fastapi Measurement Load Mr Series By Name Put
+
+```
+Merge the meter readings series of given load name into EOS measurements at given datetime.
+```
+
+**Parameters**:
+
+- `name` (query, required): Load name.
+
+**Request Body**:
+
+- `application/json`: {
+  "$ref": "#/components/schemas/PydanticDateTimeSeries"
+}
+
+**Responses**:
+
+- **200**: Successful Response
+
+- **422**: Validation Error
+
+---
+
+## PUT /v1/measurement/load-mr/value/by-name
+
+**Links**: [local](http://localhost:8503/docs#/default/fastapi_measurement_load_mr_value_by_name_put_v1_measurement_load-mr_value_by-name_put), [eos](https://petstore3.swagger.io/?url=https://raw.githubusercontent.com/Akkudoktor-EOS/EOS/refs/heads/main/openapi.json#/default/fastapi_measurement_load_mr_value_by_name_put_v1_measurement_load-mr_value_by-name_put)
+
+Fastapi Measurement Load Mr Value By Name Put
+
+```
+Merge the meter reading of given load name and value into EOS measurements at given datetime.
+```
+
+**Parameters**:
+
+- `datetime` (query, required): Datetime.
+
+- `name` (query, required): Load name.
+
+- `value` (query, required): No description provided.
+
+**Responses**:
+
+- **200**: Successful Response
+
+- **422**: Validation Error
+
+---
+
 ## GET /v1/measurement/series
 
 **Links**: [local](http://localhost:8503/docs#/default/fastapi_measurement_series_get_v1_measurement_series_get), [eos](https://petstore3.swagger.io/?url=https://raw.githubusercontent.com/Akkudoktor-EOS/EOS/refs/heads/main/openapi.json#/default/fastapi_measurement_series_get_v1_measurement_series_get)
@@ -688,7 +610,7 @@ Get the measurements of given key as series.
 
 **Parameters**:
 
-- `key` (query, required): Measurement key.
+- `key` (query, required): Prediction key.
 
 **Responses**:
 
@@ -710,7 +632,7 @@ Merge measurement given as series into given key.
 
 **Parameters**:
 
-- `key` (query, required): Measurement key.
+- `key` (query, required): Prediction key.
 
 **Request Body**:
 
@@ -740,7 +662,7 @@ Merge the measurement of given key and value into EOS measurements at given date
 
 - `datetime` (query, required): Datetime.
 
-- `key` (query, required): Measurement key.
+- `key` (query, required): Prediction key.
 
 - `value` (query, required): No description provided.
 
@@ -821,8 +743,7 @@ Args:
       "$ref": "#/components/schemas/PydanticDateTimeData"
     },
     {
-      "type": "object",
-      "additionalProperties": true
+      "type": "object"
     },
     {
       "type": "null"
@@ -1013,96 +934,6 @@ Args:
 
 ---
 
-## GET /v1/resource/status
-
-**Links**: [local](http://localhost:8503/docs#/default/fastapi_devices_status_get_v1_resource_status_get), [eos](https://petstore3.swagger.io/?url=https://raw.githubusercontent.com/Akkudoktor-EOS/EOS/refs/heads/main/openapi.json#/default/fastapi_devices_status_get_v1_resource_status_get)
-
-Fastapi Devices Status Get
-
-```
-Get the latest status of a resource/ device.
-
-Return:
-    latest_status: The latest status of a resource/ device.
-```
-
-**Parameters**:
-
-- `resource_id` (query, required): Resource ID.
-
-- `actuator_id` (query, optional): Actuator ID.
-
-**Responses**:
-
-- **200**: Successful Response
-
-- **422**: Validation Error
-
----
-
-## PUT /v1/resource/status
-
-**Links**: [local](http://localhost:8503/docs#/default/fastapi_devices_status_put_v1_resource_status_put), [eos](https://petstore3.swagger.io/?url=https://raw.githubusercontent.com/Akkudoktor-EOS/EOS/refs/heads/main/openapi.json#/default/fastapi_devices_status_put_v1_resource_status_put)
-
-Fastapi Devices Status Put
-
-```
-Update the status of a resource/ device.
-
-Return:
-    latest_status: The latest status of a resource/ device.
-```
-
-**Parameters**:
-
-- `resource_id` (query, required): Resource ID.
-
-- `actuator_id` (query, optional): Actuator ID.
-
-**Request Body**:
-
-- `application/json`: {
-  "anyOf": [
-    {
-      "$ref": "#/components/schemas/PowerMeasurement-Input"
-    },
-    {
-      "$ref": "#/components/schemas/EnergyMeasurement-Input"
-    },
-    {
-      "$ref": "#/components/schemas/PPBCPowerProfileStatus-Input"
-    },
-    {
-      "$ref": "#/components/schemas/OMBCStatus"
-    },
-    {
-      "$ref": "#/components/schemas/FRBCActuatorStatus"
-    },
-    {
-      "$ref": "#/components/schemas/FRBCEnergyStatus-Input"
-    },
-    {
-      "$ref": "#/components/schemas/FRBCStorageStatus"
-    },
-    {
-      "$ref": "#/components/schemas/FRBCTimerStatus"
-    },
-    {
-      "$ref": "#/components/schemas/DDBCActuatorStatus"
-    }
-  ],
-  "description": "Resource Status.",
-  "title": "Status"
-}
-
-**Responses**:
-
-- **200**: Successful Response
-
-- **422**: Validation Error
-
----
-
 ## GET /visualization_results.pdf
 
 **Links**: [local](http://localhost:8503/docs#/default/get_pdf_visualization_results_pdf_get), [eos](https://petstore3.swagger.io/?url=https://raw.githubusercontent.com/Akkudoktor-EOS/EOS/refs/heads/main/openapi.json#/default/get_pdf_visualization_results_pdf_get)

docs/akkudoktoreos/about.md

@@ -0,0 +1,9 @@
+% SPDX-License-Identifier: Apache-2.0
+
+# About Akkudoktor EOS
+
+The Energy System Simulation and Optimization System (EOS) provides a comprehensive solution for
+simulating and optimizing an energy system based on renewable energy sources. With a focus on
+photovoltaic (PV) systems, battery storage (batteries), load management (consumer requirements),
+heat pumps, electric vehicles, and consideration of electricity price data, this system enables
+forecasting and optimization of energy flow and costs over a specified period.

docs/akkudoktoreos/architecture.md

@@ -20,22 +20,17 @@ EOS Architecture
 
 ### Configuration
 
-The configuration controls all aspects of EOS: optimization, prediction, measurement, and energy
-management.
+The configuration controls all aspects of EOS: optimization, prediction, measurement, and energy management.
 
 ### Energy Management
 
-Energy management is the overall process to provide planning data for scheduling the different
-devices in your system in an optimal way. Energy management cares for the update of predictions and
-the optimization of the planning based on the simulated behavior of the devices. The planning is on
-the hour.
+Energy management is the overall process to provide planning data for scheduling the different devices in your system in an optimal way. Energy management cares for the update of predictions and the optimization of the planning based on the simulated behavior of the devices. The planning is on the hour. Sub-hour energy management is left
 
 ### Optimization
 
 ### Device Simulations
 
-Device simulations simulate devices' behavior based on internal logic and predicted data. They
-provide the data needed for optimization.
+Device simulations simulate devices' behavior based on internal logic and predicted data. They provide the data needed for optimization.
 
 ### Predictions
 
@@ -43,8 +38,7 @@ Predictions provide predicted future data to be used by the optimization.
 
 ### Measurements
 
-Measurements are utilized to refine predictions using real data from your system, thereby enhancing
-accuracy.
+Measurements are utilized to refine predictions using real data from your system, thereby enhancing accuracy.
 
 ### EOS Server
 

docs/akkudoktoreos/configtimewindow.md

@@ -1,849 +0,0 @@
-% SPDX-License-Identifier: Apache-2.0
-(configtimewindow-page)=
-
-# Time Window Sequence Configuration
-
-## Overview
-
-The `TimeWindowSequence` model is used to configure allowed time slots for home appliance runs.
-It contains a collection of `TimeWindow` objects that define when appliances can operate.
-
-## Basic Structure
-
-A `TimeWindowSequence` is configured as a JSON object with a `windows` array:
-
-```json
-{
-  "windows": [
-    {
-      "start_time": "09:00",
-      "duration": "PT2H",
-      "day_of_week": null,
-      "date": null,
-      "locale": null
-    }
-  ]
-}
-```
-
-## TimeWindow Fields
-
-Each `TimeWindow` object has the following fields:
-
-- **`start_time`** (required): Time when the window begins
-- **`duration`** (required): How long the window lasts
-- **`day_of_week`** (optional): Restrict to specific day of week
-- **`date`** (optional): Restrict to specific calendar date
-- **`locale`** (optional): Language for day name parsing
-
-## Time Formats
-
-### Start Time (`start_time`)
-
-The `start_time` field accepts various time formats:
-
-#### 24-Hour Format
-
-```json
-{
-  "start_time": "14:30"        // 2:30 PM
-}
-```
-
-#### 12-Hour Format with AM/PM
-
-```json
-{
-  "start_time": "2:30 PM"      // 2:30 PM
-}
-```
-
-#### Compact Format
-
-```json
-{
-  "start_time": "1430"         // 2:30 PM
-}
-```
-
-#### With Seconds
-
-```json
-{
-  "start_time": "14:30:45"     // 2:30:45 PM
-}
-```
-
-#### With Microseconds
-
-```json
-{
-  "start_time": "14:30:45.123456"
-}
-```
-
-#### European Format
-
-```json
-{
-  "start_time": "14h30"        // 2:30 PM
-}
-```
-
-#### Short Formats
-
-```json
-{
-  "start_time": "14"           // 2:00 PM
-}
-```
-
-```json
-{
-  "start_time": "2PM"          // 2:00 PM
-}
-```
-
-#### Decimal Time
-
-```json
-{
-  "start_time": "14.5"         // 2:30 PM (14:30)
-}
-```
-
-#### With Timezones
-
-```json
-{
-  "start_time": "14:30 UTC"
-}
-```
-
-```json
-{
-  "start_time": "2:30 PM EST"
-}
-```
-
-```json
-{
-  "start_time": "14:30 +05:30"
-}
-```
-
-### Duration (`duration`)
-
-The `duration` field supports multiple formats for maximum flexibility:
-
-#### ISO 8601 Duration Format (Recommended)
-
-```json
-{
-  "duration": "PT2H30M"        // 2 hours 30 minutes
-}
-```
-
-```json
-{
-  "duration": "PT3H"           // 3 hours
-}
-```
-
-```json
-{
-  "duration": "PT90M"          // 90 minutes
-}
-```
-
-```json
-{
-  "duration": "PT1H30M45S"     // 1 hour 30 minutes 45 seconds
-}
-```
-
-#### Human-Readable String Format
-
-The system accepts natural language duration strings:
-
-```json
-{
-  "duration": "2 hours 30 minutes"
-}
-```
-
-```json
-{
-  "duration": "3 hours"
-}
-```
-
-```json
-{
-  "duration": "90 minutes"
-}
-```
-
-```json
-{
-  "duration": "1 hour 30 minutes 45 seconds"
-}
-```
-
-```json
-{
-  "duration": "2 days 5 hours"
-}
-```
-
-```json
-{
-  "duration": "1 day 2 hours 30 minutes"
-}
-```
-
-#### Singular and Plural Forms
-
-Both singular and plural forms are supported:
-
-```json
-{
-  "duration": "1 day"          // Singular
-}
-```
-
-```json
-{
-  "duration": "2 days"         // Plural
-}
-```
-
-```json
-{
-  "duration": "1 hour"         // Singular
-}
-```
-
-```json
-{
-  "duration": "5 hours"        // Plural
-}
-```
-
-#### Numeric Formats
-
-##### Seconds as Integer
-
-```json
-{
-  "duration": 3600             // 3600 seconds = 1 hour
-}
-```
-
-```json
-{
-  "duration": 1800             // 1800 seconds = 30 minutes
-}
-```
-
-##### Seconds as Float
-
-```json
-{
-  "duration": 3600.5           // 3600.5 seconds = 1 hour 0.5 seconds
-}
-```
-
-##### Tuple Format [days, hours, minutes, seconds]
-
-```json
-{
-  "duration": [0, 2, 30, 0]    // 0 days, 2 hours, 30 minutes, 0 seconds
-}
-```
-
-```json
-{
-  "duration": [1, 0, 0, 0]     // 1 day
-}
-```
-
-```json
-{
-  "duration": [0, 0, 45, 30]   // 45 minutes 30 seconds
-}
-```
-
-```json
-{
-  "duration": [2, 5, 15, 45]   // 2 days, 5 hours, 15 minutes, 45 seconds
-}
-```
-
-#### Mixed Time Units
-
-You can combine different time units in string format:
-
-```json
-{
-  "duration": "1 day 4 hours 30 minutes 15 seconds"
-}
-```
-
-```json
-{
-  "duration": "3 days 2 hours"
-}
-```
-
-```json
-{
-  "duration": "45 minutes 30 seconds"
-}
-```
-
-#### Common Duration Examples
-
-##### Short Durations
-
-```json
-{
-  "duration": "30 minutes"     // Quick appliance cycle
-}
-```
-
-```json
-{
-  "duration": "PT30M"          // ISO format equivalent
-}
-```
-
-```json
-{
-  "duration": 1800             // Numeric equivalent (seconds)
-}
-```
-
-##### Medium Durations
-
-```json
-{
-  "duration": "2 hours 15 minutes"
-}
-```
-
-```json
-{
-  "duration": "PT2H15M"        // ISO format equivalent
-}
-```
-
-```json
-{
-  "duration": [0, 2, 15, 0]    // Tuple format equivalent
-}
-```
-
-##### Long Durations
-
-```json
-{
-  "duration": "1 day 8 hours"  // All-day appliance window
-}
-```
-
-```json
-{
-  "duration": "PT32H"          // ISO format equivalent
-}
-```
-
-```json
-{
-  "duration": [1, 8, 0, 0]     // Tuple format equivalent
-}
-```
-
-#### Validation Rules for Duration
-
-- **ISO 8601 format**: Must start with `PT` and use valid duration specifiers (H, M, S)
-- **String format**: Must contain valid time units (day/days, hour/hours, minute/minutes, second/seconds)
-- **Numeric format**: Must be a positive number representing seconds
-- **Tuple format**: Must be exactly 4 elements: [days, hours, minutes, seconds]
-- **All formats**: Duration must be positive (greater than 0)
-
-#### Duration Format Recommendations
-
-1. **Use ISO 8601 format** for API consistency: `"PT2H30M"`
-2. **Use human-readable strings** for configuration files: `"2 hours 30 minutes"`
-3. **Use numeric format** for programmatic calculations: `9000` (seconds)
-4. **Use tuple format** for structured data: `[0, 2, 30, 0]`
-
-#### Error Handling for Duration
-
-Common duration errors and solutions:
-
-- **Invalid ISO format**: Ensure proper `PT` prefix and valid specifiers
-- **Unknown time units**: Use day/days, hour/hours, minute/minutes, second/seconds
-- **Negative duration**: All durations must be positive
-- **Invalid tuple length**: Tuple must have exactly 4 elements
-- **String too long**: Duration strings have a maximum length limit for security
-
-## Day of Week Restrictions
-
-### Using Numbers (0=Monday, 6=Sunday)
-
-```json
-{
-  "day_of_week": 0             // Monday
-}
-```
-
-```json
-{
-  "day_of_week": 6             // Sunday
-}
-```
-
-### Using English Day Names
-
-```json
-{
-  "day_of_week": "Monday"
-}
-```
-
-```json
-{
-  "day_of_week": "sunday"      // Case insensitive
-}
-```
-
-### Using Localized Day Names
-
-```json
-{
-  "day_of_week": "Montag",     // German for Monday
-  "locale": "de"
-}
-```
-
-```json
-{
-  "day_of_week": "Lundi",      // French for Monday
-  "locale": "fr"
-}
-```
-
-## Date Restrictions
-
-### Specific Date
-
-```json
-{
-  "date": "2024-12-25"         // Christmas Day 2024
-}
-```
-
-**Note**: When `date` is specified, `day_of_week` is ignored.
-
-## Complete Examples
-
-### Example 1: Basic Daily Window
-
-Allow appliance to run between 9:00 AM and 11:00 AM every day:
-
-```json
-{
-  "windows": [
-    {
-      "start_time": "09:00",
-      "duration": "PT2H"
-    }
-  ]
-}
-```
-
-### Example 2: Weekday Only
-
-Allow appliance to run between 8:00 AM and 6:00 PM on weekdays:
-
-```json
-{
-  "windows": [
-    {
-      "start_time": "08:00",
-      "duration": "PT10H",
-      "day_of_week": 0
-    },
-    {
-      "start_time": "08:00",
-      "duration": "PT10H",
-      "day_of_week": 1
-    },
-    {
-      "start_time": "08:00",
-      "duration": "PT10H",
-      "day_of_week": 2
-    },
-    {
-      "start_time": "08:00",
-      "duration": "PT10H",
-      "day_of_week": 3
-    },
-    {
-      "start_time": "08:00",
-      "duration": "PT10H",
-      "day_of_week": 4
-    }
-  ]
-}
-```
-
-### Example 3: Multiple Daily Windows
-
-Allow appliance to run during morning and evening hours:
-
-```json
-{
-  "windows": [
-    {
-      "start_time": "06:00",
-      "duration": "PT3H"
-    },
-    {
-      "start_time": "18:00",
-      "duration": "PT4H"
-    }
-  ]
-}
-```
-
-### Example 4: Weekend Special Hours
-
-Different hours for weekdays and weekends:
-
-```json
-{
-  "windows": [
-    {
-      "start_time": "08:00",
-      "duration": "PT8H",
-      "day_of_week": "Monday"
-    },
-    {
-      "start_time": "08:00",
-      "duration": "PT8H",
-      "day_of_week": "Tuesday"
-    },
-    {
-      "start_time": "08:00",
-      "duration": "PT8H",
-      "day_of_week": "Wednesday"
-    },
-    {
-      "start_time": "08:00",
-      "duration": "PT8H",
-      "day_of_week": "Thursday"
-    },
-    {
-      "start_time": "08:00",
-      "duration": "PT8H",
-      "day_of_week": "Friday"
-    },
-    {
-      "start_time": "10:00",
-      "duration": "PT6H",
-      "day_of_week": "Saturday"
-    },
-    {
-      "start_time": "10:00",
-      "duration": "PT6H",
-      "day_of_week": "Sunday"
-    }
-  ]
-}
-```
-
-### Example 5: Holiday Schedule
-
-Special schedule for a specific date:
-
-```json
-{
-  "windows": [
-    {
-      "start_time": "10:00",
-      "duration": "PT4H",
-      "date": "2024-12-25"
-    }
-  ]
-}
-```
-
-### Example 6: Localized Configuration
-
-Using German day names:
-
-```json
-{
-  "windows": [
-    {
-      "start_time": "14:00",
-      "duration": "PT2H",
-      "day_of_week": "Montag",
-      "locale": "de"
-    },
-    {
-      "start_time": "14:00",
-      "duration": "PT2H",
-      "day_of_week": "Mittwoch",
-      "locale": "de"
-    },
-    {
-      "start_time": "14:00",
-      "duration": "PT2H",
-      "day_of_week": "Freitag",
-      "locale": "de"
-    }
-  ]
-}
-```
-
-### Example 7: Complex Schedule with Timezones
-
-Multiple windows with different timezones:
-
-```json
-{
-  "windows": [
-    {
-      "start_time": "09:00 UTC",
-      "duration": "PT4H",
-      "day_of_week": "Monday"
-    },
-    {
-      "start_time": "2:00 PM EST",
-      "duration": "PT3H",
-      "day_of_week": "Friday"
-    }
-  ]
-}
-```
-
-### Example 8: Night Shift Schedule
-
-Crossing midnight (note: each window is within a single day):
-
-```json
-{
-  "windows": [
-    {
-      "start_time": "22:00",
-      "duration": "PT2H"
-    },
-    {
-      "start_time": "00:00",
-      "duration": "PT6H"
-    }
-  ]
-}
-```
-
-## Advanced Usage Patterns
-
-### Off-Peak Hours
-
-Configure appliance to run during off-peak electricity hours:
-
-```json
-{
-  "windows": [
-    {
-      "start_time": "23:00",
-      "duration": "PT1H"
-    },
-    {
-      "start_time": "00:00",
-      "duration": "PT7H"
-    }
-  ]
-}
-```
-
-### Workday Lunch Break
-
-Allow appliance to run during lunch break on workdays:
-
-```json
-{
-  "windows": [
-    {
-      "start_time": "12:00",
-      "duration": "PT1H",
-      "day_of_week": 0
-    },
-    {
-      "start_time": "12:00",
-      "duration": "PT1H",
-      "day_of_week": 1
-    },
-    {
-      "start_time": "12:00",
-      "duration": "PT1H",
-      "day_of_week": 2
-    },
-    {
-      "start_time": "12:00",
-      "duration": "PT1H",
-      "day_of_week": 3
-    },
-    {
-      "start_time": "12:00",
-      "duration": "PT1H",
-      "day_of_week": 4
-    }
-  ]
-}
-```
-
-### Seasonal Schedule
-
-Different schedules for different dates:
-
-```json
-{
-  "windows": [
-    {
-      "start_time": "08:00",
-      "duration": "PT10H",
-      "date": "2024-06-21"
-    },
-    {
-      "start_time": "09:00",
-      "duration": "PT8H",
-      "date": "2024-12-21"
-    }
-  ]
-}
-```
-
-## Common Patterns
-
-### 1. Always Available
-
-```json
-{
-  "windows": [
-    {
-      "start_time": "00:00",
-      "duration": "PT24H"
-    }
-  ]
-}
-```
-
-### 2. Business Hours
-
-```json
-{
-  "windows": [
-    {
-      "start_time": "09:00",
-      "duration": "PT8H",
-      "day_of_week": 0
-    },
-    {
-      "start_time": "09:00",
-      "duration": "PT8H",
-      "day_of_week": 1
-    },
-    {
-      "start_time": "09:00",
-      "duration": "PT8H",
-      "day_of_week": 2
-    },
-    {
-      "start_time": "09:00",
-      "duration": "PT8H",
-      "day_of_week": 3
-    },
-    {
-      "start_time": "09:00",
-      "duration": "PT8H",
-      "day_of_week": 4
-    }
-  ]
-}
-```
-
-### 3. Never Available
-
-```json
-{
-  "windows": []
-}
-```
-
-## Validation Rules
-
-- `start_time` must be a valid time format
-- `duration` must be a positive duration
-- `day_of_week` must be 0-6 (integer) or valid day name (string)
-- `date` must be a valid ISO date format (YYYY-MM-DD)
-- If `date` is specified, `day_of_week` is ignored
-- `locale` must be a valid locale code when using localized day names
-
-## Tips and Best Practices
-
-1. **Use 24-hour format** for clarity: `"14:30"` instead of `"2:30 PM"`
-2. **Keep durations reasonable** for appliance operation cycles
-3. **Test timezone handling** if using timezone-aware times
-4. **Use specific dates** for holiday schedules
-5. **Consider overlapping windows** for flexibility
-6. **Use localization** for international deployments
-7. **Document your patterns** for maintenance
-
-## Error Handling
-
-Common errors and solutions:
-
-- **Invalid time format**: Use supported time formats listed above
-- **Invalid duration**: Use ISO 8601 duration format (PT1H30M)
-- **Invalid day name**: Check spelling and locale settings
-- **Invalid date**: Use YYYY-MM-DD format
-- **Unknown locale**: Use standard locale codes (en, de, fr, etc.)
-
-## Integration Examples
-
-### Python Usage
-
-```python
-from pydantic import ValidationError
-
-try:
-    config = TimeWindowSequence.model_validate_json(json_string)
-    print(f"Configured {len(config.windows)} time windows")
-except ValidationError as e:
-    print(f"Configuration error: {e}")
-```
-
-### API Configuration
-
-```json
-{
-  "device_id": "dishwasher_01",
-  "time_windows": {
-    "windows": [
-      {
-        "start_time": "22:00",
-        "duration": "PT2H"
-      },
-      {
-        "start_time": "06:00",
-        "duration": "PT2H"
-      }
-    ]
-  }
-}
-```

docs/akkudoktoreos/configuration.md

@@ -1,7 +1,6 @@
 % SPDX-License-Identifier: Apache-2.0
-(configuration-page)=
 
-# Configuration Guideline
+# Configuration
 
 The configuration controls all aspects of EOS: optimization, prediction, measurement, and energy
 management.
@@ -32,10 +31,10 @@ Use endpoint `POST /v1/config/reset` to reset the configuration to the values in
 
 The configuration sources and their priorities are as follows:
 
-1. `Settings`: Provided during runtime by the REST interface
-2. `Environment Variables`: Defined at startup of the REST server and during runtime
-3. `EOS Configuration File`: Read at startup of the REST server and on request
-4. `Default Values`
+1. **Runtime Config Updates**: Provided during runtime by the REST interface
+2. **Environment Variables**: Defined at startup of the REST server and during runtime
+3. **EOS Configuration File**: Read at startup of the REST server and on request
+4. **Default Values**
 
 ### Runtime Config Updates
 

docs/akkudoktoreos/integration.md

@@ -1,5 +1,4 @@
 % SPDX-License-Identifier: Apache-2.0
-(integration-page)=
 
 # Integration
 
@@ -18,23 +17,18 @@ APIs, and online services in creative and practical ways.
 
 Andreas Schmitz uses [Node-RED](https://nodered.org/) as part of his home automation setup.
 
-### Node-Red Resources
+### Resources
 
-- [Installation Guide (German)](https://www.youtube.com/playlist?list=PL8_vk9A-s7zLD865Oou6y3EeQLlNtu-Hn)
-  \— A detailed guide on integrating EOS with `Node-RED`.
+- [Installation Guide (German)](https://meintechblog.de/2024/09/05/andreas-schmitz-joerg-installiert-mein-energieoptimierungssystem/) — A detailed guide on integrating an early version of EOS with
+  `Node-RED`.
 
 ## Home Assistant
 
 [Home Assistant](https://www.home-assistant.io/) is an open-source home automation platform that
 emphasizes local control and user privacy.
 
-(duetting-solution)=
+### Resources
 
-### Home Assistant Resources
-
-- Duetting's [EOS Home Assistant Addon](https://github.com/Duetting/ha_eos_addon).
-
-## EOS Connect
-
-[EOS connect](https://github.com/ohAnd/EOS_connect) uses `EOS` for energy management and optimization,
-and connects to smart home platforms to monitor, forecast, and control energy flows.
+- Duetting's [EOS Home Assistant Addon](https://github.com/Duetting/ha_eos_addon) — Additional
+  details can be found in this
+  [discussion thread](https://github.com/Akkudoktor-EOS/EOS/discussions/294).

docs/akkudoktoreos/introduction.md

@@ -1,180 +0,0 @@
-% SPDX-License-Identifier: Apache-2.0
-
-# Introduction
-
-The Energy System Simulation and Optimization System (EOS) provides a comprehensive
-solution for simulating and optimizing an energy system based on renewable energy
-sources. With a focus on photovoltaic (PV) systems, battery storage (batteries), load
-management (consumer requirements), heat pumps, electric vehicles, and consideration of
-electricity price data, this system enables forecasting and optimization of energy flow
-and costs over a specified period.
-
-After successfully installing a PV system with or without battery storage, most owners
-first priority is often to charge the electric car with surplus energy in order to use
-the electricity generated by the PV system cost-effectively for electromobility.
-
-After initial experiences, the desire to include battery storage and dynamic electricity
-prices in the solution soon arises. The market already offers various commercial and
-non-commercial solutions for this, such as the popular open source hardware and software
-solutions evcc or openWB.
-
-Some solutions take into account the current values ​​of the system such as PV power
-output, battery storage charge level or the current electricity price to decide whether
-to charge the electric car with PV surplus or from the grid (e.g. openWB), some use
-historical consumption values ​​and PV forecast data for their calculations, but leave out
-the current electricity prices and charging the battery storage from the power grid
-(Predbat). Others are specialiced on working in combination with a specific smart home
-solution (e.g. emhass). Still others focus on certain consumers, such as the electric car,
-or are currently working on integrating the forecast values ​​(evcc). And some are commercial
-devices that require an electrician to install them and expect a certain ecosystem
-(e.g. Sunny Home Manager).
-
-The Akkudoktor EOS
-
-- takes into account historical, current and forecast data such as consumption values, PV
-  forecast data, electricity price forecast, battery storage and electric car charge levels
-- the simulation also takes into account the possibility of charging the battery storage
-  from the grid at low electricity prices
-- is not limited to certain consumers, but includes electric cars, heat pumps or more
-  powerful consumers such as tumble dryers
-- is independent of a specific smart home solution and can also be integrated into
-  self-developed solutions if desired
-- is a free and independent open source software solution
-
-![Introdution](../_static/introduction/introduction.png)
-
-The challenge is to charge (electric car) or start the consumers (washing machine, dryer)
-at the right time and to do so as cost-efficiently as possible. If PV yield forecast,
-battery storage and dynamic electricity price forecasts are included in the calculation,
-the possibilities increase, but unfortunately so does the complexity.
-
-The Akkudoktor EOS addresses this challenge by simulating energy flows in the household
-based on target values, forecast data and current operating data over a 48-hour
-observation period, running through a large number of different scenarios and finally
-providing a cost-optimized plan for the current day controlling the relevant consumers.
-
-## Prerequisites
-
-- Technical requirements
-- Input data
-
-### Technical requirements
-
-- reasonably fast computer on which EOS is installed
-- controllable energy system consisting of photovoltaic system, solar battery storage,
-  energy intensive consumers that must provide the appropriate interfaces
-- integration solution for integrating the energy system and EOS
-
-### Input Data
-
-![Overview](../_static/introduction/overview.png)
-
-The EOS requires various types of data for the simulation:
-
-Forecast data
-
-- PV yield forecast
-- Expected household consumption
-- Electricity price forecast
-- Forecast temperature trend (if heatpump is used)
-
-Basic data and current operating data
-
-- Current charge level of the battery storage
-- Value of electricity in the battery storage
-- Current charge level of the electric car
-- Energy consumption and running time of dishwasher, washing machine and tumble dryer
-
-Target values
-
-- Charge level the electric car should reach in the next few hours
-- Consumers to run in the next few hours
-
-There are various service providers available for PV forecasting that calculate forecast
-data for a PV system based on the various influencing factors, such as system size,
-orientation, location, time of year and weather conditions. EOS also offers a
-[PV forecasting service](#prediction-page) which can be used. This service uses
-public data in the background.
-
-For the forecast of household consumption EOS provides a standard load curve for an
-average day based on annual household consumption that you can fetch via API. This data
-was compiled based on data from several households and provides an initial usable basis.
-Alternatively your own collected historical data could be used to reflect your personal
-consumption behaviour.
-
-## Simulation Results
-
-Based on the input data, the EOS uses a genetic algorithm to create a cost-optimized
-schedule for the coming hours from numerous simulations of the overall system.
-
-The plan created contains for each of the coming hours
-
-- Control information
-  - whether and with what power the battery storage should be charged from the grid
-  - when the battery storage should be charged via the PV system
-  - whether discharging the battery storage is permitted or not
-  - when and with what power the electric car should be charged
-  - when a household appliance should be activated
-- Energy history information
-  - Total load of the house
-  - Grid consumption
-  - Feed-in
-  - Load of the planned household appliances
-  - Charge level of the battery storage
-  - Charge level of the electric car
-  - Active losses
-- Cost information
-  - Revenue per hour (when fed into the grid)
-  - Total costs per hour (when drawn from the grid)
-  - Overall balance (revenue-costs)
-  - Cost development
-
-If required, the simulation result can also be created and downloaded in graphical
-form as a PDF from EOS.
-
-## Integration
-
-The Akkudoktor EOS can be integrated into a wide variety of systems with a variety
-of components.
-
-![Integration](../_static/introduction/integration.png)
-
-However, the components are not integrated by the EOS itself, but must be integrated by
-the user using an integration solution and currently requires some effort and technical
-know-how.
-
-Any [integration](#integration-page) solution that can act as an intermediary between the
-components and the REST API of EOS can be used. One possible solution that enables the
-integration of components and EOS is Node-RED. Another solution could be Home Assistant
-usings its built in features.
-
-Access to the data and functions of the components can be done in a variety of ways.
-Node-RED offers a large number of types of nodes that allow access via the protocols
-commonly used in this area, such as Modbus or MQTT. Access to any existing databases,
-such as InfluxDB or PostgreSQL, is also possible via nodes provided by Node-RED.
-
-It becomes easier if a smart home solution like Home Assistant, openHAB or ioBroker or
-solutions such as evcc or openWB are already in use. In this case, these smart home
-solutions already take over the technical integration and communication with the components
-at a technical level and Node-RED offers nodes for accessing these solutions, so that the
-corresponding sources can be easily integrated into a flow.
-
-In Home Assistant you could use an automation to prepare the input payload for EOS and
-then use the RESTful integration to call EOS. Based on this concept there is already a
-Home Assistant add-on created by [Duetting](#duetting-solution).
-
-The plan created by EOS must also be executed via the chosen integration solution,
-with the respective devices receiving their instructions according to the plan.
-
-## Limitations
-
-The plan calculated by EOS is cost-optimized due to the genetic algorithm used, but not
-necessarily cost-optimal, since genetic algorithms do not always find the global optimum,
-but usually find good local optima very quickly in a large solution space.
-
-## Links
-
-- [German Videos explaining the basic concept and installation process of EOS (YouTube)](https://www.youtube.com/playlist?list=PL8_vk9A-s7zLD865Oou6y3EeQLlNtu-Hn)
-- [German Forum of Akkudoktor EOS](https://akkudoktor.net/c/der-akkudoktor/eos)
-- [Akkudoktor-EOS GitHub Repository](https://github.com/Akkudoktor-EOS/EOS)
-- [Latest EOS Documentation](https://akkudoktor-eos.readthedocs.io/en/latest/)

docs/akkudoktoreos/logging.md

@@ -1,81 +0,0 @@
-% SPDX-License-Identifier: Apache-2.0
-(logging-page)=
-
-# Logging
-
-EOS automatically records important events and messages to help you understand what’s happening and
-to troubleshoot problems.
-
-## How Logging Works
-
-- By default, logs are shown in your terminal (console).
-- You can also save logs to a file for later review.
-- Log files are rotated automatically to avoid becoming too large.
-
-## Controlling Log Details
-
-### 1. Command-Line Option
-
-Set the amount of log detail shown on the console by using `--log-level` when starting EOS.
-
-Example:
-
-```{eval-rst}
-.. tabs::
-
-  .. tab:: Windows
-
-     .. code-block:: powershell
-
-        .venv\Scripts\python src/akkudoktoreos/server/eos.py --log-level DEBUG
-
-  .. tab:: Linux
-
-     .. code-block:: bash
-
-        .venv/bin/python src/akkudoktoreos/server/eos.py --log-level DEBUG
-
-```
-
-Common levels:
-
-- DEBUG (most detail)
-- INFO (default)
-- WARNING
-- ERROR
-- CRITICAL (least detail)
-
-### 2. Configuration File
-
-You can also set logging options in your EOS configuration file (EOS.config.json).
-
-```Json
-{
-  "logging": {
-    "console_level": "INFO",
-    "file_level": "DEBUG"
-  }
-}
-```
-
-### 3. Environment Variable
-
-You can also control the log level by setting the `EOS_LOGGING__CONSOLE_LEVEL` and the
-`EOS_LOGGING__FILE_LEVEL` environment variables.
-
-```bash
-  EOS_LOGGING__CONSOLE_LEVEL="INFO"
-  EOS_LOGGING__FILE_LEVEL="DEBUG"
-```
-
-## File Logging
-
-If the `file_level` configuration is set, log records are written to a rotating log file. The log
-file is in the data output directory and named `eos.log`. You may directly read the file or use
-the `/v1/logging/log` endpoint to access the file log.
-
-:::{admonition} Note
-:class: note
-The `/v1/logging/log` endpoint needs file logging to be enabled. Otherwise old or no logging
-information is provided.
-:::

docs/akkudoktoreos/measurement.md

@@ -1,19 +1,25 @@
 % SPDX-License-Identifier: Apache-2.0
-(measurement-page)=
 
 # Measurements
 
 Measurements are utilized to refine predictions using real data from your system, thereby enhancing
 accuracy.
 
-- Household Load Measurement
-- Grid Export Measurement
-- Grid Import Measurement
+- **Household Load Measurement**
+- **Grid Export Measurement**
+- **Grid Import Measurement**
 
 ## Storing Measurements
 
 EOS stores measurements in a **key-value store**, where the term `measurement key` refers to the
-unique identifier used to store and retrieve specific measurement data.
+unique identifier used to store and retrieve specific measurement data. Note that the key-value
+store is memory-based, meaning that all stored data will be lost upon restarting the EOS REST
+server.
+
+:::{admonition} Todo
+:class: note
+Ensure that measurement data persists across server restarts.
+:::
 
 Several endpoints of the EOS REST server allow for the management and retrieval of these
 measurements.
@@ -24,14 +30,14 @@ The measurement data must be or is provided in one of the following formats:
 
 A dictionary with the following structure:
 
-```json
-{
-    "start_datetime": "2024-01-01 00:00:00",
-    "interval": "1 hour",
-    "<measurement key>": [value, value, ...],
-    "<measurement key>": [value, value, ...],
-    ...
-}
+```python
+    {
+        "start_datetime": "2024-01-01 00:00:00",
+        "interval": "1 Hour",
+        "<measurement key>": [value, value, ...],
+        "<measurement key>": [value, value, ...],
+        ...
+    }
 ```
 
 ### 2. DateTimeDataFrame
@@ -45,84 +51,43 @@ The column name of the data must be the same as the names of the `measurement ke
 A JSON string created from a [pandas](https://pandas.pydata.org/docs/index.html) series with a
 `DatetimeIndex`. Use [pandas.Series.to_json(orient="index")](https://pandas.pydata.org/docs/reference/api/pandas.Series.to_json.html#pandas.Series.to_json).
 
-Creates a dictionary like this:
-
-```json
-{
-  "data": {
-     "2024-01-01T00:00:00+01:00": 1,
-     "2024-01-02T00:00:00+01:00": 2,
-     "2024-01-03T00:00:00+01:00": 3,
-     ...
-  },
-  "dtype": "float64",
-  "tz": "Europe/Berlin"
-}
-```
-
 ## Load Measurement
 
-The EOS measurement store provides for storing energy meter readings of loads.
+The EOS measurement store provides for storing meter readings of loads. There are currently five loads
+foreseen. The associated `measurement key`s are:
 
-The associated `measurement key`s can be configured by:
+- `load0_mr`: Load0 meter reading [kWh]
+- `load1_mr`: Load1 meter reading [kWh]
+- `load2_mr`: Load2 meter reading [kWh]
+- `load3_mr`: Load3 meter reading [kWh]
+- `load4_mr`: Load4 meter reading [kWh]
 
-```json
-{
-  "measurement": {
-    "load_emr_keys": ["load0_emr", "my special load", ...]
-  }
-}
-```
+For ease of use, you can assign descriptive names to the `measurement key`s to represent your
+system's load sources. Use the following `configuration options` to set these names
+(e.g., 'Dish Washer', 'Heat Pump'):
+
+- `load0_name`: Name of the load0 source
+- `load1_name`: Name of the load1 source
+- `load2_name`: Name of the load2 source
+- `load3_name`: Name of the load3 source
+- `load4_name`: Name of the load4 source
 
 Load measurements can be stored for any datetime. The values between different meter readings are
-linearly approximated. Storing values between optimization intervals is generally not useful.
+linearly approximated. Since optimization occurs on the hour, storing values between hours is
+generally not useful.
 
 The EOS measurement store automatically sums all given loads to create a total load value series
 for specified intervals, usually one hour. This aggregated data can be used for load predictions.
 
-:::{admonition} Warning
-:class: warning
-Only use **actual meter readings** in **kWh**, not energy consumption.
-Example: `112345.77`, `112389.23`, `112412.55`, …
-:::
-
 ## Grid Export/ Import Measurement
 
 The EOS measurement store also allows for the storage of meter readings for grid import and export.
+The associated `measurement key`s are:
 
-The associated `measurement key`s can be configured by:
-
-```json
-{
-  "measurement": {
-    "grid_export_emr_keys": ["grid_export_emr", ...],
-    "grid_import_emr_keys": ["grid_import_emr", ...],
-  }
-}
-```
+- `grid_export_mr`: Export to grid meter reading [kWh]
+- `grid_import_mr`: Import from grid meter reading [kWh]
 
 :::{admonition} Todo
 :class: note
 Currently not used. Integrate grid meter readings into the respective predictions.
 :::
-
-## Battery/ Electric Vehicle State of Charge (SoC) Measurement
-
-The state of charge (SoC) measurement of batteries and electric vehicle batteries can be stored.
-
-The associated `measurement key` is pre-defined by the device configuration. It can be
-determined from the device configuration by the read-only `measurement_key_soc_factor` configuration
-option.
-
-## Battery/ Electric Vehicle Power Measurement
-
-The charge/ discharge power measurements of batteries and electric vehicle batteries can be stored.
-Charging power is denoted by a negative value, discharging power by a positive value.
-
-The associated `measurement key`s are pre-defined by the device configuration. They can be
-determined from the device configuration by read-only configuration options:
-
-- `measurement_key_power_l1_w`
-- `measurement_key_power_l2_w`
-- `measurement_key_power_l3_w`
-- `measurement_key_power_3_phase_sym_w`

docs/akkudoktoreos/optimauto.md

@@ -1,448 +0,0 @@
-% SPDX-License-Identifier: Apache-2.0
-
-# Automatic Optimization
-
-## Introduction
-
-EOS offers two approaches to optimize your energy management system: `post /optimize optimization` and
-`automatic optimization`.
-
-The `post /optimize optimization` interface, based on a **POST** request to `/optimize`, is widely
-used. It was originally developed by Andreas at the start of the project and is still demonstrated
-in his instructional videos. This interface allows users or external systems to trigger an
-optimization manually, supplying custom parameters and timing.
-
-As an alternative, EOS supports `automatic optimization`, which runs automatically at configured
-intervals. It retrieves all required input data — including electricity prices, battery storage
-capacity, PV production forecasts, and temperature data — based on your system configuration.
-
-### Genetic Algorithm
-
-Both optimization modes use the same core optimization engine.
-
-EOS uses a [genetic algorithm](https://en.wikipedia.org/wiki/Genetic_algorithm) to find an optimal
-control strategy for home energy devices such as household loads, batteries, and electric vehicles.
-
-In this context, each **individual** represents a possible solution — a specific control schedule
-that defines how devices should operate over time. These individuals are evaluated using
-[resource simulations](#resource-page), which model the system’s energy behavior over a defined time
-period divided into fixed intervals.
-
-The quality of each solution (its *fitness*) is determined by how well it performs during
-simulation, based on objectives such as minimizing electricity costs, maximizing self-consumption,
-or meeting battery charge targets.
-
-Through an iterative process of selection, crossover, and mutation, the algorithm gradually evolves
-more effective solutions. The final result is an optimized control strategy that balances multiple
-system goals within the constraints of the input data and configuration.
-
-:::{note}
-You don’t need to understand the internal workings of the genetic algorithm to benefit from
-automatic optimization. EOS handles everything behind the scenes based on your configuration.
-However, advanced users can fine-tune the optimization behavior using additional settings like
-population size, penalties, and random seed.
-:::
-
-## Energy Management Plan
-
-Whenever the optimization is run, the energy management plan is updated. The energy management plan
-provides a list of energy management instructions in chronological order. The instructions lean on
-to the [S2 standard](https://docs.s2standard.org/) to have maximum flexibility and stay completely
-independent from any manufacturer.
-
-### Battery Instructions
-
-The battery control instructions assume an idealized battery model. Under this model, the battery
-can be operated in four discrete operation modes:
-
-| **Operation Mode ID** | **Description**                                                                      |
-| --------------------- | ------------------------------------------------------------------------------------ |
-| **IDLE**              | Battery neither charges nor discharges; holds its state of charge.                   |
-| **CHARGE**            | Charge at a specified power rate up to the allowable maximum.                        |
-| **DISCHARGE**         | Discharge at a specified power rate up to the allowable maximum.                     |
-| **ALLOW_DISCHARGE**   | Allow the battery to freely discharge depending on its instantaneous power setpoint. |
-
-The **operation mode factor** (0.0–1.0) specifies the normalized power rate relative to the
-battery's nominal maximum charge or discharge power. A value of 1.0 corresponds to full-rate
-charging or discharging, while 0.0 indicates no power transfer. Intermediate values scale the power
-proportionally.
-
-### Electric Vehicle Instructions
-
-The electric vehicle control instructions assume an idealized EV battery model. Under this model,
-the EV battery can be operated in two operation modes:
-
-| **Operation Mode ID** | **Description**                                                                      |
-| --------------------- | ------------------------------------------------------------------------------------ |
-| **IDLE**              | Battery neither charges nor discharges; holds its state of charge.                   |
-| **CHARGE**            | Charge at a specified power rate up to the allowable maximum.                        |
-
-The **operation mode factor** (0.0–1.0) specifies the normalized power rate relative to the
-battery's nominal maximum charge power. A value of 1.0 corresponds to full-rate charging, while 0.0
-indicates no power transfer. Intermediate values scale the power proportionally.
-
-### Home Appliance Instructions
-
-The home appliance instructions assume an idealized home appliance model. Under this model,
-the home appliance can be operated in two operation modes:
-
-| **Operation Mode ID** | **Description**                                                                      |
-| --------------------- | ------------------------------------------------------------------------------------ |
-| **RUN**               | The home appliance is started and runs until the end of it's power sequence.         |
-| **IDLE**              | The home appliance does not run.                                                     |
-
-The **operation mode factor** (0.0–1.0) is ignored.
-
-## Configuration
-
-### Energy management configuration
-
-The energy management is run on configured intervals with some startup delay after server start.
-Both values are given in seconds.
-
-:::{admonition} Note
-:class: note
-If no interval is configured (`None`, `null`) there will be only one energy management run at
-startup.
-:::
-
-The energy management can be run in two modes:
-
-- **OPTIMIZATION**: A full optimization is done. This includes update of predictions.
-- **PREDICTION**: Only the predictions are updated.
-
-**Example:**
-
-```json
-{
-    "ems": {
-        "startup_delay": 5.0,
-        "interval": 300.0,
-        "mode": "OPTIMIZATION"
-    }
-}
-```
-
-### Optimization Configuration
-
-#### Optimization Time Configuration
-
-- **horizon_hours**:
-    The optimization horizon parameter defines the default time window — in hours — within which
-    the energy optimization goal shall be achieved.
-
-    Specific devices, like the home appliance, have their own configuration for time windows. If
-    the time windows are not configured the simulation uses the default time window.
-
-    Each device simulation run must ensure that all tasks or appliance cycles (e.g., running a
-    dishwasher) are completed within the configured time windows.
-
-- **interval**: Defines the time step in seconds between control actions
-    (e.g. `3600` for one hour, `900` for 15 minutes).
-
-:::{warning}
-**Current Limitation**
-
-At present, the `interval` setting is **not used** by the genetic algorithm. Instead:
-
-- The control interval is fixed to **1 hour**.
-
-Support for configurable intervals (e.g. 15-minute steps) may be added in a future release.
-:::
-
-#### Genetic Algorithm Parameters
-
-The behavior of the genetic algorithm can be customized using the following configuration options:
-
-- **individuals** (`int`, default: `300`):
-  Sets the number of individuals (candidate solutions) in the (first) generation. A higher number
-  increases solution diversity and the chance of finding a good result, but also increases
-  computation time.
-
-- **generations** (`int`, default: `400`):
-  Sets the number of generations to evaluate the optimal solution. In each generation, solutions are
-  evaluated and evolved. More generations can improve optimization quality but increase computation
-  time. Best results are usually found within a moderate number of generations.
-
-- **seed** (`int` or `null`, default: `null`):
-  Sets the random seed for reproducible results.
-
-  - If `null`, a random seed is used (non-reproducible).
-  - If an integer is provided, it ensures that the same optimization input yields the same output.
-
-    A fixed seed to ensure reproducibility. Runs with the same seed and configuration will
-    produce the same results.
-
-- **penalties** (`dict`):
-  Defines how penalties are applied to solutions that violate constraints (e.g., undercharged
-  batteries). Penalty function parameter values influence the fitness score, discouraging
-  undesirable solutions.
-
-:::{note}
-**Supported Penalty Functions**
-
-Currently, the only supported penalty function parameter is:
-
-- `ev_soc_miss`:
-  Applies a penalty when the **state of charge (SOC)** of the electric vehicle battery falls below
-  the required minimum. This encourages the optimizer to ensure sufficient EV charging.
-:::
-
-#### Value Formats
-
-- **Time-related values**:
-  - `hours`: specified in **hours** (e.g. `24`)
-  - `interval`: specified in **seconds** (e.g. `3600`)
-
-- **Genetic algorithm parameters**:
-  - `individuals`: must be an **integer**
-  - `seed`: must be an **integer** or `null` for random behavior
-
-- **Penalty function parameter values**: may be `float`, `int`, or `string`, depending on the type
-  of penalty function.
-
-#### Optimization configuration example
-
-```json
-{
-    "optimization": {
-        "hours": 24,
-        "interval": 3600,
-        "genetic" : {
-            "individuals": 300,
-            "generations": 400,
-            "seed": null,
-            "penalties": {
-                "ev_soc_miss": 10
-            }
-        }
-    }
-}
-```
-
-### Device simulation configuration
-
-The device simulations are used to evaluate the fitness of the individuals of the solution
-population.
-
-The GENETIC algorithm supports 4 devices:
-
-- **inverter**: A photovoltaic power inverter that can export to the grid and charge a battery.
-  The inverter is mandatory.
-- **electric_vehicle**: An electric vehicle, basically the battery of an electric vehicle. The
-  The electrical vehicle is optional.
-- **battery**: A battery that can be charged by the inverter. The battery is mandatory.
-- **home_appliance**: A home appliance, like a washing machine or a dish washer. The home
-  appliance is optional.
-
-:::{admonition} Warning
-:class: warning
-The GENETIC algorithm can only use the first inverter, electrical vehicle, battery, home appliance
-that is configured, even if more devices are configured.
-:::
-
-#### Inverter simulation configuration
-
-**Example:**
-
-```json
-{
-    "devices": {
-        "max_inverters": 1,
-        "inverters": [
-            {
-                "device_id": "inv1",
-                "max_power_w": 10000,
-                "battery_id": "bat1"
-            }
-        ]
-    }
-}
-```
-
-#### Electric vehicle simulation configuration
-
-**Example:**
-
-```json
-{
-    "devices": {
-        "max_electric_vehicles": 1,
-        "electric_vehicles": [
-            {
-                "device_id": "ev1",
-                "capacity_wh": 50000,
-                "max_charge_power_w": 10000,
-                "charge_rates": [0.0, 0.25, 0.5, 0.75, 1.0],
-                "min_soc_percentage": 10,
-                "max_soc_percentage": 80
-            }
-        ]
-    },
-    "measurement": {
-        "electric_vehicle_soc_keys": ["ev1_soc"]
-    }
-}
-```
-
-#### Battery simulation configuration
-
-**Example:**
-
-```json
-{
-    "devices": {
-        "max_batteries": 1,
-        "batteries": [
-            {
-                "device_id": "battery1",
-                "capacity_wh": 8000,
-                "charging_efficiency": 0.88,
-                "discharging_efficiency": 0.88,
-                "levelized_cost_of_storage_kwh": 0.12,
-                "max_charge_power_w": 8000,
-                "min_charge_power_w": 50,
-                "charge_rates": null,
-                "min_soc_percentage": 5,
-                "max_soc_percentage": 95
-            }
-        ]
-    }
-}
-```
-
-#### Home appliance simulation configuration
-
-**Example:**
-
-```json
-{
-    "devices": {
-        "max_home_appliances": 1,
-        "home_appliances": [
-            {
-                "device_id": "washing machine",
-                "consumption_wh": 600,
-                "duration_h": 3,
-                "time_windows": null,
-            }
-        ]
-    }
-}
-```
-
-The time windows the home appliance may run can be [configured](#configtimewindow-page) in several
-ways. See the [time window configuration](#configtimewindow-page) for details.
-
-## Predictions configuration
-
-The device simulation may rely on predictions to simulate proper behaviour. E.g. the inverter needs
-to know the PV forecast.
-
-Configure the [predictions](#prediction-page) as described on the [prediction page](#prediction-page).
-
-### Providing your own prediction data
-
-If EOS does not have a suitable prediction provider you can provide your own data for a prediction.
-Configure the respective import provider (ElecPriceImport, LoadImport, PVForecastImport,
-WeatherImport) and use one of the following endpoints to provide your own data:
-
-- **PUT** `/v1/prediction/import/ElecPriceImport`
-- **PUT** `/v1/prediction/import/LoadImport`
-- **PUT** `/v1/prediction/import/PVForecastImport`
-- **PUT** `/v1/prediction/import/WeatherImport`
-
-## Measurement configuration
-
-Predictions and device simulations often rely on **measurement data** to produce accurate results.
-For example:
-
-- A **load forecast** requires past energy meter readings.
-- A **battery simulation** needs the current **state of charge (SoC)** to start from the correct
-  condition.
-
-Before using these features, make sure to configure the [measurement](#measurement-page) as
-described on the [measurement page](#measurement-page).
-
-### Providing your own measurement data
-
-You can provide your own measurement data to the prediction and simulation engine through the
-following REST endpoints (see the [measurement page](#measurement-page) for details on the data
-format):
-
-- **PUT** `/v1/measurement/data`
-- **PUT** `/v1/measurement/dataframe`
-- **PUT** `/v1/measurement/series`
-- **PUT** `/v1/measurement/value`
-
-### Example: Supplying Battery and EV SoC
-
-For **batteries** and **electric vehicles**, it is strongly recommended to provide
-**current SoC**. This ensures that simulations start with the correct state.
-
-The simplest way is to use the `/v1/measurement/value` endpoint.
-Assuming the battery is named `battery1` and the EV is named `ev11`:
-
-1. **Use the measurement keys** that are pre-configured for your **devices**. For example:
-
-    ```json
-    {
-        "devices": {
-            "batteries": [
-                {
-                "device_id": "battery1", "capacity_wh": 8000,  ...
-                "measurement_key_soc_factor": "battery1-soc-factor", ...
-                }
-            ],
-            "electric_vehicles": [
-                {
-                "device_id": "ev11", "capacity_wh": 8000,  ...
-                "measurement_key_soc_factor": "ev11-soc-factor", ...
-                }
-            ]
-        }
-    }
-    ```
-
-2. **Record your SoC readings** to these keys.
-
-   - Enter the values as **factor of total capacity** of the respective **battery**.
-
-In these examples:
-
-- datetime specifies the timestamp of the measurement.
-- key is the measurement key (e.g. battery1-soc-factor).
-- value is the numeric measurement value (e.g. SoC as factor of total capacity).
-
-#### Raw HTTP request
-
-```http
-PUT http://127.0.0.1:8503/v1/measurement/value?datetime=2025-09-26T16%3A39&key=battery1-soc-factor&value=0.57
-PUT http://127.0.0.1:8503/v1/measurement/value?datetime=2025-09-26T16%3A39&key=ev11-soc-factor&value=0.22
-```
-
-#### Equivalent curl commands
-
-```bash
-curl -X PUT "http://127.0.0.1:8503/v1/measurement/value?datetime=2025-09-26T16%3A39&key=battery1-soc-factor&value=0.57"
-curl -X PUT "http://127.0.0.1:8503/v1/measurement/value?datetime=2025-09-26T16%3A39&key=ev11-soc-factor&value=0.22"
-```
-
-### Example: Supplying Load Data
-
-To provide your actual load measurements in Akkudoktor-EOS:
-
-1. **Configure the measurement keys** for your load energy meters. For example:
-
-    ```json
-    {
-        "measurements": {
-            "load_emr_keys": ["my_load_meter_reading", "my_other_load_meter_reading"]
-        }
-    }
-    ```
-
-2. **Record your meter readings** to these keys.
-
-   - Enter the values exactly as your energy meters report them, in **kWh**.
-   - Use the same approach as when supplying battery or EV SoC data.

docs/akkudoktoreos/optimization.md

@@ -0,0 +1,8 @@
+% SPDX-License-Identifier: Apache-2.0
+
+# Optimization
+
+:::{admonition} Todo
+:class: note
+Describe optimization.
+:::

docs/akkudoktoreos/optimpost.md

@@ -1,253 +0,0 @@
-% SPDX-License-Identifier: Apache-2.0
-
-# `POST /optimize` Optimization
-
-## Introduction
-
-The `POST /optimize` API endpoint optimizes your energy management system based on various inputs
-including electricity prices, battery storage capacity, PV forecast, and temperature data.
-
-The `POST /optimize` optimization interface is the "classical" interface developed by Andreas at the
-start of the projects and used and described in his videos. It allows and requires to define all the
-optimization paramters on the endpoint request.
-
-:::{admonition} Warning
-:class: warning
-The `POST /optimize` endpoint interface does not regard configurations set for the parameters
-passed to the request. You have to set the parameters even if given in the configuration.
-:::
-
-## Input Payload
-
-### Sample Request
-
-```json
-{
-    "ems": {
-        "preis_euro_pro_wh_akku": 0.0001,
-        "einspeiseverguetung_euro_pro_wh": [
-          0.00007, 0.00007, 0.00007, 0.00007, 0.00007, 0.00007, 0.00007,
-          0.00007, 0.00007, 0.00007, 0.00007, 0.00007, 0.00007, 0.00007,
-          0.00007, 0.00007, 0.00007, 0.00007, 0.00007, 0.00007, 0.00007,
-          0.00007, 0.00007, 0.00007, 0.00007, 0.00007, 0.00007, 0.00007,
-          0.00007, 0.00007, 0.00007, 0.00007, 0.00007, 0.00007, 0.00007,
-          0.00007, 0.00007, 0.00007, 0.00007, 0.00007, 0.00007, 0.00007,
-          0.00007, 0.00007, 0.00007, 0.00007, 0.00007, 0.00007
-        ],
-        "gesamtlast": [
-          676.71, 876.19, 527.13, 468.88, 531.38, 517.95, 483.15, 472.28,
-          1011.68, 995.00, 1053.07, 1063.91, 1320.56, 1132.03, 1163.67,
-          1176.82, 1216.22, 1103.78, 1129.12, 1178.71, 1050.98, 988.56, 912.38,
-          704.61, 516.37, 868.05, 694.34, 608.79, 556.31, 488.89, 506.91,
-          804.89, 1141.98, 1056.97, 992.46, 1155.99, 827.01, 1257.98, 1232.67,
-          871.26, 860.88, 1158.03, 1222.72, 1221.04, 949.99, 987.01, 733.99,
-          592.97
-        ],
-        "pv_prognose_wh": [
-          0, 0, 0, 0, 0, 0, 0, 8.05, 352.91, 728.51, 930.28, 1043.25, 1106.74,
-          1161.69, 6018.82, 5519.07, 3969.88, 3017.96, 1943.07, 1007.17,
-          319.67, 7.88, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5.04, 335.59, 705.32,
-          1121.12, 1604.79, 2157.38, 1433.25, 5718.49, 4553.96, 3027.55,
-          2574.46, 1720.4, 963.4, 383.3, 0, 0, 0
-        ],
-        "strompreis_euro_pro_wh": [
-          0.0003384, 0.0003318, 0.0003284, 0.0003283, 0.0003289, 0.0003334,
-          0.0003290, 0.0003302, 0.0003042, 0.0002430, 0.0002280, 0.0002212,
-          0.0002093, 0.0001879, 0.0001838, 0.0002004, 0.0002198, 0.0002270,
-          0.0002997, 0.0003195, 0.0003081, 0.0002969, 0.0002921, 0.0002780,
-          0.0003384, 0.0003318, 0.0003284, 0.0003283, 0.0003289, 0.0003334,
-          0.0003290, 0.0003302, 0.0003042, 0.0002430, 0.0002280, 0.0002212,
-          0.0002093, 0.0001879, 0.0001838, 0.0002004, 0.0002198, 0.0002270,
-          0.0002997, 0.0003195, 0.0003081, 0.0002969, 0.0002921, 0.0002780
-        ]
-    },
-    "pv_akku": {
-        "device_id": "battery1",
-        "capacity_wh": 26400,
-        "max_charge_power_w": 5000,
-        "initial_soc_percentage": 80,
-        "min_soc_percentage": 15
-    },
-    "inverter": {
-        "device_id": "inverter1",
-        "max_power_wh": 10000,
-        "battery_id": "battery1"
-    },
-    "eauto": {
-        "device_id": "ev1",
-        "capacity_wh": 60000,
-        "charging_efficiency": 0.95,
-        "charge_rates": [0.0, 0.375, 0.5, 0.625, 0.75, 0.875, 1.0],
-        "discharging_efficiency": 1.0,
-        "max_charge_power_w": 11040,
-        "initial_soc_percentage": 54,
-        "min_soc_percentage": 0
-    },
-    "temperature_forecast": [
-      18.3, 17.8, 16.9, 16.2, 15.6, 15.1, 14.6, 14.2, 14.3, 14.8, 15.7, 16.7, 17.4,
-      18.0, 18.6, 19.2, 19.1, 18.7, 18.5, 17.7, 16.2, 14.6, 13.6, 13.0, 12.6, 12.2,
-      11.7, 11.6, 11.3, 11.0, 10.7, 10.2, 11.4, 14.4, 16.4, 18.3, 19.5, 20.7, 21.9,
-      22.7, 23.1, 23.1, 22.8, 21.8, 20.2, 19.1, 18.0, 17.4
-    ],
-    "start_solution": null
-}
-```
-
-## Input Parameters
-
-### Energy Management System (EMS)
-
-#### Battery Cost (`preis_euro_pro_wh_akku`)
-
-- Unit: €/Wh
-- Purpose: Represents the residual value of energy stored in the battery
-- Impact: Lower values encourage battery depletion, higher values preserve charge at the end of the
-  simulation.
-
-#### Feed-in Tariff (`einspeiseverguetung_euro_pro_wh`)
-
-- Unit: €/Wh
-- Purpose: Compensation received for feeding excess energy back to the grid
-
-#### Total Load Forecast (`gesamtlast`)
-
-- Unit: W
-- Time Range: 48 hours (00:00 today to 23:00 tomorrow)
-- Format: Array of hourly values
-- Note: Exclude optimizable loads (EV charging, battery charging, etc.)
-
-##### Data Sources
-
-1. Standard Load Profile: `GET /v1/prediction/list?key=load_mean` for a standard load profile based
-   on your yearly consumption.
-2. Adjusted Load Profile: `GET /v1/prediction/list?key=load_mean_adjusted` for a combination of a
-   standard load profile based on your yearly consumption incl. data from last 48h.
-
-#### PV Generation Forecast (`pv_prognose_wh`)
-
-- Unit: W
-- Time Range: 48 hours (00:00 today to 23:00 tomorrow)
-- Format: Array of hourly values
-- Data Source: `GET /v1/prediction/series?key=pvforecast_ac_power`
-
-#### Electricity Price Forecast (`strompreis_euro_pro_wh`)
-
-- Unit: €/Wh
-- Time Range: 48 hours (00:00 today to 23:00 tomorrow)
-- Format: Array of hourly values
-- Data Source: `GET /v1/prediction/list?key=elecprice_marketprice_wh`
-
-Verify prices against your local tariffs.
-
-### Battery Storage System
-
-#### Configuration
-
-- `device_id`: ID of battery
-- `capacity_wh`: Total battery capacity in Wh
-- `charging_efficiency`: Charging efficiency (0-1)
-- `discharging_efficiency`: Discharging efficiency (0-1)
-- `max_charge_power_w`: Maximum charging power in W
-
-#### State of Charge (SoC)
-
-- `initial_soc_percentage`: Current battery level (%)
-- `min_soc_percentage`: Minimum allowed SoC (%)
-- `max_soc_percentage`: Maximum allowed SoC (%)
-
-### Inverter
-
-- `device_id`: ID of inverter
-- `max_power_wh`: Maximum inverter power in Wh
-- `battery_id`: ID of battery
-
-### Electric Vehicle (EV)
-
-- `device_id`: ID of electric vehicle
-- `capacity_wh`: Battery capacity in Wh
-- `charging_efficiency`: Charging efficiency (0-1)
-- `discharging_efficiency`: Discharging efficiency (0-1)
-- `max_charge_power_w`: Maximum charging power in W
-- `initial_soc_percentage`: Current charge level (%)
-- `min_soc_percentage`: Minimum allowed SoC (%)
-- `max_soc_percentage`: Maximum allowed SoC (%)
-
-### Temperature Forecast
-
-- Unit: °C
-- Time Range: 48 hours (00:00 today to 23:00 tomorrow)
-- Format: Array of hourly values
-- Data Source: `GET /v1/prediction/list?key=weather_temp_air`
-
-## Output Format
-
-### Sample Response
-
-```json
-{
-    "ac_charge": [0.625, 0, ..., 0.75, 0],
-    "dc_charge": [1, 1, ..., 1, 1],
-    "discharge_allowed": [0, 0, 1, ..., 0, 0],
-    "eautocharge_hours_float": [0.625, 0, ..., 0.75, 0],
-    "result": {
-        "Last_Wh_pro_Stunde": [...],
-        "EAuto_SoC_pro_Stunde": [...],
-        "Einnahmen_Euro_pro_Stunde": [...],
-        "Gesamt_Verluste": 1514.96,
-        "Gesamtbilanz_Euro": 2.51,
-        "Gesamteinnahmen_Euro": 2.88,
-        "Gesamtkosten_Euro": 5.39,
-        "akku_soc_pro_stunde": [...]
-    }
-}
-```
-
-### Output Parameters
-
-#### Battery Control
-
-- `ac_charge`: Grid charging schedule (0.0-1.0)
-- `dc_charge`: DC charging schedule (0-1)
-- `discharge_allowed`: Discharge permission (0 or 1)
-
-0 (no charge)
-1 (charge with full load)
-
-`ac_charge` multiplied by the maximum charge power of the battery results in the planned charging
-power.
-
-#### EV Charging
-
-- `eautocharge_hours_float`: EV charging schedule (0.0-1.0)
-
-#### Results
-
-The `result` object contains detailed information about the optimization outcome. The length of the
-array is between 25 and 48 and starts at the current hour and ends at 23:00 tomorrow.
-
-- `Last_Wh_pro_Stunde`: Array of hourly load values in Wh
-  - Shows the total energy consumption per hour
-  - Includes household load, battery charging/discharging, and EV charging
-
-- `EAuto_SoC_pro_Stunde`: Array of hourly EV state of charge values (%)
-  - Shows the projected EV battery level throughout the optimization period
-
-- `Einnahmen_Euro_pro_Stunde`: Array of hourly revenue values in Euro
-
-- `Gesamt_Verluste`: Total energy losses in Wh
-
-- `Gesamtbilanz_Euro`: Overall financial balance in Euro
-
-- `Gesamteinnahmen_Euro`: Total revenue in Euro
-
-- `Gesamtkosten_Euro`: Total costs in Euro
-
-- `akku_soc_pro_stunde`: Array of hourly battery state of charge values (%)
-
-## Timeframe overview
-
-```{figure} ../_static/optimization_timeframes.png
-:alt: Timeframe Overview
-
-Timeframe Overview
-```

docs/akkudoktoreos/prediction.md

@@ -1,28 +1,27 @@
 % SPDX-License-Identifier: Apache-2.0
-(prediction-page)=
 
 # Predictions
 
 Predictions, along with simulations and measurements, form the foundation upon which energy
 optimization is executed. In EOS, a standard set of predictions is managed, including:
 
-- Household Load Prediction
-- Electricity Price Prediction
-- Feed In Tariff Prediction
-- PV Power Prediction
-- Weather Prediction
+- **Household Load Prediction**
+- **Electricity Price Prediction**
+- **PV Power Prediction**
+- **Weather Prediction**
 
 ## Storing Predictions
 
 EOS stores predictions in a **key-value store**, where the term `prediction key` refers to the
-unique key used to retrieve specific prediction data.
+unique key used to retrieve specific prediction data. The key-value store is in memory. Stored
+data is lost on re-start of the EOS REST server.
 
 ## Prediction Providers
 
 Most predictions can be sourced from various providers. The specific provider to use is configured
 in the EOS configuration and can be set by prediction type. For example:
 
-```json
+```python
 {
   "weather": {
     "provider": "ClearOutside"
@@ -48,7 +47,7 @@ The prediction data must be provided in one of the following formats:
 
 A dictionary with the following structure:
 
-```json
+```python
     {
         "start_datetime": "2024-01-01 00:00:00",
         "interval": "1 Hour",
@@ -61,15 +60,13 @@ A dictionary with the following structure:
 #### 2. DateTimeDataFrame
 
 A JSON string created from a [pandas](https://pandas.pydata.org/docs/index.html) dataframe with a
-`DatetimeIndex`. Use
-[pandas.DataFrame.to_json(orient="index")](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_json.html#pandas.DataFrame.to_json).
+`DatetimeIndex`. Use [pandas.DataFrame.to_json(orient="index")](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_json.html#pandas.DataFrame.to_json).
 The column name of the data must be the same as the names of the `prediction key`s.
 
 #### 3. DateTimeSeries
 
 A JSON string created from a [pandas](https://pandas.pydata.org/docs/index.html) series with a
-`DatetimeIndex`. Use
-[pandas.Series.to_json(orient="index")](https://pandas.pydata.org/docs/reference/api/pandas.Series.to_json.html#pandas.Series.to_json).
+`DatetimeIndex`. Use [pandas.Series.to_json(orient="index")](https://pandas.pydata.org/docs/reference/api/pandas.Series.to_json.html#pandas.Series.to_json).
 
 ## Adjusted Predictions
 
@@ -119,11 +116,9 @@ Configuration options:
   - `provider`: Electricity price provider id of provider to be used.
 
     - `ElecPriceAkkudoktor`: Retrieves from Akkudoktor.net.
-    - `ElecPriceEnergyCharts`: Retrieves from Energy-Charts.info.
     - `ElecPriceImport`: Imports from a file or JSON string.
 
   - `charges_kwh`: Electricity price charges (€/kWh).
-  - `vat_rate`: VAT rate factor applied to electricity price when charges are used (default: 1.19).
   - `provider_settings.import_file_path`: Path to the file to import electricity price forecast data from.
   - `provider_settings.import_json`: JSON string, dictionary of electricity price forecast value lists.
 
@@ -135,24 +130,6 @@ prices by extrapolating historical price data combined with the most recent actu
 from Akkudoktor.net. Electricity price charges given in the `charges_kwh` configuration
 option are added.
 
-### ElecPriceEnergyCharts Provider
-
-The `ElecPriceEnergyCharts` provider retrieves day-ahead electricity market prices from
-[Energy-Charts.info](https://www.Energy-Charts.info). It supports both short-term and extended forecasting by combining
-real-time market data with historical price trends.
-
-- For the next 24 hours, market prices are fetched directly from Energy-Charts.info.
-- For periods beyond 24 hours, prices are estimated using extrapolation based on historical data and the latest
-  available market values.
-
-Charges and VAT
-
-- If `charges_kwh` configuration option is greater than 0, the electricity price is calculated as:
-  `(market price + charges_kwh) * vat_rate` where `vat_rate` is configurable (default: 1.19 for 19% VAT).
-- If `charges_kwh` is set to 0, the electricity price is simply: `market_price` (no VAT applied).
-
-**Note:** For the most accurate forecasts, it is recommended to set the `historic_hours` parameter to 840.
-
 ### ElecPriceImport Provider
 
 The `ElecPriceImport` provider is designed to import electricity prices from a file or a JSON
@@ -170,31 +147,11 @@ The electricity proce forecast data must be provided in one of the formats descr
 The data may additionally or solely be provided by the
 **PUT** `/v1/prediction/import/ElecPriceImport` endpoint.
 
-## Feed In Tariff Prediction
-
-Prediction keys:
-
-- `feed_in_tarif_wh`: Feed in tarif per Wh (€/Wh).
-- `feed_in_tarif_kwh`: Feed in tarif per kWh (€/kWh)
-
-Configuration options:
-
-- `feedintarif`: Feed in tariff configuration.
-
-  - `provider`: Feed in tariff provider id of provider to be used.
-
-    - `FeedInTariffFixed`: Provides fixed feed in tariff values.
-    - `FeedInTariffImport`: Imports from a file or JSON string.
-
-  - `provider_settings.feed_in_tariff_kwh`: Fixed feed in tariff (€/kWh).
-  - `provider_settings.import_file_path`: Path to the file to import feed in tariff forecast data from.
-  - `provider_settings.import_json`: JSON string, dictionary of feed in tariff value lists.
-
 ## Load Prediction
 
 Prediction keys:
 
-- `loadforecast_power_w`: Predicted load mean value (W).
+- `load_mean`: Predicted load mean value (W).
 - `load_std`: Predicted load standard deviation (W).
 - `load_mean_adjusted`: Predicted load mean value adjusted by load measurement (W).
 
@@ -205,55 +162,17 @@ Configuration options:
   - `provider`: Load provider id of provider to be used.
 
     - `LoadAkkudoktor`: Retrieves from local database.
-    - `LoadVrm`: Retrieves data from the VRM API by Victron Energy.
     - `LoadImport`: Imports from a file or JSON string.
 
-  - `provider_settings.LoadAkkudoktor.loadakkudoktor_year_energy_kwh`: Yearly energy consumption (kWh).
-  - `provider_settings.LoadVRM.load_vrm_token`: API token.
-  - `provider_settings.LoadVRM.load_vrm_idsite`: load_vrm_idsite.
-  - `provider_settings.LoadImport.loadimport_file_path`: Path to the file to import load forecast data from.
-  - `provider_settings.LoadImport.loadimport_json`: JSON string, dictionary of load forecast value lists.
+  - `provider_settings.loadakkudoktor_year_energy`: Yearly energy consumption (kWh).
+  - `provider_settings.loadimport_file_path`: Path to the file to import load forecast data from.
+  - `provider_settings.loadimport_json`: JSON string, dictionary of load forecast value lists.
 
 ### LoadAkkudoktor Provider
 
-The `LoadAkkudoktor` provider retrieves generic load data from the local database and scales
-it to match the annual energy consumption specified in the
-`LoadAkkudoktor.loadakkudoktor_year_energy` configuration option.
-
-### LoadAkkudoktorAdjusted Provider
-
-The `LoadAkkudoktorAdjusted` provider retrieves generic load data from the local database and scales
-it to match the annual energy consumption specified in the
-`LoadAkkudoktor.loadakkudoktor_year_energy` configuration option. In addition, the provider refines
-the forecast by incorporating available measured load data, ensuring a more realistic and
-site-specific consumption profile.
-
-For details on how to supply load measurements, see the [Measurements](measurement-page) section.
-
-### LoadVrm Provider
-
-The `LoadVrm` provider retrieves load forecast data from the VRM API by Victron Energy.
-To receive forecasts, the system data must be configured under Dynamic ESS in the VRM portal.
-To query the forecasts, an API token is required, which can also be created in the VRM portal under Preferences.
-This token must be stored in the EOS configuration along with the VRM-Installations-ID.
-
-```json
-    {
-        "load": {
-            "provider": "LoadVrm",
-            "provider_settings": {
-                "LoadVRM": {
-                    "load_vrm_token": "dummy-token",
-                    "load_vrm_idsite": 12345
-                }
-             }
-        }
-    }
-```
-
-The prediction keys for the load forecast data are:
-
-- `load_mean`: Predicted load mean value (W).
+The `LoadAkkudoktor` provider retrieves generic load data from a local database and tailors it to
+align with the annual energy consumption specified in the `loadakkudoktor_year_energy` configuration
+option.
 
 ### LoadImport Provider
 
@@ -293,25 +212,16 @@ Configuration options:
   - `provider`: PVForecast provider id of provider to be used.
 
     - `PVForecastAkkudoktor`: Retrieves from Akkudoktor.net.
-    - `PVForecastVrm`: Retrieves data from the VRM API by Victron Energy.
     - `PVForecastImport`: Imports from a file or JSON string.
 
   - `planes[].surface_tilt`: Tilt angle from horizontal plane. Ignored for two-axis tracking.
-  - `planes[].surface_azimuth`: Orientation (azimuth angle) of the (fixed) plane.
-                                Clockwise from north (north=0, east=90, south=180, west=270).
+  - `planes[].surface_azimuth`: Orientation (azimuth angle) of the (fixed) plane. Clockwise from north (north=0, east=90, south=180, west=270).
   - `planes[].userhorizon`: Elevation of horizon in degrees, at equally spaced azimuth clockwise from north.
   - `planes[].peakpower`: Nominal power of PV system in kW.
   - `planes[].pvtechchoice`: PV technology. One of 'crystSi', 'CIS', 'CdTe', 'Unknown'.
-  - `planes[].mountingplace`: Type of mounting for PV system.
-                              Options are 'free' for free-standing and 'building' for building-integrated.
+  - `planes[].mountingplace`: Type of mounting for PV system. Options are 'free' for free-standing and 'building' for building-integrated.
   - `planes[].loss`: Sum of PV system losses in percent
-  - `planes[].trackingtype`: Type of suntracking.
-                             0=fixed,
-                             1=single horizontal axis aligned north-south,
-                             2=two-axis tracking,
-                             3=vertical axis tracking,
-                             4=single horizontal axis aligned east-west,
-                             5=single inclined axis aligned north-south.
+  - `planes[].trackingtype`: Type of suntracking. 0=fixed, 1=single horizontal axis aligned north-south, 2=two-axis tracking, 3=vertical axis tracking, 4=single horizontal axis aligned east-west, 5=single inclined axis aligned north-south.
   - `planes[].optimal_surface_tilt`: Calculate the optimum tilt angle. Ignored for two-axis tracking.
   - `planes[].optimalangles`: Calculate the optimum tilt and azimuth angles. Ignored for two-axis tracking.
   - `planes[].albedo`: Proportion of the light hitting the ground that it reflects back.
@@ -323,73 +233,39 @@ Configuration options:
   - `provider_settings.import_file_path`: Path to the file to import PV forecast data from.
   - `provider_settings.import_json`: JSON string, dictionary of PV forecast value lists.
 
----
+------
 
-Detailed definitions taken from
-[PVGIS](https://joint-research-centre.ec.europa.eu/photovoltaic-geographical-information-system-pvgis/getting-started-pvgis/pvgis-user-manual_en).
+Some of the planes configuration options directly follow the [PVGIS](https://joint-research-centre.ec.europa.eu/photovoltaic-geographical-information-system-pvgis/getting-started-pvgis/pvgis-user-manual_en) nomenclature.
+
+Detailed definitions taken from **PVGIS**:
 
 - `pvtechchoice`
 
-The performance of PV modules depends on the temperature and on the solar irradiance, but the exact
-dependence varies between different types of PV modules. At the moment we can estimate the losses
-due to temperature and irradiance effects for the following types of modules: crystalline silicon
-cells; thin film modules made from CIS or CIGS and thin film modules made from Cadmium Telluride
-(CdTe).
+The performance of PV modules depends on the temperature and on the solar irradiance, but the exact dependence varies between different types of PV modules. At the moment we can estimate the losses due to temperature and irradiance effects for the following types of modules: crystalline silicon cells; thin film modules made from CIS or CIGS and thin film modules made from Cadmium Telluride (CdTe).
 
-For other technologies (especially various amorphous technologies), this correction cannot be
-calculated here. If you choose one of the first three options here the calculation of performance
-will take into account the temperature dependence of the performance of the chosen technology. If
-you choose the other option (other/unknown), the calculation will assume a loss of 8% of power due
-to temperature effects (a generic value which has found to be reasonable for temperate climates).
+For other technologies (especially various amorphous technologies), this correction cannot be calculated here. If you choose one of the first three options here the calculation of performance will take into account the temperature dependence of the performance of the chosen technology. If you choose the other option (other/unknown), the calculation will assume a loss of 8% of power due to temperature effects (a generic value which has found to be reasonable for temperate climates).
 
-PV power output also depends on the spectrum of the solar radiation. PVGIS can calculate how the
-variations of the spectrum of sunlight affects the overall energy production from a PV system. At
-the moment this calculation can be done for crystalline silicon and CdTe modules. Note that this
-calculation is not yet available when using the NSRDB solar radiation database.
+PV power output also depends on the spectrum of the solar radiation. PVGIS can calculate how the variations of the spectrum of sunlight affects the overall energy production from a PV system. At the moment this calculation can be done for crystalline silicon and CdTe modules. Note that this calculation is not yet available when using the NSRDB solar radiation database.
 
 - `peakpower`
 
-This is the power that the manufacturer declares that the PV array can produce under standard test
-conditions (STC), which are a constant 1000W of solar irradiation per square meter in the plane of
-the array, at an array temperature of 25°C. The peak power should be entered in kilowatt-peak (kWp).
-If you do not know the declared peak power of your modules but instead know the area of the modules
-and the declared conversion efficiency (in percent), you can calculate the peak power as
-power = area \* efficiency / 100.
+This is the power that the manufacturer declares that the PV array can produce under standard test conditions (STC), which are a constant 1000W of solar irradiation per square meter in the plane of the array, at an array temperature of 25°C. The peak power should be entered in kilowatt-peak (kWp). If you do not know the declared peak power of your modules but instead know the area of the modules and the declared conversion efficiency (in percent), you can calculate the peak power as power = area * efficiency / 100.
 
-Bifacial modules: PVGIS doesn't make specific calculations for bifacial modules at present. Users
-who wish to explore the possible benefits of this technology can input the power value for Bifacial
-Nameplate Irradiance. This can also be can also be estimated from the front side peak power P_STC
-value and the bifaciality factor, φ (if reported in the module data sheet) as:
-P_BNPI = P_STC \* (1 + φ \* 0.135). NB this bifacial approach is not appropriate for BAPV or BIPV
-installations or for modules mounting on a N-S axis i.e. facing E-W.
+Bifacial modules: PVGIS doesn't make specific calculations for bifacial modules at present. Users who wish to explore the possible benefits of this technology can input the power value for Bifacial Nameplate Irradiance. This can also be can also be estimated from the front side peak power P_STC value and the bifaciality factor, φ (if reported in the module data sheet) as: P_BNPI  = P_STC * (1 + φ * 0.135). NB this bifacial approach  is not appropriate for BAPV or BIPV installations or for modules mounting on a N-S axis i.e. facing E-W.
 
 - `loss`
 
-The estimated system losses are all the losses in the system, which cause the power actually
-delivered to the electricity grid to be lower than the power produced by the PV modules. There are
-several causes for this loss, such as losses in cables, power inverters, dirt (sometimes snow) on
-the modules and so on. Over the years the modules also tend to lose a bit of their power, so the
-average yearly output over the lifetime of the system will be a few percent lower than the output
-in the first years.
+The estimated system losses are all the losses in the system, which cause the power actually delivered to the electricity grid to be lower than the power produced by the PV modules. There are several causes for this loss, such as losses in cables, power inverters, dirt (sometimes snow) on the modules and so on. Over the years the modules also tend to lose a bit of their power, so the average yearly output over the lifetime of the system will be a few percent lower than the output in the first years.
 
-We have given a default value of 14% for the overall losses. If you have a good idea that your value
-will be different (maybe due to a really high-efficiency inverter) you may reduce this value a little.
+We have given a default value of 14% for the overall losses. If you have a good idea that your value will be different (maybe due to a really high-efficiency inverter) you may reduce this value a little.
 
 - `mountingplace`
 
-For fixed (non-tracking) systems, the way the modules are mounted will have an influence on the
-temperature of the module, which in turn affects the efficiency. Experiments have shown that if the
-movement of air behind the modules is restricted, the modules can get considerably hotter
-(up to 15°C at 1000W/m2 of sunlight).
+For fixed (non-tracking) systems, the way the modules are mounted will have an influence on the temperature of the module, which in turn affects the efficiency. Experiments have shown that if the movement of air behind the modules is restricted, the modules can get considerably hotter (up to 15°C at 1000W/m2 of sunlight).
 
-In PVGIS there are two possibilities: free-standing, meaning that the modules are mounted on a rack
-with air flowing freely behind the modules; and building- integrated, which means that the modules
-are completely built into the structure of the wall or roof of a building, with no air movement
-behind the modules.
+In PVGIS there are two possibilities: free-standing, meaning that the modules are mounted on a rack with air flowing freely behind the modules; and building- integrated, which means that the modules are completely built into the structure of the wall or roof of a building, with no air movement behind the modules.
 
-Some types of mounting are in between these two extremes, for instance if the modules are mounted on
-a roof with curved roof tiles, allowing air to move behind the modules. In such cases, the
-performance will be somewhere between the results of the two calculations that are possible here.
+Some types of mounting are in between these two extremes, for instance if the modules are mounted on a roof with curved roof tiles, allowing air to move behind the modules. In such cases, the performance will be somewhere between the results of the two calculations that are possible here.
 
 - `userhorizon`
 
@@ -401,10 +277,9 @@ represent equal angular distance around the horizon. For instance, if you have 3
 point is due north, the next is 10 degrees east of north, and so on, until the last point, 10
 degrees west of north.
 
----
+------
 
-Most of the configuration options are in line with the
-[PVLib](https://pvlib-python.readthedocs.io/en/stable/_modules/pvlib/iotools/pvgis.html) definition for PVGIS data.
+Most of the planes configuration options are in line with the [PVLib](https://pvlib-python.readthedocs.io/en/stable/_modules/pvlib/iotools/pvgis.html) definition for PVGIS data.
 
 Detailed definitions from **PVLib** for PVGIS data.
 
@@ -417,7 +292,7 @@ Tilt angle from horizontal plane.
 Orientation (azimuth angle) of the (fixed) plane. Clockwise from north (north=0, east=90, south=180,
 west=270). This is offset 180 degrees from the convention used by PVGIS.
 
----
+------
 
 ### PVForecastAkkudoktor Provider
 
@@ -432,8 +307,7 @@ The following prediction configuration options of the PV system must be set:
 For each plane of the PV system the following configuration options must be set:
 
 - `pvforecast.planes[].surface_tilt`: Tilt angle from horizontal plane. Ignored for two-axis tracking.
-- `pvforecast.planes[].surface_azimuth`: Orientation (azimuth angle) of the (fixed) plane.
-                                         Clockwise from north (north=0, east=90, south=180, west=270).
+- `pvforecast.planes[].surface_azimuth`: Orientation (azimuth angle) of the (fixed) plane. Clockwise from north (north=0, east=90, south=180, west=270).
 - `pvforecast.planes[].userhorizon`: Elevation of horizon in degrees, at equally spaced azimuth clockwise from north.
 - `pvforecast.planes[].inverter_paco`: AC power rating of the inverter. [W]
 - `pvforecast.planes[].peakpower`: Nominal power of PV system in kW.
@@ -454,56 +328,34 @@ Example:
         "surface_azimuth": -10,
         "surface_tilt": 7,
         "userhorizon": [20, 27, 22, 20],
-        "inverter_paco": 10000
+        "inverter_paco": 10000,
       },
       {
         "peakpower": 4.8,
         "surface_azimuth": -90,
         "surface_tilt": 7,
         "userhorizon": [30, 30, 30, 50],
-        "inverter_paco": 10000
+        "inverter_paco": 10000,
       },
       {
         "peakpower": 1.4,
         "surface_azimuth": -40,
         "surface_tilt": 60,
         "userhorizon": [60, 30, 0, 30],
-        "inverter_paco": 2000
+        "inverter_paco": 2000,
       },
       {
         "peakpower": 1.6,
         "surface_azimuth": 5,
         "surface_tilt": 45,
         "userhorizon": [45, 25, 30, 60],
-        "inverter_paco": 1400
+        "inverter_paco": 1400,
       }
     ]
   }
 }
 ```
 
-### PVForecastVrm Provider
-
-The `PVForecastVrm` provider retrieves pv power forecast data from the VRM API by Victron Energy.
-To receive forecasts, the system data must be configured under Dynamic ESS in the VRM portal.
-To query the forecasts, an API token is required, which can also be created in the VRM portal under Preferences.
-This token must be stored in the EOS configuration along with the VRM-Installations-ID.
-
-```python
-    {
-        "pvforecast": {
-            "provider": "PVForecastVrm",
-            "provider_settings": {
-                "pvforecast_vrm_token": "dummy-token",
-                "pvforecast_vrm_idsite": 12345
-            }
-    }
-```
-
-The prediction keys for the PV forecast data are:
-
-- `pvforecast_dc_power`: Total DC power (W).
-
 ### PVForecastImport Provider
 
 The `PVForecastImport` provider is designed to import PV forecast data from a file or a JSON
@@ -512,8 +364,8 @@ becomes available.
 
 The prediction keys for the PV forecast data are:
 
-- `pvforecast_ac_power`: Total AC power (W).
-- `pvforecast_dc_power`: Total DC power (W).
+- `pvforecast_ac_power`: Total DC power (W).
+- `pvforecast_dc_power`: Total AC power (W).
 
 The PV forecast data must be provided in one of the formats described in
 <project:#prediction-import-providers>. The data source can be given in the
@@ -546,7 +398,7 @@ Prediction keys:
 - `weather_temp_air`: Temperature (°C)
 - `weather_total_clouds`: Total Clouds (% Sky Obscured)
 - `weather_visibility`: Visibility (m)
-- `weather_wind_direction`: Wind Direction (°)
+- `weather_wind_direction`: "Wind Direction (°)
 - `weather_wind_speed`: Wind Speed (kmph)
 
 Configuration options:
@@ -555,8 +407,8 @@ Configuration options:
 
   - `provider`: Load provider id of provider to be used.
 
-    - `BrightSky`: Retrieves from [BrightSky](https://api.brightsky.dev).
-    - `ClearOutside`: Retrieves from [ClearOutside](https://clearoutside.com/forecast).
+    - `BrightSky`: Retrieves from https://api.brightsky.dev.
+    - `ClearOutside`: Retrieves from https://clearoutside.com/forecast.
     - `LoadImport`: Imports from a file or JSON string.
 
   - `provider_settings.import_file_path`: Path to the file to import weatherforecast data from.
@@ -578,7 +430,7 @@ The provider provides forecast data for the following prediction keys:
 - `weather_temp_air`: Temperature (°C)
 - `weather_total_clouds`: Total Clouds (% Sky Obscured)
 - `weather_visibility`: Visibility (m)
-- `weather_wind_direction`: Wind Direction (°)
+- `weather_wind_direction`: "Wind Direction (°)
 - `weather_wind_speed`: Wind Speed (kmph)
 
 ### ClearOutside Provider
@@ -608,7 +460,7 @@ The provider provides forecast data for the following prediction keys:
 - `weather_temp_air`: Temperature (°C)
 - `weather_total_clouds`: Total Clouds (% Sky Obscured)
 - `weather_visibility`: Visibility (m)
-- `weather_wind_direction`: Wind Direction (°)
+- `weather_wind_direction`: "Wind Direction (°)
 - `weather_wind_speed`: Wind Speed (kmph)
 
 ### WeatherImport Provider
@@ -639,7 +491,7 @@ The prediction keys for the weather forecast data are:
 - `weather_temp_air`: Temperature (°C)
 - `weather_total_clouds`: Total Clouds (% Sky Obscured)
 - `weather_visibility`: Visibility (m)
-- `weather_wind_direction`: Wind Direction (°)
+- `weather_wind_direction`: "Wind Direction (°)
 - `weather_wind_speed`: Wind Speed (kmph)
 
 The PV forecast data must be provided in one of the formats described in

docs/akkudoktoreos/resource.md

@@ -1,258 +0,0 @@
-% SPDX-License-Identifier: Apache-2.0
-(resource-page)=
-
-# Resources (Device Simulations)
-
-## Concepts
-
-The simulations for resources are leaning on general concepts of the [S2 standard].
-
-### Control Types
-
-The control of resources and such what a resource simulation will simulate follows three
-basic control principles:
-
-- Operation Mode Based Control (OMBC)
-- Fill Rate Based Control (FRBC)
-- Demand Driven Based Control (DDBC)
-
-Although these control principles differ enough to separate them into three distinct control types,
-there are some common aspects that make them similar:
-
-- Operation Modes
-- Transitions and
-- Timers.
-
-The objective for a control type is under which circumstances what things can be adjusted, and what
-the constraints are for these adjustments. The three control types model a virtual, abstract resource
-for simulation.
-
-The abstract resource ignores all details of pyhsical device that are not relevant to energy
-management. In addition, physical devices have an enormous variety in parameters, sensors, control
-strategies, concerns, safeguards, and so on. It would be practically impossible to develop a
-simulation that can
-understand all the parameters of all the physical devices on the market. By making the resource more
-abstract, its concepts can be translated to all sorts of physical devices, even though internally
-they function very differently. As a consequence, it not always possible to make a 100% accurate
-description of all the behaviors and constraints in these abstractions. But the abstractions used
-in the control types are quite powerful, and should allow you to come pretty close.
-
-The control types basically define how the simulated resource can be described. The user in the end
-selects the proper desciption of a physical device using the configuration options provided for
-resource simulations. The configuration sets how the simulated resource functions, what it can do and
-what kind of constraints it has.
-
-### Resource Simulation
-
-Based on the description of this virtual resource, the resource simulation can make predictions of
-what the physical device will do in certain situations, and when it is allowed to execute
-instructions generated by the optimization as part of the energy management plan evaluation.
-
-### Resource Status
-
-Once the physical device has changed it's behavior, the resource simulation should be informed
-to make the simulation change it's state accordingly.
-
-The actual state of a pyhsical device may be reported to the resource simulation by the
-**PUT** `/v1/resource/status` API endpoint.
-
-## Battery
-
-There is a wealth of possible battery operation modes:
-
-<!-- pyml disable line-length -->
-| Mode                      | Purpose / Behavior                                                                      | Typical Trigger / Context                                        |
-| ------------------------- | --------------------------------------------------------------------------------------- | ---------------------------------------------------------------- |
-| **IDLE**                  | Battery neither charges nor discharges (SOC stable).                                    | No active control objective or power imbalance below thresholds. |
-| **SELF_CONSUMPTION**      | Charge from PV surplus and discharge to cover local load.                               | PV generation > load (charge) or load > PV (discharge).          |
-| **NON_EXPORT**   | Charge from on-site or local surplus with the goal of minimizing or preventing energy export to the external grid. Discharging to the grid is not allowed. | Export limit reached and SOC < SOC_max.                          |
-| **PEAK_SHAVING**          | Discharge to keep grid import below a target threshold.                                 | Predicted or measured site load exceeds peak limit.              |
-| **GRID_SUPPORT_EXPORT**   | Discharge energy to grid for revenue (V2G, wholesale market, flexibility service).      | Market or signal permits profitable export.                      |
-| **GRID_SUPPORT_IMPORT**   | Charge from grid to absorb surplus or provide up-regulation service.                    | Low-price or grid-support signal detected.                       |
-| **FREQUENCY_REGULATION**  | Rapid charge/discharge response to grid frequency deviations.                           | Active participation in frequency control.                       |
-| **RAMP_RATE_CONTROL**     | Smooth site-level power ramp rates by buffering fluctuations.                           | Sudden PV/load change exceeding ramp limit.                      |
-| **RESERVE_BACKUP**        | Maintain SOC ≥ reserve threshold to ensure backup capacity.                             | Resilience mode active, grid operational.                        |
-| **OUTAGE_SUPPLY**         | Islanded operation: power local loads using stored energy (and PV if available).        | Grid failure detected.                                           |
-| **FORCED_CHARGE**         | Manual or external control command to charge (e.g., pre-event, maintenance). No discharge.  | Operator or optimizer command. |
-| **FORCED_DISCHARGE**      | Manual or external control command to discharge. No charge.                             | Operator or optimizer command.                                   |
-| **FAULT**                 | Battery unavailable due to fault, safety, or protection state.                          | Fault detected (thermal, voltage, comms, etc.).                  |
-<!-- pyml enable line-length -->
-
-The optimization algorithm, the device simulation and the configuration properties only support the
-most important of these modes.
-
-### Battery Simulation
-
-The battery simulation assumes an idealized battery model. Under this model, the battery can be
-operated in three discrete operation modes with fill rate based control (FRBC):
-
-| **Operation Mode ID**    | **Description**                                                       |
-| ------------------------ | --------------------------------------------------------------------- |
-| **SELF_CONSUMPTION**     | Charge from local surplus and discharge to cover local load.          |
-| **NON_EXPORT**           | Charge from local surplus and do not discharge.                       |
-| **FORCED_CHARGE**        | Charge.                                                               |
-
-The **operation mode factor** (0.0–1.0) specifies the normalized power rate relative to the
-battery's nominal maximum charge or discharge power. A value of 1.0 corresponds to full-rate
-charging or discharging, while 0.0 indicates no power transfer. Intermediate values scale the power
-proportionally.
-
-The **fill level** (0.0–1.0) specifies the normalized fill level relative to the
-battery's nominal maximum charge. A value of 1.0 corresponds to full while 0.0 indicates empty.
-Intermediate values scale the fill level proportionally.
-
-### Battery Configuration
-
-### Battery Stati
-
-To keep the battery simulation in synchonization with the actual stati of the battery the following
-resource stati may be reported to EOS by the **PUT** `/v1/resource/status` API endpoint.
-
-#### Battery FRBCActuatorStatus
-
-The operation mode the battery is currently operated.
-
-```json
-{
-  "type": "FRBCActuatorStatus",
-  "active_operation_mode_id": "GRID_SUPPORT_IMPORT",
-  "operation_mode_factor": "0.375",
-  "previous_operation_mode_id": "SELF_CONSUMPTION",
-  "transistion_timestamp": "20250725T12:00:12"
-}
-```
-
-#### Battery FRBCStorageStatus
-
-The current battery state of charge (SoC).
-
-```json
-{
-  "type": "FRBCStorageStatus",
-  "present_fill_level": "0.88"
-}
-```
-
-#### Battery PowerMeasurement
-
-The current power that the battery is charged or discharged with \[W\].
-
-```json
-{
-  "type": "PowerMeasurement",
-  "measurement_timestamp": "20250725T12:00:12",
-  "values": [
-    {
-      "commodity_quantity": "ELECTRIC.POWER.L1",
-      "value": "887.5"
-    },
-    {
-      "commodity_quantity": "ELECTRIC.POWER.L2",
-      "value": "905.5"
-    },
-    {
-      "commodity_quantity": "ELECTRIC.POWER.L2",
-      "value": "1100.7"
-    },
-  ]
-}
-```
-
-For symmetric (or unknown) power distribution:
-
-```json
-{
-  "type": "PowerMeasurement",
-  "measurement_timestamp": "20250725T12:00:12",
-  "values": [
-    {
-      "commodity_quantity": "ELECTRIC.POWER.3_PHASE_SYM",
-      "value": "1000"
-    }
-  ]
-}
-```
-
-## Electric Vehicle
-
-The electric vehicle is basically a battery with a reduced set of operation modes.
-
-### Electric Vehicle Instructions
-
-The electric vehicle control instructions assume an idealized EV battery model. Under this model,
-the EV battery can be operated in two operation modes:
-
-| **Operation Mode ID** | **Description**                                                         |
-| --------------------- | ----------------------------------------------------------------------- |
-| **IDLE**              | Battery neither charges nor discharges; holds its state of charge.      |
-| **FORCED_CHARGE**     | Charge at a specified power rate up to the allowable maximum.           |
-
-The **operation mode factor** (0.0–1.0) specifies the normalized power rate relative to the
-battery's nominal maximum charge power. A value of 1.0 corresponds to full-rate charging, while 0.0
-indicates no power transfer. Intermediate values scale the power proportionally.
-
-## Home Appliance
-
-The optimization algorithm supports one start of the home appliance within the optimization
-horizon.
-
-### Home Appliance Simulation
-
-### Home Appliance Configuration
-
-Home appliance to run within the optimization horizon.
-
-```json
-[
-  {
-    "device_id": "dishwasher1",
-    "consumption_wh": 2000,
-    "duration_h": 3
-  }
-]
-```
-
-Home appliance to run within a time window of 5 hours starting at 8:00 every day and another time
-window of 3 hours starting at 15:00 every day. See
-[Time Window Sequence Configuration](configtimewindow-page) for more information.
-
-```json
-[
-  {
-    "device_id": "dishwasher1",
-    "consumption_wh": 2000,
-    "duration_h": 3,
-    "time_windows": {
-      "windows": [
-        {
-          "start_time": "08:00",
-          "duration": "5 hours"
-        },
-        {
-          "start_time": "15:00",
-          "duration": "3 hours"
-        }
-      ]
-    }
-  }
-]
-```
-
-:::{admonition} Note
-:class: note
-The optimization algorithm always restricts to one start within the optimization horizon per
-energy management run.
-:::
-
-### Home Appliance Instructions
-
-The home appliance instructions assume an idealized home appliance model. Under this model,
-the home appliance can be operated in two operation modes:
-
-| **Operation Mode ID** | **Description**                                                         |
-|-----------------------|-------------------------------------------------------------------------|
-| **RUN**               | The home appliance is started and runs until the end of it's power      |
-|                       | sequence.                                                               |
-| **IDLE**              | The home appliance does not run.                                        |
-
-The **operation mode factor** (0.0–1.0) is ignored.

docs/akkudoktoreos/serverapi.md

@@ -1,5 +1,4 @@
 % SPDX-License-Identifier: Apache-2.0
-(server-api-page)=
 
 # Server API
 

docs/conf.py

@@ -99,7 +99,7 @@ html_theme_options = {
     "logo_only": False,
     "titles_only": True,
 }
-html_css_files = ["eos.css"]  # Make body size wider
+html_css_files = ["eos.css"]
 
 # -- Options for autodoc -------------------------------------------------
 # https://www.sphinx-doc.org/en/master/usage/extensions/autodoc.html

docs/develop/CHANGELOG.md

@@ -1,4 +0,0 @@
-```{include} ../../CHANGELOG.md
-:relative-docs: ../
-:relative-images:
-```

docs/develop/develop.md

@@ -1,593 +0,0 @@
-% SPDX-License-Identifier: Apache-2.0
-(develop-page)=
-
-# Development Guide
-
-## Development Prerequisites
-
-Have or
-[create](https://docs.github.com/en/get-started/start-your-journey/creating-an-account-on-github)
-a [GitHub](https://github.com/) account.
-
-Make shure all the source installation prequistes are installed. See the
-[installation guideline](#install-page) for a detailed list of tools.
-
-Under Linux the [make](https://www.gnu.org/software/make/manual/make.html) tool should be installed
-as we have a lot of pre-fabricated commands for it.
-
-Install your favorite editor or integrated development environment (IDE):
-
-- Full-Featured IDEs
-
-  - [Eclipse + PyDev](https://www.pydev.org/)
-  - [KDevelop](https://www.kdevelop.org/)
-  - [PyCharm](https://www.jetbrains.com/pycharm/)
-  - ...
-
-- Code Editors with Python Support
-
-  - [Visual Studio Code (VS Code)](https://code.visualstudio.com/)
-  - [Sublime Text](https://www.sublimetext.com/)
-  - [Atom / Pulsar](https://pulsar-edit.dev/)
-  - ...
-
-- Python-Focused or Beginner-Friendly IDEs
-
-  - [Spyder](https://www.spyder-ide.org/)
-  - [Thonny](https://thonny.org/)
-  - [IDLE](https://www.python.org/downloads/)
-  - ...
-
-## Step 1 – Fork the Repository
-
-[Fork the EOS repository](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/fork-a-repo)
-to your GitHub account.
-
-Clone your fork locally and add the EOS upstream remote to track updates.
-
-```{eval-rst}
-.. tabs::
-
-  .. tab:: Windows
-
-     .. code-block:: powershell
-
-        git clone https://github.com/<YOURUSERNAME>/EOS.git
-        cd EOS
-        git remote add eos https://github.com/Akkudoktor-EOS/EOS.git
-
-  .. tab:: Linux
-
-     .. code-block:: bash
-
-        git clone https://github.com/<YOURUSERNAME>/EOS.git
-        cd EOS
-        git remote add eos https://github.com/Akkudoktor-EOS/EOS.git
-```
-
-Replace `<YOURUSERNAME>` with your GitHub username.
-
-## Step 2 – Development Setup
-
-This is recommended for developers who want to modify the source code and test changes locally.
-
-### Step 2.1 – Create a Virtual Environment
-
-```{eval-rst}
-.. tabs::
-
-  .. tab:: Windows
-
-     .. code-block:: powershell
-
-        python -m venv .venv
-        .venv\Scripts\pip install --upgrade pip
-        .venv\Scripts\pip install -r requirements-dev.txt
-        .venv\Scripts\pip install build
-        .venv\Scripts\pip install -e .
-
-  .. tab:: Linux
-
-     .. code-block:: bash
-
-        python3 -m venv .venv
-        .venv/bin/pip install --upgrade pip
-        .venv/bin/pip install -r requirements-dev.txt
-        .venv/bin/pip install build
-        .venv/bin/pip install -e .
-
-  .. tab:: Linux Make
-
-     .. code-block:: bash
-
-        make install
-```
-
-### Step 2.2 – Activate the Virtual Environment
-
-```{eval-rst}
-.. tabs::
-
-  .. tab:: Windows
-
-     .. code-block:: powershell
-
-        .venv\Scripts\activate.bat
-
-  .. tab:: Linux
-
-     .. code-block:: bash
-
-        source .venv/bin/activate
-```
-
-### Step 2.3 - Install pre-commit
-
-Our code style and commit message checks use [`pre-commit`](https://pre-commit.com).
-
-```{eval-rst}
-.. tabs::
-
-  .. tab:: Windows
-
-     .. code-block:: powershell
-
-        pre-commit install
-        pre-commit install --hook-type commit-msg --hook-type pre-push
-
-  .. tab:: Linux
-
-     .. code-block:: bash
-
-        pre-commit install
-        pre-commit install --hook-type commit-msg --hook-type pre-push
-```
-
-## Step 3 - Run EOS
-
-Make EOS accessible at [http://localhost:8503/docs](http://localhost:8503/docs) and EOSdash at
-[http://localhost:8504](http://localhost:8504).
-
-### Option 1 – Using Python Virtual Environment
-
-```{eval-rst}
-.. tabs::
-
-  .. tab:: Windows
-
-     .. code-block:: powershell
-
-        python -m akkudoktoreos.server.eos
-
-  .. tab:: Linux
-
-     .. code-block:: bash
-
-        python -m akkudoktoreos.server.eos
-
-  .. tab:: Linux Make
-
-     .. code-block:: bash
-
-        make run
-```
-
-To have full control of the servers during development you may start the servers independently -
-e.g. in different terminal windows. Don't forget to activate the virtual environment in your
-terminal window.
-
-:::{admonition} Note
-:class: note
-If you killed or stopped the servers shortly before, the ports may still be occupied by the last
-processes. It may take more than 60 seconds until the ports are released.
-:::
-
-You may add the `--reload true` parameter to have the servers automatically restarted on source code
-changes. It is best to also add `--startup_eosdash false` to EOS to prevent the automatic restart
-interfere with the EOS server trying to start EOSdash.
-
-<!-- pyml disable line-length -->
-```{eval-rst}
-.. tabs::
-
-  .. tab:: Windows
-
-     .. code-block:: powershell
-
-        python -m akkudoktoreos.server.eosdash --host localhost --port 8504 --log_level DEBUG --reload true
-
-  .. tab:: Linux
-
-     .. code-block:: bash
-
-        python -m akkudoktoreos.server.eosdash --host localhost --port 8504 --log_level DEBUG --reload true
-
-  .. tab:: Linux Make
-
-     .. code-block:: bash
-
-        make run-dash-dev
-```
-
-```{eval-rst}
-.. tabs::
-
-  .. tab:: Windows
-
-     .. code-block:: powershell
-
-        python -m akkudoktoreos.server.eos --host localhost --port 8503 --log_level DEBUG --startup_eosdash false --reload true
-
-  .. tab:: Linux
-
-     .. code-block:: bash
-
-        python -m akkudoktoreos.server.eos --host localhost --port 8503 --log_level DEBUG --startup_eosdash false --reload true
-
-  .. tab:: Linux Make
-
-     .. code-block:: bash
-
-        make run-dev
-```
-<!-- pyml enable line-length -->
-
-### Option 2 – Using Docker
-
-#### Step 3.1 – Build the Docker Image
-
-```{eval-rst}
-.. tabs::
-
-  .. tab:: Windows
-
-     .. code-block:: powershell
-
-        docker build -t akkudoktoreos .
-
-  .. tab:: Linux
-
-     .. code-block:: bash
-
-        docker build -t akkudoktoreos .
-```
-
-#### Step 3.2 – Run the Container
-
-```{eval-rst}
-.. tabs::
-
-  .. tab:: Windows
-
-     .. code-block:: powershell
-
-        docker run -d `
-          --name akkudoktoreos `
-          -p 8503:8503 `
-          -p 8504:8504 `
-          -e OPENBLAS_NUM_THREADS=1 `
-          -e OMP_NUM_THREADS=1 `
-          -e MKL_NUM_THREADS=1 `
-          -e EOS_SERVER__HOST=0.0.0.0 `
-          -e EOS_SERVER__PORT=8503 `
-          -e EOS_SERVER__EOSDASH_HOST=0.0.0.0 `
-          -e EOS_SERVER__EOSDASH_PORT=8504 `
-          --ulimit nproc=65535:65535 `
-          --ulimit nofile=65535:65535 `
-          --security-opt seccomp=unconfined `
-          akkudoktor-eos:latest
-
-  .. tab:: Linux
-
-     .. code-block:: bash
-
-          docker run -d \
-            --name akkudoktoreos \
-            -p 8503:8503 \
-            -p 8504:8504 \
-            -e OPENBLAS_NUM_THREADS=1 \
-            -e OMP_NUM_THREADS=1 \
-            -e MKL_NUM_THREADS=1 \
-            -e EOS_SERVER__HOST=0.0.0.0 \
-            -e EOS_SERVER__PORT=8503 \
-            -e EOS_SERVER__EOSDASH_HOST=0.0.0.0 \
-            -e EOS_SERVER__EOSDASH_PORT=8504 \
-            --ulimit nproc=65535:65535 \
-            --ulimit nofile=65535:65535 \
-            --security-opt seccomp=unconfined \
-            akkudoktor-eos:latest
-```
-
-#### Step 3.3 – Manage the Container
-
-```{eval-rst}
-.. tabs::
-
-  .. tab:: Windows
-
-     .. code-block:: powershell
-
-        docker logs -f akkudoktoreos
-        docker stop akkudoktoreos
-        docker start akkudoktoreos
-        docker rm -f akkudoktoreos
-
-  .. tab:: Linux
-
-     .. code-block:: bash
-
-        docker logs -f akkudoktoreos
-        docker stop akkudoktoreos
-        docker start akkudoktoreos
-        docker rm -f akkudoktoreos
-```
-
-For detailed Docker instructions, refer to [Installation Guideline](install-page)
-
-### Step 4 - Create the changes
-
-#### Step 4.1 - Create a development branch
-
-```bash
-git checkout -b <MY_DEVELOPMENT_BRANCH>
-```
-
-Replace `<MY_DEVELOPMENT_BRANCH>` with the development branch name. The branch name shall be of the
-format (feat|fix|chore|docs|refactor|test)/[a-z0-9._-]+, e.g:
-
-- feat/my_cool_new_feature
-- fix/this_annoying_bug
-- ...
-
-#### Step 4.2 – Edit the sources
-
-Use your fovourite editor or IDE to edit the sources.
-
-#### Step 4.3 - Check the source code for correct format
-
-```{eval-rst}
-.. tabs::
-
-  .. tab:: Windows
-
-     .. code-block:: powershell
-
-        pre-commit run --all-files
-
-  .. tab:: Linux
-
-     .. code-block:: bash
-
-        pre-commit run --all-files
-
-  .. tab:: Linux Make
-
-     .. code-block:: bash
-
-        make format
-```
-
-#### Step 4.4 - Test the changes
-
-At a minimum, you should run the module tests:
-
-```{eval-rst}
-.. tabs::
-
-  .. tab:: Windows
-
-     .. code-block:: powershell
-
-        pytest -vs --cov src --cov-report term-missing
-
-  .. tab:: Linux
-
-     .. code-block:: bash
-
-        pytest -vs --cov src --cov-report term-missing
-
-  .. tab:: Linux Make
-
-     .. code-block:: bash
-
-        make test
-```
-
-You should also run the system tests. These include additional tests that interact with real
-resources:
-
-```{eval-rst}
-.. tabs::
-
-  .. tab:: Windows
-
-     .. code-block:: powershell
-
-        pytest --system-test -vs --cov src --cov-report term-missing
-
-  .. tab:: Linux
-
-     .. code-block:: bash
-
-        pytest --system-test -vs --cov src --cov-report term-missing
-
-  .. tab:: Linux Make
-
-     .. code-block:: bash
-
-        make test-system
-```
-
-To do profiling use:
-
-```{eval-rst}
-.. tabs::
-
-  .. tab:: Windows
-
-     .. code-block:: powershell
-
-        python tests/single_test_optimization.py --profile
-
-  .. tab:: Linux
-
-     .. code-block:: bash
-
-        python tests/single_test_optimization.py --profile
-
-  .. tab:: Linux Make
-
-     .. code-block:: bash
-
-        make test-profile
-```
-
-#### Step 4.5 - Commit the changes
-
-Add the changed and new files to the commit.
-
-Create a commit.
-
-### Step 5 - Pull request
-
-Before creating a pull request assure the changes are based on the latest EOS upstream.
-
-Update your local main branch:
-
-```bash
-git checkout main
-git pull eos main
-```
-
-Switch back to your local development branch and rebase to main.
-
-```bash
-git checkout <MY_DEVELOPMENT_BRANCH>
-git rebase -i main
-```
-
-During rebase you can also squash your changes into one (preferred) or a set of commits that have
-proper commit messages and can easily be reviewed.
-
-After rebase run the tests once again.
-
-If everything is ok push the commit(s) to your fork on Github.
-
-```bash
-git push -f origin
-```
-
-If your push by intention does not comply to the rules you can skip the verification by:
-
-```bash
-git push -f --no-verify origin
-```
-
-<!-- pyml disable line-length -->
-Once ready, [submit a pull request](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/creating-a-pull-request-from-a-fork)
-with your fork to the [Akkudoktor-EOS/EOS@master](https://github.com/Akkudoktor-EOS/EOS) repository.
-<!-- pyml enable line-length -->
-
-## Developer Tips
-
-### Keep Your Fork Updated
-
-Regularly pull changes from the eos repository to avoid merge conflicts:
-
-```{eval-rst}
-.. tabs::
-
-  .. tab:: Windows
-
-     .. code-block:: powershell
-
-        git checkout main
-        git pull eos main
-        git push origin
-
-  .. tab:: Linux
-
-     .. code-block:: bash
-
-        git checkout main
-        git pull eos main
-        git push origin
-```
-
-Rebase your development branch to the latest eos main branch.
-
-```{eval-rst}
-.. tabs::
-
-  .. tab:: Windows
-
-     .. code-block:: powershell
-
-        git checkout  <MY_DEVELOPMENT_BRANCH>
-        git rebase -i main
-
-  .. tab:: Linux
-
-     .. code-block:: bash
-
-        git checkout  <MY_DEVELOPMENT_BRANCH>
-        git rebase -i main
-```
-
-### Create Feature Branches
-
-Work in separate branches for each feature or bug fix:
-
-```{eval-rst}
-.. tabs::
-
-  .. tab:: Windows
-
-     .. code-block:: powershell
-
-        git checkout -b feat/my-feature
-
-  .. tab:: Linux
-
-     .. code-block:: bash
-
-        git checkout -b feat/my-feature
-```
-
-### Run Tests Frequently
-
-Ensure your changes do not break existing functionality:
-
-```{eval-rst}
-.. tabs::
-
-  .. tab:: Windows
-
-     .. code-block:: powershell
-
-        pytest -vs --cov src --cov-report term-missing
-
-  .. tab:: Linux
-
-     .. code-block:: bash
-
-        pytest -vs --cov src --cov-report term-missing
-
-  .. tab:: Linux Make
-
-     .. code-block:: bash
-
-        make test
-```
-
-### Follow Coding Standards
-
-Keep your code consistent with existing style and conventions.
-
-### Use Issues for Discussion
-
-Before making major changes, open an issue or discuss with maintainers.
-
-### Document Changes
-
-Update docstrings, comments, and any relevant documentation.

docs/develop/getting_started.md

@@ -1,86 +1,111 @@
-% SPDX-License-Identifier: Apache-2.0
-(getting-started-page)=
-
 # Getting Started
 
-## Installation and Running
+## Installation
 
-AkkudoktorEOS can be installed and run using several different methods:
+The project requires Python 3.10 or newer. Currently there are no official packages or images published.
 
-- **Release package** (for stable versions)
-- **Docker image** (for easy deployment)
-- **From source** (for developers)
+Following sections describe how to locally start the EOS server on `http://localhost:8503`.
 
-See the [installation guideline](#install-page) for detailed instructions on each method.
+### Run from source
 
-### Where to Find AkkudoktorEOS
+Install the dependencies in a virtual environment:
 
-- **Release Packages**: [GitHub Releases](https://github.com/Akkudoktor-EOS/EOS/releases)
-- **Docker Images**: [Docker Hub](https://hub.docker.com/r/akkudoktor/eos)
-- **Source Code**: [GitHub Repository](https://github.com/Akkudoktor-EOS/EOS)
+```{eval-rst}
+.. tabs::
+
+  .. tab:: Windows
+
+     .. code-block:: powershell
+
+        python -m venv .venv
+        .venv\Scripts\pip install -r requirements.txt
+        .venv\Scripts\pip install -e .
+
+  .. tab:: Linux
+
+     .. code-block:: bash
+
+        python -m venv .venv
+        .venv/bin/pip install -r requirements.txt
+        .venv/bin/pip install -e .
+
+```
+
+Start the EOS fastapi server:
+
+```{eval-rst}
+.. tabs::
+
+  .. tab:: Windows
+
+     .. code-block:: powershell
+
+        .venv\Scripts\python src/akkudoktoreos/server/eos.py
+
+  .. tab:: Linux
+
+     .. code-block:: bash
+
+        .venv/bin/python src/akkudoktoreos/server/eos.py
+
+```
+
+### Docker
+
+```{eval-rst}
+.. tabs::
+
+  .. tab:: Windows
+
+     .. code-block:: powershell
+
+        docker compose up --build
+
+  .. tab:: Linux
+
+     .. code-block:: bash
+
+        docker compose up --build
+
+```
 
 ## Configuration
 
-AkkudoktorEOS uses the `EOS.config.json` file to manage all configuration settings.
+This project uses the `EOS.config.json` file to manage configuration settings.
 
 ### Default Configuration
 
-If essential configuration settings are missing, the application automatically uses a default
-configuration to get you started quickly.
+A default configuration file `default.config.json` is provided. This file contains all the necessary configuration keys with their default values.
 
-### Custom Configuration Directory
+### Custom Configuration
 
-You can specify a custom location for your configuration by setting the `EOS_DIR` environment
-variable:
+Users can specify a custom configuration directory by setting the environment variable `EOS_DIR`.
 
-```bash
-export EOS_DIR=/path/to/your/config
-```
+- If the directory specified by `EOS_DIR` contains an existing `EOS.config.json` file, the application will use this configuration file.
+- If the `EOS.config.json` file does not exist in the specified directory, the `default.config.json` file will be copied to the directory as `EOS.config.json`.
 
-**How it works:**
+### Configuration Updates
 
-- **If `EOS.config.json` exists** in the `EOS_DIR` directory → the application uses this
-  configuration
-- **If `EOS.config.json` doesn't exist** → the application copies `default.config.json` to `EOS_DIR`
-  as `EOS.config.json`
+If the configuration keys in the `EOS.config.json` file are missing or different from those in `default.config.json`, they will be automatically updated to match the default settings, ensuring that all required keys are present.
 
-### Creating Your Configuration
+## Classes and Functionalities
 
-There are three ways to configure AkkudoktorEOS:
+This project uses various classes to simulate and optimize the components of an energy system. Each class represents a specific aspect of the system, as described below:
 
-1. **EOSdash (Recommended)** - The easiest method is to use the web-based dashboard at
-   [http://localhost:8504](http://localhost:8504)
+- `Battery`: Simulates a battery storage system, including capacity, state of charge, and now charge and discharge losses.
 
-2. **Manual editing** - Create or edit the `EOS.config.json` file directly in your preferred text
-   editor
+- `PVForecast`: Provides forecast data for photovoltaic generation, based on weather data and historical generation data.
 
-3. **Server API** - Programmatically change configuration through the [server API](#server-api-page)
+- `Load`: Models the load requirements of a household or business, enabling the prediction of future energy demand.
 
-For a complete reference of all available configuration options, see the [configuration guideline](#configuration-page).
+- `Heatpump`: Simulates a heat pump, including its energy consumption and efficiency under various operating conditions.
 
-## Quick Start Example
+- `Strompreis`: Provides information on electricity prices, enabling optimization of energy consumption and generation based on tariff information.
 
-```bash
-# Pull the latest docker image
-docker pull akkudoktor/eos:latest
+- `EMS`: The Energy Management System (EMS) coordinates the interaction between the various components, performs optimization, and simulates the operation of the entire energy system.
 
-# Run the application
-docker run -d \
-  --name akkudoktoreos \
-  -p 8503:8503 \
-  -p 8504:8504 \
-  -e OPENBLAS_NUM_THREADS=1 \
-  -e OMP_NUM_THREADS=1 \
-  -e MKL_NUM_THREADS=1 \
-  -e EOS_SERVER__HOST=0.0.0.0 \
-  -e EOS_SERVER__PORT=8503 \
-  -e EOS_SERVER__EOSDASH_HOST=0.0.0.0 \
-  -e EOS_SERVER__EOSDASH_PORT=8504 \
-  --ulimit nproc=65535:65535 \
-  --ulimit nofile=65535:65535 \
-  --security-opt seccomp=unconfined \
-  akkudoktor/eos:latest
+These classes work together to enable a detailed simulation and optimization of the energy system. For each class, specific parameters and settings can be adjusted to test different scenarios and strategies.
 
-# Access the dashboard
-open http://localhost:8504
-```
+### Customization and Extension
+
+Each class is designed to be easily customized and extended to integrate additional functions or improvements. For example, new methods can be added for more accurate modeling of PV system or battery behavior. Developers are invited to modify and extend the system according to their needs.

docs/develop/install.md

@@ -1,288 +0,0 @@
-% SPDX-License-Identifier: Apache-2.0
-(install-page)=
-
-# Installation Guide
-
-This guide provides different methods to install AkkudoktorEOS:
-
-- Installation from Source (GitHub)
-- Installation from Release Package (GitHub)
-- Installation with Docker (DockerHub)
-- Installation with Docker (docker-compose)
-
-Choose the method that best suits your needs.
-
-:::{admonition} Tip
-:class: Note
-If you need to update instead, see the [Update Guideline](update-page). For reverting to a previous
-release see the [Revert Guideline](revert-page).
-:::
-
-## Installation Prerequisites
-
-Before installing, ensure you have the following:
-
-### For Source / Release Installation
-
-- Python 3.10 or higher
-- pip
-- Git (only for source)
-- Tar/Zip (for release package)
-
-### For Docker Installation
-
-- Docker Engine 20.10 or higher
-- Docker Compose (optional, recommended)
-
-## Installation from Source (GitHub) (M1)
-
-Recommended for developers or users wanting the latest updates.
-
-### 1) Clone the Repository (M1)
-
-```{eval-rst}
-.. tabs::
-
-  .. tab:: Windows
-
-     .. code-block:: powershell
-
-        git clone https://github.com/Akkudoktor-EOS/EOS.git
-        cd EOS
-
-  .. tab:: Linux
-
-     .. code-block:: bash
-
-        git clone https://github.com/Akkudoktor-EOS/EOS.git
-        cd EOS
-```
-
-### 2) Create a Virtual Environment and install dependencies (M1)
-
-```{eval-rst}
-.. tabs::
-
-  .. tab:: Windows
-
-     .. code-block:: powershell
-
-        python -m venv .venv
-        .venv\Scripts\pip install -r requirements.txt
-        .venv\Scripts\pip install -e .
-
-  .. tab:: Linux
-
-     .. code-block:: bash
-
-        python -m venv .venv
-        .venv/bin/pip install -r requirements.txt
-        .venv/bin/pip install -e .
-
-```
-
-### 3) Run EOS (M1)
-
-```{eval-rst}
-.. tabs::
-
-  .. tab:: Windows
-
-     .. code-block:: powershell
-
-        .venv\Scripts\python -m akkudoktoreos.server.eos
-
-  .. tab:: Linux
-
-     .. code-block:: bash
-
-        .venv/bin/python -m akkudoktoreos.server.eos
-
-```
-
-EOS is now available at:
-
-- API: [http://localhost:8503/docs](http://localhost:8503/docs)
-- EOSdash: [http://localhost:8504](http://localhost:8504)
-
-If you want to make EOS and EOSdash accessible from outside of your machine or container at this
-stage of the installation provide appropriate IP addresses on startup.
-
-<!-- pyml disable line-length -->
-```{eval-rst}
-.. tabs::
-
-  .. tab:: Windows
-
-     .. code-block:: powershell
-
-        .venv\Scripts\python -m akkudoktoreos.server.eos --host 0.0.0.0 --eosdash-host 0.0.0.0
-
-  .. tab:: Linux
-
-     .. code-block:: bash
-
-        .venv/bin/python -m akkudoktoreos.server.eos --host 0.0.0.0 --eosdash-host 0.0.0.0
-
-```
-<!-- pyml enable line-length -->
-
-### 4) Configure EOS (M1)
-
-Use EOSdash at [http://localhost:8504](http://localhost:8504) to configure EOS.
-
-## Installation from Release Package (GitHub) (M2)
-
-This method is recommended for users who want a stable, tested version.
-
-### 1) Download the Latest Release (M2)
-
-Visit the [Releases page](https://github.com/Akkudoktor-EOS/EOS/tags) and download the latest
-release package (e.g., `akkudoktoreos-v0.2.0.tar.gz` or `akkudoktoreos-v0.2.0.zip`).
-
-### 2) Extract the Package (M2)
-
-```bash
-tar -xzf akkudoktoreos-v0.2.0.tar.gz  # For .tar.gz
-# or
-unzip akkudoktoreos-v0.2.0.zip  # For .zip
-
-cd akkudoktoreos-v0.2.0
-```
-
-### 3) Create a virtual environment and run and configure EOS (M2)
-
-Follow Step 2), 3) and 4) of method M1. Start at
-`2) Create a Virtual Environment and install dependencies`
-
-### 4) Update the source code (M2)
-
-To extract a new release to a new directory just proceed with method M2 step 1) for the new release.
-
-You may remove the old release directory afterwards.
-
-## Installation with Docker (DockerHub) (M3)
-
-This method is recommended for easy deployment and containerized environments.
-
-### 1) Pull the Docker Image (M3)
-
-```bash
-docker pull akkudoktor/eos:latest
-```
-
-For a specific version:
-
-```bash
-docker pull akkudoktor/eos:v<version>
-```
-
-### 2) Run the Container (M3)
-
-**Basic run:**
-
-```bash
-docker run -d \
-  --name akkudoktoreos \
-  -p 8503:8503 \
-  -p 8504:8504 \
-  -e OPENBLAS_NUM_THREADS=1 \
-  -e OMP_NUM_THREADS=1 \
-  -e MKL_NUM_THREADS=1 \
-  -e EOS_SERVER__HOST=0.0.0.0 \
-  -e EOS_SERVER__PORT=8503 \
-  -e EOS_SERVER__EOSDASH_HOST=0.0.0.0 \
-  -e EOS_SERVER__EOSDASH_PORT=8504 \
-  --ulimit nproc=65535:65535 \
-  --ulimit nofile=65535:65535 \
-  --security-opt seccomp=unconfined \
-  akkudoktor/eos:latest
-```
-
-### 3) Verify the Container is Running (M3)
-
-```bash
-docker ps
-docker logs akkudoktoreos
-```
-
-EOS should now be accessible at [http://localhost:8503/docs](http://localhost:8503/docs) and EOSdash
-should be available at [http://localhost:8504](http://localhost:8504).
-
-### 4) Configure EOS (M3)
-
-Use EOSdash at [http://localhost:8504](http://localhost:8504) to configure EOS.
-
-## Installation with Docker (docker-compose) (M4)
-
-### 1) Get the akkudoktoreos source code (M4)
-
-You may use either method M1 or method M2 to get the source code.
-
-### 2) Build and run the container (M4)
-
-```{eval-rst}
-.. tabs::
-
-  .. tab:: Windows
-
-     .. code-block:: powershell
-
-        docker compose up --build
-
-  .. tab:: Linux
-
-     .. code-block:: bash
-
-        docker compose up --build
-
-```
-
-### 3) Verify the Container is Running (M4)
-
-```bash
-docker ps
-docker logs akkudoktoreos
-```
-
-EOS should now be accessible at [http://localhost:8503/docs](http://localhost:8503/docs) and EOSdash
-should be available at [http://localhost:8504](http://localhost:8504).
-
-### 4) Configure EOS
-
-Use EOSdash at [http://localhost:8504](http://localhost:8504) to configure EOS.
-
-## Helpful Docker Commands
-
-**View logs:**
-
-```bash
-docker logs -f akkudoktoreos
-```
-
-**Stop the container:**
-
-```bash
-docker stop akkudoktoreos
-```
-
-**Start the container:**
-
-```bash
-docker start akkudoktoreos
-```
-
-**Remove the container:**
-
-```bash
-docker rm -f akkudoktoreos
-```
-
-**Update to latest version:**
-
-```bash
-docker pull Akkudoktor-EOS/EOS:latest
-docker stop akkudoktoreos
-docker rm akkudoktoreos
-# Then run the container again with the run command
-```

docs/develop/release.md

@@ -1,212 +0,0 @@
-% SPDX-License-Identifier: Apache-2.0
-(release-page)=
-
-# Release Process
-
-This document describes how to prepare and publish a new release **via a Pull Request from a fork**,
-and how to set a **development version** after the release.
-
-## ✅ Overview of the Process
-
-| Step | Actor       | Action |
-|------|-------------|--------|
-| 1    | Contributor | Prepare a release branch **in your fork** using Commitizen |
-| 2    | Contributor | Open a **Pull Request to upstream** (`Akkudoktor-EOS/EOS`) |
-| 3    | Maintainer  | Review and **merge the release PR** |
-| 4    | Maintainer  | Create the **GitHub Release and tag** |
-| 5    | Maintainer  | Set the **development version marker** via a follow-up PR |
-
-## 🔄 Detailed Workflow
-
-### 1️⃣ Contributor: Prepare the Release in Your Fork
-
-#### Clone and sync your fork
-
-```bash
-git clone https://github.com/<your-username>/EOS
-cd EOS
-git remote add eos https://github.com/Akkudoktor-EOS/EOS
-
-git fetch eos
-git checkout main
-git pull eos main
-````
-
-#### Create the release branch
-
-```bash
-git checkout -b release/vX.Y.Z
-```
-
-#### Bump the version information
-
-At least update
-
-- pyproject.toml
-- src/akkudoktoreos/core/version.py
-- src/akkudoktoreos/data/default.config.json
-- Makefile
-
-and the generated documentation:
-
-```bash
-make bump VERSION=0.1.0+dev NEW_VERSION=X.Y.Z
-make gen-docs
-```
-
-You may check the changes by:
-
-```bash
-git diff
-```
-
-#### Create a new CHANGELOG.md entry
-
-Edit CHANGELOG.md
-
-#### Create the new release commit
-
-```bash
-git add pyproject.toml src/akkudoktoreos/core/version.py \
-    src/akkudoktoreos/data/default.config.json Makefile CHANGELOG.md
-git commit -s -m "chore(release): Release vX.Y.Z"
-```
-
-#### Push the branch to your fork
-
-```bash
-git push --set-upstream origin release/vX.Y.Z
-```
-
-### 2️⃣ Contributor: Open the Release Pull Request
-
-| From                                 | To                        |
-| ------------------------------------ | ------------------------- |
-| `<your-username>/EOS:release/vX.Y.Z` | `Akkudoktor-EOS/EOS:main` |
-
-**PR Title:**
-
-```text
-chore(release): release vX.Y.Z
-```
-
-**PR Description Template:**
-
-```markdown
-## Release vX.Y.Z
-
-This pull request prepares release **vX.Y.Z**.
-
-### Changes
-- Version bump
-- Changelog update
-
-### Changelog Summary
-<!-- Copy key highlights from CHANGELOG.md here -->
-
-See `CHANGELOG.md` for full details.
-```
-
-### 3️⃣ Maintainer: Review and Merge the Release PR
-
-**Review Checklist:**
-
-- ✅ Only version files and `CHANGELOG.md` are modified
-- ✅ Version numbers are consistent
-- ✅ Changelog is complete and properly formatted
-- ✅ No unrelated changes are included
-
-**Merge Strategy:**
-
-- Prefer **Merge Commit** (or **Squash Merge**, per project preference)
-- Use commit message: `chore(release): Release vX.Y.Z`
-
-### 4️⃣ Maintainer: Publish the GitHub Release
-
-1. Go to **GitHub → Releases → Draft a new release**
-2. **Choose tag** → enter `vX.Y.Z` (GitHub creates the tag on publish)
-3. **Release title:** `vX.Y.Z`
-4. **Paste changelog entry** from `CHANGELOG.md`
-5. Optionally enable **Set as latest release**
-6. Click **Publish release** 🎉
-
-### 5️⃣ Maintainer: Prepare the Development Version Marker
-
-**Sync local copy:**
-
-```bash
-git fetch eos
-git checkout main
-git pull eos main
-```
-
-**Create a development version branch:**
-
-```bash
-git checkout -b release/vX.Y.Z_dev
-```
-
-**Set development version marker manually:**
-
-```bash
-make bump VERSION=X.Y.Z NEW_VERSION=X.Y.Z+dev
-make gen-docs
-```
-
-```bash
-git add pyproject.toml src/akkudoktoreos/core/version.py \
-    src/akkudoktoreos/data/default.config.json Makefile
-git commit -s -m "chore: set development version marker X.Y.Z+dev"
-```
-
-```bash
-git push --set-upstream origin release/vX.Y.Z_dev
-```
-
-### 6️⃣ Maintainer (or Contributor): Open the Development Version PR
-
-| From                                     | To                        |
-| ---------------------------------------- | ------------------------- |
-| `<your-username>/EOS:release/vX.Y.Z_dev` | `Akkudoktor-EOS/EOS:main` |
-
-**PR Title:**
-
-```text
-chore: development version vX.Y.Z+dev
-```
-
-**PR Description Template:**
-
-```markdown
-## Development version vX.Y.Z+dev
-
-This pull request marks the repository as back in active development.
-
-### Changes
-- Set version to `vX.Y.Z+dev`
-
-No changelog entry is needed.
-```
-
-### 7️⃣ Maintainer: Review and Merge the Development Version PR
-
-**Checklist:**
-
-- ✅ Only version files updated to `+dev`
-- ✅ No unintended changes
-
-**Merge Strategy:**
-
-- Merge with commit message: `chore: development version vX.Y.Z+dev`
-
-## ✅ Quick Reference
-
-| Step | Actor | Action |
-| ---- | ----- | ------ |
-| **1. Prepare release branch** | Contributor | Bump version & changelog via Commitizen |
-| **2. Open release PR** | Contributor | Submit release for review |
-| **3. Review & merge release PR** | Maintainer | Finalize changes into `main` |
-| **4. Publish GitHub Release** | Maintainer | Create tag & notify users |
-| **5. Prepare development version branch** | Maintainer | Set development marker |
-| **6. Open development PR** | Maintainer (or Contributor) | Propose returning to development state |
-| **7. Review & merge development PR** | Maintainer | Mark repository as back in development |

docs/develop/revert.md

@@ -1,155 +0,0 @@
-% SPDX-License-Identifier: Apache-2.0
-(revert-page)=
-
-# Revert Guide
-
-This guide explains how to **revert AkkudoktorEOS to a previous version**.
-The exact methods and steps differ depending on how EOS was installed:
-
-- M1/M2: Reverting when Installed from Source or Release Package
-- M3/M4: Reverting when Installed via Docker
-
-:::{admonition} Important
-:class: warning
-Before reverting, ensure you have a backup of your `EOS.config.json`.
-EOS also maintains internal configuration backups that can be restored after a downgrade.
-:::
-
-:::{admonition} Tip
-:class: Note
-If you need to update instead, see the [Update Guideline](update-page).
-:::
-
-## Revert to a Previous Version of EOS
-
-You can revert to a previous version using the same installation method you originally selected.
-See: [Installation Guideline](install-page)
-
-## Reverting when Installed from Source or Release Package (M1/M2)
-
-### 1) Locate the target version (M2)
-
-Go to the GitHub Releases page:
-
-> <https://github.com/Akkudoktor-EOS/EOS/tags>
-
-### 2) Download or check out that version (M1/M2)
-
-#### Git (source) (M1)
-
-```bash
-git fetch
-git checkout v<version>
-````
-
-Example:
-
-```bash
-git checkout v0.1.0
-```
-
-Then reinstall dependencies:
-
-```bash
-.venv/bin/pip install -r requirements.txt --upgrade
-```
-
-#### Release package (M2)
-
-Download and extract the desired ZIP or TAR release.
-Refer to **Method 2** in the [Installation Guideline](install-page).
-
-### 3) Restart EOS (M1/M2)
-
-```bash
-.venv/bin/python -m akkudoktoreos.server.eos
-```
-
-### 4) Restore configuration (optional) (M1/M2)
-
-If your configuration changed since the downgrade, you may restore a previous backup:
-
-- via **EOSdash**
-
-    Admin → configuration → Revert to backup
-
-    or
-
-    Admin → configuration → Import from file
-
-- via **REST**
-
-    ```bash
-    curl -X PUT "http://<host>:8503/v1/config/revert?backup_id=<backup>"
-    ```
-
-## Reverting when Installed via Docker (M3/M4)
-
-### 1) Pull the desired image version (M3/M4)
-
-```bash
-docker pull akkudoktor/eos:v<version>
-```
-
-Example:
-
-```bash
-docker pull akkudoktor/eos:v0.1.0
-```
-
-### 2) Stop and remove the current container (M3/M4)
-
-```bash
-docker stop akkudoktoreos
-docker rm akkudoktoreos
-```
-
-### 3) Start a container with the selected version (M3/M4)
-
-Start EOS as usual, using your existing `docker run` or `docker compose` setup
-(see Method 3 or Method 4 in the [Installation Guideline](install-page)).
-
-### 4) Restore configuration (optional) (M3/M4)
-
-In many cases configuration will migrate automatically.
-If needed, you may restore a configuration backup:
-
-- via **EOSdash**
-
-    Admin → configuration → Revert to backup
-
-    or
-
-    Admin → configuration → Import from file
-
-- via **REST**
-
-    ```bash
-    curl -X PUT "http://<host>:8503/v1/config/revert?backup_id=<backup>"
-    ```
-
-## About Configuration Backups
-
-EOS keeps configuration backup files next to your active `EOS.config.json`.
-
-You can list and restore backups:
-
-- via **EOSdash UI**
-- via **REST API**
-
-### List available backups
-
-```bash
-GET /v1/config/backups
-```
-
-### Restore backup
-
-```bash
-PUT /v1/config/revert?backup_id=<id>
-```
-
-:::{admonition} Important
-:class: warning
-If no backup file is available, create or copy a previously saved `EOS.config.json` before reverting.
-:::

docs/index.md

@@ -8,61 +8,23 @@
 
 ```{toctree}
 :maxdepth: 2
-:caption: Overview
-
-akkudoktoreos/introduction.md
-
-```
-
-```{toctree}
-:maxdepth: 2
-:caption: Tutorials
+:caption: 'Contents:'
 
+welcome.md
+akkudoktoreos/about.md
 develop/getting_started.md
-
-```
-
-```{toctree}
-:maxdepth: 2
-:caption: How-To Guides
-
 develop/CONTRIBUTING.md
-develop/install.md
-develop/update.md
-develop/revert.md
-
-```
-
-```{toctree}
-:maxdepth: 2
-:caption: Reference
-
 akkudoktoreos/architecture.md
 akkudoktoreos/configuration.md
-akkudoktoreos/configtimewindow.md
-akkudoktoreos/optimpost.md
-akkudoktoreos/optimauto.md
-akkudoktoreos/resource.md
+akkudoktoreos/optimization.md
 akkudoktoreos/prediction.md
 akkudoktoreos/measurement.md
 akkudoktoreos/integration.md
-akkudoktoreos/logging.md
 akkudoktoreos/serverapi.md
 akkudoktoreos/api.rst
-
 ```
 
-```{toctree}
-:maxdepth: 2
-:caption: Development
-
-develop/develop.md
-develop/release.md
-develop/CHANGELOG.md
-
-```
-
-## Indices and tables
+# Indices and tables
 
 - {ref}`genindex`
 - {ref}`modindex`

docs/pymarkdown.json

@@ -1,20 +0,0 @@
-{
-    "plugins": {
-        "md007": {
-            "enabled": true,
-            "code_block_line_length" : 160
-        },
-        "md013": {
-            "enabled": true,
-            "line_length" : 120
-        },
-        "md041": {
-            "enabled": false
-        }
-    },
-    "extensions": {
-        "front-matter" : {
-            "enabled" : true
-        }
-    }
-}

docs/welcome.md

@@ -1,12 +1,12 @@
 % SPDX-License-Identifier: Apache-2.0
 
-# Welcome to the EOS documentation
+# Welcome to the EOS documentation!
 
 This documentation is continuously written. It is edited via text files in the
 [Markdown/ Markedly Structured Text](https://myst-parser.readthedocs.io/en/latest/index.html)
 markup language and then compiled into a static website/ offline document using the open source tool
-[Sphinx](https://www.sphinx-doc.org) and is available on
-[Read the Docs](https://akkudoktor-eos.readthedocs.io/en/latest/).
+[Sphinx](https://www.sphinx-doc.org) and will someday land on
+[Read the Docs](https://akkudoktoreos.readthedocs.io/en/latest/index.html).
 
 You can contribute to EOS's documentation by opening
 [GitHub issues](https://github.com/Akkudoktor-EOS/EOS/issues)

pyproject.toml

@@ -1,13 +1,13 @@
 [project]
 name = "akkudoktor-eos"
-version = "0.2.0"
+version = "0.0.1"
 authors = [
   { name="Andreas Schmitz", email="author@example.com" },
 ]
 description = "This project provides a comprehensive solution for simulating and optimizing an energy system based on renewable energy sources. With a focus on photovoltaic (PV) systems, battery storage (batteries), load management (consumer requirements), heat pumps, electric vehicles, and consideration of electricity price data, this system enables forecasting and optimization of energy flow and costs over a specified period."
 readme = "README.md"
 license = {file = "LICENSE"}
-requires-python = ">=3.11"
+requires-python = ">=3.10"
 classifiers = [
     "Development Status :: 3 - Alpha",
     "Programming Language :: Python :: 3",
@@ -43,18 +43,12 @@ profile = "black"
 
 [tool.ruff]
 line-length = 100
-exclude = [
-    "tests",
-    "scripts",
-]
-output-format = "full"
 
 [tool.ruff.lint]
 select = [
     "F",      # Enable all `Pyflakes` rules.
     "D",      # Enable all `pydocstyle` rules, limiting to those that adhere to the
               # Google convention via `convention = "google"`, below.
-    "S",      # Enable all `flake8-bandit` rules.
 ]
 ignore = [
    # Prevent errors due to ruff false positives
@@ -107,31 +101,3 @@ ignore_missing_imports = true
 [[tool.mypy.overrides]]
 module = "xprocess.*"
 ignore_missing_imports = true
-
-[tool.commitizen]
-name = "cz_conventional_commits"
-version_scheme = "semver"
-version = "0.2.0"            # <-- Set your current version heretag_format = "v$version"
-
-# Files to automatically update when bumping version
-update_changelog_on_bump = true
-changelog_incremental = true
-annotated_tag = true
-bump_message = "chore(release): $current_version → $new_version"
-
-# Branch validation settings
-branch_validation = true
-branch_pattern = "^(feat|fix|chore|docs|refactor|test)/[a-z0-9._-]+$"
-
-# Customize changelog generation
-[tool.commitizen.changelog]
-path = "CHANGELOG.md"
-template = "keepachangelog"
-
-# If your version is stored in multiple files (Python modules, docs etc.), add them here
-[tool.commitizen.files]
-version = [
-  "pyproject.toml",          # Auto-update project version
-  "src/akkudoktoreos/core/version.py",
-  "src/akkudoktoreos/data/default.config.json"
-]

requirements-dev.txt

@@ -1,29 +1,13 @@
 -r requirements.txt
-
-# Pre-commit framework - basic package requirements handled by pre-commit itself
-# - pre-commit-hooks
-# - isort
-# - ruff
-# - mypy (mirrors-mypy) - sync with requirements-dev.txt (if on pypi)
-# - pymarkdown
-# - commitizen - sync with requirements-dev.txt (if on pypi)
-pre-commit==4.3.0
-mypy==1.18.2
-types-requests==2.32.4.20250913 # for mypy
-pandas-stubs==2.3.2.250926 # for mypy
-tokenize-rt==6.2.0 # for mypy
-commitizen==4.9.1
-deprecated==1.3.1 # for commitizen
-
-# Sphinx
-sphinx==8.2.3
+gitpython==3.1.44
+myst-parser==4.0.0
+sphinx==8.1.3
 sphinx_rtd_theme==3.0.2
 sphinx-tabs==3.4.7
-GitPython==3.1.45
-myst-parser==4.0.1
-
-# Pytest
-pytest==8.4.2
-pytest-cov==7.0.0
-coverage==7.11.1
+pytest==8.3.4
+pytest-cov==6.0.0
 pytest-xprocess==1.0.2
+pre-commit
+mypy==1.13.0
+types-requests==2.32.0.20241016
+pandas-stubs==2.2.3.241126

requirements.txt

@@ -1,31 +1,24 @@
-babel==2.17.0
-beautifulsoup4==4.14.2
-cachebox==5.1.0
-numpy==2.3.4
-numpydantic==1.7.0
-matplotlib==3.10.7
-contourpy==1.3.3
-fastapi[standard-no-fastapi-cloud-cli]==0.121.0
-fastapi_cli==0.0.14
-rich-toolkit==0.15.1
-python-fasthtml==0.12.33
-MonsterUI==1.0.32
+cachebox==4.4.2
+numpy==2.2.2
+numpydantic==1.6.7
+matplotlib==3.10.0
+fastapi[standard]==0.115.7
+python-fasthtml==0.12.0
+MonsterUI==0.0.29
 markdown-it-py==3.0.0
-mdit-py-plugins==0.5.0
-bokeh==3.8.1
-uvicorn==0.38.0
-scikit-learn==1.7.2
-tzfpy==1.1.0
-deap==1.4.3
-requests==2.32.5
-pandas==2.3.3
-pendulum==3.1.0
-platformdirs==4.5.0
-psutil==7.1.3
-pvlib==0.13.1
-pydantic==2.12.4
-pydantic_extra_types==2.10.6
-statsmodels==0.14.5
-pydantic-settings==2.11.0
+mdit-py-plugins==0.4.2
+bokeh==3.6.3
+uvicorn==0.34.0
+scikit-learn==1.6.1
+timezonefinder==6.5.8
+deap==1.4.2
+requests==2.32.3
+pandas==2.2.3
+pendulum==3.0.0
+platformdirs==4.3.6
+psutil==6.1.1
+pvlib==0.11.2
+pydantic==2.10.6
+statsmodels==0.14.4
+pydantic-settings==2.7.0
 linkify-it-py==2.0.3
-loguru==0.7.3

scripts/bump_version.py

@@ -1,170 +0,0 @@
-"""Update version strings in multiple project files only if the old version matches.
-
-This script updates version information in:
-- pyproject.toml
-- src/akkudoktoreos/core/version.py
-- src/akkudoktoreos/data/default.config.json
-- Makefile
-
-Supported version formats:
-- __version__ = "<version>"
-- version = "<version>"
-- "version": "<version>"
-- VERSION ?: <version>
-
-It will:
-- Replace VERSION → NEW_VERSION if the old version is found.
-- Report which files were updated.
-- Report which files contained mismatched versions.
-- Report which files had no version.
-
-Usage:
-    python bump_version.py VERSION NEW_VERSION
-
-Args:
-    VERSION (str): Version expected before replacement.
-    NEW_VERSION (str): Version to write.
-
-"""
-#!/usr/bin/env python3
-import argparse
-import glob
-import os
-import re
-import shutil
-from pathlib import Path
-from typing import List, Tuple
-
-# Patterns to match version strings
-VERSION_PATTERNS = [
-    re.compile(r'(__version__\s*=\s*")(?P<ver>[^"]+)(")'),
-    re.compile(r'(version\s*=\s*")(?P<ver>[^"]+)(")'),
-    re.compile(r'("version"\s*:\s*")(?P<ver>[^"]+)(")'),
-    re.compile(r'(VERSION\s*\?=\s*)(?P<ver>[^\s]+)'),  # For Makefile: VERSION ?= 0.2.0
-]
-
-# Default files to process
-DEFAULT_FILES = [
-    "pyproject.toml",
-    "src/akkudoktoreos/core/version.py",
-    "src/akkudoktoreos/data/default.config.json",
-    "Makefile",
-]
-
-
-def backup_file(file_path: str) -> str:
-    """Create a backup of the given file with a .bak suffix.
-
-    Args:
-        file_path: Path to the file to backup.
-
-    Returns:
-        Path to the backup file.
-    """
-    backup_path = f"{file_path}.bak"
-    shutil.copy2(file_path, backup_path)
-    return backup_path
-
-
-def replace_version_in_file(
-    file_path: Path, old_version: str, new_version: str, dry_run: bool = False
-) -> Tuple[bool, bool]:
-    """
-    Replace old_version with new_version in the given file if it matches.
-
-    Args:
-        file_path: Path to the file to modify.
-        old_version: The old version to replace.
-        new_version: The new version to set.
-        dry_run: If True, don't actually modify files.
-
-    Returns:
-        Tuple[bool, bool]: (file_would_be_updated, old_version_found)
-    """
-    content = file_path.read_text()
-    new_content = content
-    old_version_found = False
-    file_would_be_updated = False
-
-    for pattern in VERSION_PATTERNS:
-        def repl(match):
-            nonlocal old_version_found, file_would_be_updated
-            ver = match.group("ver")
-            if ver == old_version:
-                old_version_found = True
-                file_would_be_updated = True
-                # Some patterns have 3 groups (like quotes)
-                if len(match.groups()) == 3:
-                    return f"{match.group(1)}{new_version}{match.group(3)}"
-                else:
-                    return f"{match.group(1)}{new_version}"
-            return match.group(0)
-
-        new_content = pattern.sub(repl, new_content)
-
-    if file_would_be_updated:
-        if dry_run:
-            print(f"[DRY-RUN] Would update {file_path}")
-        else:
-            backup_path = file_path.with_suffix(file_path.suffix + ".bak")
-            shutil.copy(file_path, backup_path)
-            file_path.write_text(new_content)
-            print(f"Updated {file_path} (backup saved to {backup_path})")
-    elif not old_version_found:
-        print(f"[SKIP] {file_path}: old version '{old_version}' not found")
-
-    return file_would_be_updated, old_version_found
-
-
-def main():
-    parser = argparse.ArgumentParser(description="Bump version across project files.")
-    parser.add_argument("old_version", help="Old version to replace")
-    parser.add_argument("new_version", help="New version to set")
-    parser.add_argument(
-        "--dry-run", action="store_true", help="Show what would be changed without modifying files"
-    )
-    parser.add_argument(
-        "--glob", nargs="*", help="Optional glob patterns to include additional files"
-    )
-    args = parser.parse_args()
-
-    updated_files = []
-    not_found_files = []
-
-    # Determine files to update
-    files_to_update: List[Path] = [Path(f) for f in DEFAULT_FILES]
-    if args.glob:
-        for pattern in args.glob:
-            files_to_update.extend(Path(".").glob(pattern))
-
-    files_to_update = list(dict.fromkeys(files_to_update))  # remove duplicates
-
-    any_updated = False
-    for file_path in files_to_update:
-        if file_path.exists() and file_path.is_file():
-            updated, _ = replace_version_in_file(
-                file_path, args.old_version, args.new_version, args.dry_run
-            )
-            any_updated |= updated
-            if updated:
-                updated_files.append(file_path)
-        else:
-            print(f"[SKIP] {file_path}: file does not exist")
-            not_found_files.append(file_path)
-
-    print("\nSummary:")
-    if updated_files:
-        print(f"Updated files ({len(updated_files)}):")
-        for f in updated_files:
-            print(f"  {f}")
-    else:
-        print("No files were updated.")
-
-    if not_found_files:
-        print(f"Files where old version was not found ({len(not_found_files)}):")
-        for f in not_found_files:
-            print(f"  {f}")
-
-
-if __name__ == "__main__":
-    main()

scripts/convert_lightweight_tags.py

@@ -1,69 +0,0 @@
-#!/usr/bin/env python3
-
-import subprocess
-import sys
-
-MESSAGE_PREFIX = "Converted to annotated tag:"
-
-def run(cmd, capture_output=False):
-    """Run a shell command and return output if needed."""
-    result = subprocess.run(cmd, shell=True, check=True, text=True, capture_output=capture_output)
-    return result.stdout.strip() if capture_output else None
-
-def get_all_tags():
-    """Return a list of all tags."""
-    return run("git tag", capture_output=True).splitlines()
-
-def is_lightweight(tag):
-    """Return True if a tag is lightweight (points to commit, not tag object)."""
-    return run(f"git cat-file -t {tag}", capture_output=True) == "commit"
-
-def get_commit_of_tag(tag):
-    """Return the commit SHA a tag points to."""
-    return run(f"git rev-list -n 1 {tag}", capture_output=True)
-
-def convert_tag(tag):
-    """Delete and recreate a tag as annotated."""
-    commit = get_commit_of_tag(tag)
-    print(f"Converting {tag} -> annotated ({commit})")
-    run(f"git tag -d {tag}")
-    run(f'git tag -a {tag} -m "{MESSAGE_PREFIX} {tag}" {commit}')
-
-def main():
-    dry_run = "--dry-run" in sys.argv
-    push = "--push" in sys.argv
-
-    tags = get_all_tags()
-    lightweight_tags = [t for t in tags if is_lightweight(t)]
-
-    if not lightweight_tags:
-        print("✅ No lightweight tags found.")
-        return
-
-    print("🔍 Lightweight tags found:\n  " + "\n  ".join(lightweight_tags))
-
-    if dry_run:
-        print("\n📝 Dry run: No changes will be made.")
-        return
-
-    confirm = input("\n⚠️ Convert ALL of these tags to annotated? (y/N): ").lower()
-    if confirm != "y":
-        print("❌ Aborted.")
-        return
-
-    for tag in lightweight_tags:
-        convert_tag(tag)
-
-    print("\n✅ Conversion complete.")
-
-    if push:
-        print("📤 Pushing updated tags to origin (force)...")
-        run("git push origin --tags --force")
-        print("✅ Tags pushed.")
-    else:
-        print("\n🚀 To push changes, run:\n  git push origin --tags --force")
-
-if __name__ == "__main__":
-    print("=== Lightweight Tag Converter ===")
-    print("Usage: python convert_lightweight_tags.py [--dry-run] [--push]\n")
-    main()

scripts/cz_check_branch.py

@@ -1,47 +0,0 @@
-#!/usr/bin/env python3
-"""Branch name checker using regex (compatible with Commitizen v4.9.1).
-
-Cross-platform + .venv aware.
-"""
-
-import os
-import re
-import subprocess
-import sys
-from pathlib import Path
-
-
-def find_cz() -> str:
-    venv = os.getenv("VIRTUAL_ENV")
-    paths = [Path(venv)] if venv else []
-    paths.append(Path.cwd() / ".venv")
-
-    for base in paths:
-        cz = base / ("Scripts" if os.name == "nt" else "bin") / ("cz.exe" if os.name == "nt" else "cz")
-        if cz.exists():
-            return str(cz)
-    return "cz"
-
-
-def main():
-    # Get current branch name
-    try:
-        branch = subprocess.check_output(["git", "rev-parse", "--abbrev-ref", "HEAD"], text=True).strip()
-    except subprocess.CalledProcessError:
-        print("❌ Could not determine current branch name.")
-        return 1
-
-    # Regex pattern
-    pattern = r"^(feat|fix|chore|docs|refactor|test)/[a-z0-9._-]+$"
-
-    print(f"🔍 Checking branch name '{branch}'...")
-    if not re.match(pattern, branch):
-        print(f"❌ Branch name '{branch}' does not match pattern '{pattern}'")
-        return 1
-
-    print("✅ Branch name is valid.")
-    return 0
-
-
-if __name__ == "__main__":
-    sys.exit(main())

scripts/cz_check_commit_message.py

@@ -1,63 +0,0 @@
-#!/usr/bin/env python3
-"""Commitizen commit message checker that is .venv aware.
-
-Works for commits with -m or commit message file.
-"""
-
-import os
-import subprocess
-import sys
-from pathlib import Path
-
-
-def find_cz() -> str:
-    """Find Commitizen executable, preferring virtualenv."""
-    venv = os.getenv("VIRTUAL_ENV")
-    paths = []
-    if venv:
-        paths.append(Path(venv))
-    paths.append(Path.cwd() / ".venv")
-
-    for base in paths:
-        cz = base / ("Scripts" if os.name == "nt" else "bin") / ("cz.exe" if os.name == "nt" else "cz")
-        if cz.exists():
-            return str(cz)
-    return "cz"
-
-
-def main():
-    cz = find_cz()
-
-    # 1️⃣ Try commit-msg file (interactive commit)
-    commit_msg_file = sys.argv[1] if len(sys.argv) > 1 else None
-
-    # 2️⃣ If not file, fallback to -m message (Git sets GIT_COMMIT_MSG in some environments, or we create a temp file)
-    if not commit_msg_file:
-        msg = os.getenv("GIT_COMMIT_MSG") or ""
-        if not msg:
-            print("⚠️  No commit message file or environment message found. Skipping Commitizen check.")
-            return 0
-        import tempfile
-
-        with tempfile.NamedTemporaryFile("w+", delete=False) as tmp:
-            tmp.write(msg)
-            tmp.flush()
-            commit_msg_file = tmp.name
-
-    print(f"🔍 Checking commit message using {cz}...")
-
-    try:
-        subprocess.check_call([cz, "check", "--commit-msg-file", commit_msg_file])
-        print("✅ Commit message follows Commitizen convention.")
-        return 0
-    except subprocess.CalledProcessError:
-        print("❌ Commit message validation failed.")
-        return 1
-    finally:
-        # Clean up temp file if we created one
-        if 'tmp' in locals():
-            os.unlink(tmp.name)
-
-
-if __name__ == "__main__":
-    sys.exit(main())

scripts/cz_check_new_commits.py

@@ -1,70 +0,0 @@
-#!/usr/bin/env python3
-"""Pre-push hook: Commitizen check for *new commits only*.
-
-Cross-platform + virtualenv-aware:
-- Prefers activated virtual environment (VIRTUAL_ENV)
-- Falls back to ./.venv if found
-- Falls back to global cz otherwise
-"""
-
-import os
-import subprocess
-import sys
-from pathlib import Path
-
-
-def find_cz_executable() -> str:
-    """Return path to Commitizen executable, preferring virtual environments."""
-    # 1️⃣ Active virtual environment (if running inside one)
-    venv_env = os.getenv("VIRTUAL_ENV")
-    if venv_env:
-        cz_path = Path(venv_env) / ("Scripts" if os.name == "nt" else "bin") / ("cz.exe" if os.name == "nt" else "cz")
-        if cz_path.exists():
-            return str(cz_path)
-
-    # 2️⃣ Local .venv in repo root
-    repo_venv = Path.cwd() / ".venv"
-    cz_path = repo_venv / ("Scripts" if os.name == "nt" else "bin") / ("cz.exe" if os.name == "nt" else "cz")
-    if cz_path.exists():
-        return str(cz_path)
-
-    # 3️⃣ Global fallback
-    return "cz"
-
-
-def get_merge_base() -> str | None:
-    """Return merge-base between HEAD and upstream branch, or None if unavailable."""
-    try:
-        return (
-            subprocess.check_output(
-                ["git", "merge-base", "@{u}", "HEAD"],
-                stderr=subprocess.DEVNULL,
-                text=True,
-            )
-            .strip()
-        )
-    except subprocess.CalledProcessError:
-        return None
-
-
-def main() -> int:
-    cz = find_cz_executable()
-    base = get_merge_base()
-
-    if not base:
-        print("⚠️  No upstream found; skipping Commitizen check for new commits.")
-        return 0
-
-    print(f"🔍 Using {cz} to check new commits from {base}..HEAD ...")
-
-    try:
-        subprocess.check_call([cz, "check", "--rev-range", f"{base}..HEAD"])
-        print("✅ All new commits follow Commitizen conventions.")
-        return 0
-    except subprocess.CalledProcessError as e:
-        print("❌ Commitizen check failed for one or more new commits.")
-        return e.returncode
-
-
-if __name__ == "__main__":
-    sys.exit(main())

scripts/generate_config_md.py

@@ -4,19 +4,21 @@
 import argparse
 import json
 import os
-import re
 import sys
 import textwrap
 from pathlib import Path
-from typing import Any, Type, Union
+from typing import Any, Union
 
-from loguru import logger
 from pydantic.fields import ComputedFieldInfo, FieldInfo
 from pydantic_core import PydanticUndefined
 
 from akkudoktoreos.config.config import ConfigEOS, GeneralSettings, get_config
+from akkudoktoreos.core.logging import get_logger
 from akkudoktoreos.core.pydantic import PydanticBaseModel
-from akkudoktoreos.utils.datetimeutil import to_datetime
+from akkudoktoreos.utils.docs import get_model_structure_from_examples
+
+logger = get_logger(__name__)
+
 
 documented_types: set[PydanticBaseModel] = set()
 undocumented_types: dict[PydanticBaseModel, tuple[str, list[str]]] = dict()
@@ -52,45 +54,6 @@ def resolve_nested_types(field_type: Any, parent_types: list[str]) -> list[tuple
     return resolved_types
 
 
-def get_example_or_default(field_name: str, field_info: FieldInfo, example_ix: int) -> Any:
-    """Generate a default value for a field, considering constraints."""
-    if field_info.examples is not None:
-        try:
-            return field_info.examples[example_ix]
-        except IndexError:
-            return field_info.examples[-1]
-
-    if field_info.default is not None:
-        return field_info.default
-
-    raise NotImplementedError(f"No default or example provided '{field_name}': {field_info}")
-
-
-def get_model_structure_from_examples(
-    model_class: type[PydanticBaseModel], multiple: bool
-) -> list[dict[str, Any]]:
-    """Create a model instance with default or example values, respecting constraints."""
-    example_max_length = 1
-
-    # Get first field with examples (non-default) to get example_max_length
-    if multiple:
-        for _, field_info in model_class.model_fields.items():
-            if field_info.examples is not None:
-                example_max_length = len(field_info.examples)
-                break
-
-    example_data: list[dict[str, Any]] = [{} for _ in range(example_max_length)]
-
-    for field_name, field_info in model_class.model_fields.items():
-        if field_info.deprecated:
-            continue
-        for example_ix in range(example_max_length):
-            example_data[example_ix][field_name] = get_example_or_default(
-                field_name, field_info, example_ix
-            )
-    return example_data
-
-
 def create_model_from_examples(
     model_class: PydanticBaseModel, multiple: bool
 ) -> list[PydanticBaseModel]:
@@ -182,7 +145,6 @@ def generate_config_table_md(
         field_type = field_info.annotation if regular_field else field_info.return_type
         default_value = get_default_value(field_info, regular_field)
         description = field_info.description if field_info.description else "-"
-        deprecated = field_info.deprecated if field_info.deprecated else None
         read_only = "rw" if regular_field else "ro"
         type_name = get_type_name(field_type)
 
@@ -192,34 +154,25 @@ def generate_config_table_md(
                 env_entry = f"| `{prefix}{config_name}` "
             else:
                 env_entry = "| "
-        if deprecated:
-            if isinstance(deprecated, bool):
-                description = "Deprecated!"
-            else:
-                description = deprecated
         table += f"| {field_name} {env_entry}| `{type_name}` | `{read_only}` | `{default_value}` | {description} |\n"
 
-        # inner_types: dict[type[PydanticBaseModel], tuple[str, list[str]]] = dict()
-        inner_types: dict[Any, tuple[str, list[str]]] = dict()
+        inner_types: dict[PydanticBaseModel, tuple[str, list[str]]] = dict()
 
         def extract_nested_models(subtype: Any, subprefix: str, parent_types: list[str]):
-            """Extract nested models."""
             if subtype in inner_types.keys():
                 return
             nested_types = resolve_nested_types(subtype, [])
             for nested_type, nested_parent_types in nested_types:
-                # Nested type may be of type class, enum, typing.Any
-                if isinstance(nested_type, type) and issubclass(nested_type, PydanticBaseModel):
-                    # Nested type is a subclass of PydanticBaseModel
+                if issubclass(nested_type, PydanticBaseModel):
                     new_parent_types = parent_types + nested_parent_types
                     if "list" in parent_types:
                         new_prefix = ""
                     else:
                         new_prefix = f"{subprefix}"
                     inner_types.setdefault(nested_type, (new_prefix, new_parent_types))
-
-                    # Handle normal fields
-                    for nested_field_name, nested_field_info in nested_type.model_fields.items():
+                    for nested_field_name, nested_field_info in list(
+                        nested_type.model_fields.items()
+                    ) + list(nested_type.model_computed_fields.items()):
                         nested_field_type = nested_field_info.annotation
                         if new_prefix:
                             new_prefix += f"{nested_field_name.upper()}__"
@@ -229,8 +182,6 @@ def generate_config_table_md(
                             new_parent_types + [nested_field_name],
                         )
 
-                    # Do not extract computed fields
-
         extract_nested_models(field_type, f"{prefix}{config_name}__", toplevel_keys + [field_name])
 
         for new_type, info in inner_types.items():
@@ -308,7 +259,7 @@ def generate_config_md(config_eos: ConfigEOS) -> str:
     markdown = "# Configuration Table\n\n"
 
     # Generate tables for each top level config
-    for field_name, field_info in config_eos.__class__.model_fields.items():
+    for field_name, field_info in config_eos.model_fields.items():
         field_type = field_info.annotation
         markdown += generate_config_table_md(
             field_type, [field_name], f"EOS_{field_name.upper()}__", True
@@ -328,20 +279,6 @@ def generate_config_md(config_eos: ConfigEOS) -> str:
     markdown = markdown.rstrip("\n")
     markdown += "\n"
 
-    # Assure log path does not leak to documentation
-    markdown = re.sub(
-        r'(?<=["\'])/[^"\']*/output/eos\.log(?=["\'])',
-        '/home/user/.local/share/net.akkudoktoreos.net/output/eos.log',
-        markdown
-    )
-
-    # Assure timezone name does not leak to documentation
-    tz_name = to_datetime().timezone_name
-    markdown = re.sub(re.escape(tz_name), "Europe/Berlin", markdown, flags=re.IGNORECASE)
-    # Also replace UTC, as GitHub CI always is on UTC
-    markdown = re.sub(re.escape("UTC"), "Europe/Berlin", markdown, flags=re.IGNORECASE)
-
-
     return markdown
 
 
@@ -361,7 +298,7 @@ def main():
     try:
         config_md = generate_config_md(config_eos)
         if os.name == "nt":
-            config_md = config_md.replace("\\\\", "/")
+            config_md = config_md.replace("127.0.0.1", "0.0.0.0").replace("\\\\", "/")
         if args.output_file:
             # Write to file
             with open(args.output_file, "w", encoding="utf-8", newline="\n") as f:

scripts/generate_openapi.py

@@ -42,9 +42,6 @@ def generate_openapi() -> dict:
     general = openapi_spec["components"]["schemas"]["ConfigEOS"]["properties"]["general"]["default"]
     general["config_file_path"] = "/home/user/.config/net.akkudoktoreos.net/EOS.config.json"
     general["config_folder_path"] = "/home/user/.config/net.akkudoktoreos.net"
-    # Fix file path for logging settings to not show local/test file path
-    logging = openapi_spec["components"]["schemas"]["ConfigEOS"]["properties"]["logging"]["default"]
-    logging["file_path"] = "/home/user/.local/share/net.akkudoktoreos.net/output/eos.log"
 
     return openapi_spec
 
@@ -61,6 +58,8 @@ def main():
     try:
         openapi_spec = generate_openapi()
         openapi_spec_str = json.dumps(openapi_spec, indent=2)
+        if os.name == "nt":
+            openapi_spec_str = openapi_spec_str.replace("127.0.0.1", "0.0.0.0")
         if args.output_file:
             # Write to file
             with open(args.output_file, "w", encoding="utf-8", newline="\n") as f:

scripts/generate_openapi_md.py

@@ -286,7 +286,7 @@ def main():
     try:
         openapi_md = generate_openapi_md()
         if os.name == "nt":
-            openapi_md = openapi_md.replace("127.0.0.1", "127.0.0.1")
+            openapi_md = openapi_md.replace("127.0.0.1", "0.0.0.0")
         if args.output_file:
             # Write to file
             with open(args.output_file, "w", encoding="utf-8", newline="\n") as f:

scripts/gitlint/eos_commit_rules.py

@@ -1 +0,0 @@
-# Placeholder for gitlint user rules (see https://jorisroovers.com/gitlint/latest/rules/user_defined_rules/).

single_test_optimization.py

@@ -1,7 +1,6 @@
 #!/usr/bin/env python3
 
 import argparse
-import asyncio
 import cProfile
 import json
 import pstats
@@ -10,27 +9,24 @@ import time
 from typing import Any
 
 import numpy as np
-from loguru import logger
 
 from akkudoktoreos.config.config import get_config
 from akkudoktoreos.core.ems import get_ems
-from akkudoktoreos.core.emsettings import EnergyManagementMode
-from akkudoktoreos.optimization.genetic.geneticparams import (
-    GeneticOptimizationParameters,
+from akkudoktoreos.core.logging import get_logger
+from akkudoktoreos.optimization.genetic import (
+    OptimizationParameters,
+    optimization_problem,
 )
 from akkudoktoreos.prediction.prediction import get_prediction
-from akkudoktoreos.utils.datetimeutil import to_datetime
 
-config_eos = get_config()
-prediction_eos = get_prediction()
-ems_eos = get_ems()
+get_logger(__name__, logging_level="DEBUG")
 
 
-def prepare_optimization_real_parameters() -> GeneticOptimizationParameters:
+def prepare_optimization_real_parameters() -> OptimizationParameters:
     """Prepare and return optimization parameters with real world data.
 
     Returns:
-        GeneticOptimizationParameters: Configured optimization parameters
+        OptimizationParameters: Configured optimization parameters
     """
     # Make a config
     settings = {
@@ -42,18 +38,6 @@ def prepare_optimization_real_parameters() -> GeneticOptimizationParameters:
             "hours": 48,
             "historic_hours": 24,
         },
-        "optimization": {
-            "horizon_hours": 24,
-            "interval": 3600,
-            "genetic": {
-                "individuals": 300,
-                "generations": 400,
-                "seed": None,
-                "penalties": {
-                    "ev_soc_miss": 10,
-                },
-            },
-        },
         # PV Forecast
         "pvforecast": {
             "provider": "PVForecastAkkudoktor",
@@ -100,30 +84,14 @@ def prepare_optimization_real_parameters() -> GeneticOptimizationParameters:
         "load": {
             "provider": "LoadAkkudoktor",
             "provider_settings": {
-                "LoadAkkudoktor": {
-                    "loadakkudoktor_year_energy_kwh": 5000,  # Energy consumption per year in kWh
-                },
+                "loadakkudoktor_year_energy": 5000,  # Energy consumption per year in kWh
             },
         },
         # -- Simulations --
-        # Assure we have charge rates for the EV
-        "devices": {
-            "max_electric_vehicles": 1,
-            "electric_vehicles": [
-                {
-                    "charge_rates": [
-                        0.0,
-                        6.0 / 16.0,
-                        8.0 / 16.0,
-                        10.0 / 16.0,
-                        12.0 / 16.0,
-                        14.0 / 16.0,
-                        1.0,
-                    ],
-                },
-            ],
-        },
     }
+    config_eos = get_config()
+    prediction_eos = get_prediction()
+    ems_eos = get_ems()
 
     # Update/ set configuration
     config_eos.merge_settings_from_dict(settings)
@@ -131,14 +99,14 @@ def prepare_optimization_real_parameters() -> GeneticOptimizationParameters:
     # Get current prediction data for optimization run
     ems_eos.set_start_datetime()
     print(
-        f"Real data prediction from {prediction_eos.ems_start_datetime} to {prediction_eos.end_datetime}"
+        f"Real data prediction from {prediction_eos.start_datetime} to {prediction_eos.end_datetime}"
     )
     prediction_eos.update_data()
 
     # PV Forecast (in W)
     pv_forecast = prediction_eos.key_to_array(
         key="pvforecast_ac_power",
-        start_datetime=prediction_eos.ems_start_datetime,
+        start_datetime=prediction_eos.start_datetime,
         end_datetime=prediction_eos.end_datetime,
     )
     print(f"pv_forecast: {pv_forecast}")
@@ -146,7 +114,7 @@ def prepare_optimization_real_parameters() -> GeneticOptimizationParameters:
     # Temperature Forecast (in degree C)
     temperature_forecast = prediction_eos.key_to_array(
         key="weather_temp_air",
-        start_datetime=prediction_eos.ems_start_datetime,
+        start_datetime=prediction_eos.start_datetime,
         end_datetime=prediction_eos.end_datetime,
     )
     print(f"temperature_forecast: {temperature_forecast}")
@@ -154,7 +122,7 @@ def prepare_optimization_real_parameters() -> GeneticOptimizationParameters:
     # Electricity Price (in Euro per Wh)
     strompreis_euro_pro_wh = prediction_eos.key_to_array(
         key="elecprice_marketprice_wh",
-        start_datetime=prediction_eos.ems_start_datetime,
+        start_datetime=prediction_eos.start_datetime,
         end_datetime=prediction_eos.end_datetime,
     )
     print(f"strompreis_euro_pro_wh: {strompreis_euro_pro_wh}")
@@ -162,7 +130,7 @@ def prepare_optimization_real_parameters() -> GeneticOptimizationParameters:
     # Overall System Load (in W)
     gesamtlast = prediction_eos.key_to_array(
         key="load_mean",
-        start_datetime=prediction_eos.ems_start_datetime,
+        start_datetime=prediction_eos.start_datetime,
         end_datetime=prediction_eos.end_datetime,
     )
     print(f"gesamtlast: {gesamtlast}")
@@ -172,7 +140,7 @@ def prepare_optimization_real_parameters() -> GeneticOptimizationParameters:
     print(f"start_solution: {start_solution}")
 
     # Define parameters for the optimization problem
-    return GeneticOptimizationParameters(
+    return OptimizationParameters(
         **{
             "ems": {
                 "preis_euro_pro_wh_akku": 0e-05,
@@ -182,18 +150,14 @@ def prepare_optimization_real_parameters() -> GeneticOptimizationParameters:
                 "strompreis_euro_pro_wh": strompreis_euro_pro_wh,
             },
             "pv_akku": {
-                "device_id": "battery 1",
+                "device_id": "battery1",
                 "capacity_wh": 26400,
                 "initial_soc_percentage": 15,
                 "min_soc_percentage": 15,
             },
-            "inverter": {
-                "device_id": "inverter 1",
-                "max_power_wh": 10000,
-                "battery_id": "battery 1",
-            },
+            "inverter": {"device_id": "iv1", "max_power_wh": 10000, "battery_id": "battery1"},
             "eauto": {
-                "device_id": "electric vehicle 1",
+                "device_id": "ev1",
                 "min_soc_percentage": 50,
                 "capacity_wh": 60000,
                 "charging_efficiency": 0.95,
@@ -206,49 +170,12 @@ def prepare_optimization_real_parameters() -> GeneticOptimizationParameters:
     )
 
 
-def prepare_optimization_parameters() -> GeneticOptimizationParameters:
+def prepare_optimization_parameters() -> OptimizationParameters:
     """Prepare and return optimization parameters with predefined data.
 
     Returns:
-        GeneticOptimizationParameters: Configured optimization parameters
+        OptimizationParameters: Configured optimization parameters
     """
-    # Initialize the optimization problem using the default configuration
-    config_eos.merge_settings_from_dict(
-        {
-            "prediction": {"hours": 48},
-            "optimization": {
-                "horizon_hours": 48,
-                "interval": 3600,
-                "genetic": {
-                    "individuals": 300,
-                    "generations": 400,
-                    "seed": None,
-                    "penalties": {
-                        "ev_soc_miss": 10,
-                    },
-                },
-            },
-            # Assure we have charge rates for the EV
-            "devices": {
-                "max_electric_vehicles": 1,
-                "electric_vehicles": [
-                    {
-                        "device_id": "Default EV",
-                        "charge_rates": [
-                            0.0,
-                            6.0 / 16.0,
-                            8.0 / 16.0,
-                            10.0 / 16.0,
-                            12.0 / 16.0,
-                            14.0 / 16.0,
-                            1.0,
-                        ],
-                    },
-                ],
-            },
-        }
-    )
-
     # PV Forecast (in W)
     pv_forecast = np.zeros(48)
     pv_forecast[12] = 5000
@@ -367,7 +294,7 @@ def prepare_optimization_parameters() -> GeneticOptimizationParameters:
     start_solution = None
 
     # Define parameters for the optimization problem
-    return GeneticOptimizationParameters(
+    return OptimizationParameters(
         **{
             "ems": {
                 "preis_euro_pro_wh_akku": 0e-05,
@@ -377,18 +304,14 @@ def prepare_optimization_parameters() -> GeneticOptimizationParameters:
                 "strompreis_euro_pro_wh": strompreis_euro_pro_wh,
             },
             "pv_akku": {
-                "device_id": "battery 1",
+                "device_id": "battery1",
                 "capacity_wh": 26400,
                 "initial_soc_percentage": 15,
                 "min_soc_percentage": 15,
             },
-            "inverter": {
-                "device_id": "inverter 1",
-                "max_power_wh": 10000,
-                "battery_id": "battery 1",
-            },
+            "inverter": {"device_id": "iv1", "max_power_wh": 10000, "battery_id": "battery1"},
             "eauto": {
-                "device_id": "electric vehicle 1",
+                "device_id": "ev1",
                 "min_soc_percentage": 50,
                 "capacity_wh": 60000,
                 "charging_efficiency": 0.95,
@@ -416,33 +339,27 @@ def run_optimization(
     # Prepare parameters
     if parameters_file:
         with open(parameters_file, "r") as f:
-            parameters = GeneticOptimizationParameters(**json.load(f))
+            parameters = OptimizationParameters(**json.load(f))
     elif real_world:
         parameters = prepare_optimization_real_parameters()
     else:
         parameters = prepare_optimization_parameters()
-    logger.info("Optimization Parameters:")
-    logger.info(parameters.model_dump_json(indent=4))
 
-    if start_hour is None:
-        start_datetime = None
-    else:
-        start_datetime = to_datetime().set(hour=start_hour)
+    if verbose:
+        print("\nOptimization Parameters:")
+        print(parameters.model_dump_json(indent=4))
 
-    asyncio.run(
-        ems_eos.run(
-            start_datetime=start_datetime,
-            mode=EnergyManagementMode.OPTIMIZATION,
-            genetic_parameters=parameters,
-            genetic_individuals=ngen,
-            genetic_seed=seed,
-        )
+    # Initialize the optimization problem using the default configuration
+    config_eos = get_config()
+    config_eos.merge_settings_from_dict(
+        {"prediction": {"hours": 48}, "optimization": {"hours": 48}}
     )
+    opt_class = optimization_problem(verbose=verbose, fixed_seed=seed)
 
-    solution = ems_eos.genetic_solution()
-    if solution is None:
-        return None
-    return solution.model_dump_json()
+    # Perform the optimisation based on the provided parameters and start hour
+    result = opt_class.optimierung_ems(parameters=parameters, start_hour=start_hour, ngen=ngen)
+
+    return result.model_dump_json()
 
 
 def main():

single_test_prediction.py

@@ -93,22 +93,6 @@ def config_elecprice() -> dict:
     return settings
 
 
-def config_feedintarifffixed() -> dict:
-    """Configure settings for feed in tariff forecast."""
-    settings = {
-        "general": {
-            "latitude": 52.52,
-            "longitude": 13.405,
-        },
-        "prediction": {
-            "hours": 48,
-            "historic_hours": 24,
-        },
-        "feedintariff": dict(),
-    }
-    return settings
-
-
 def config_load() -> dict:
     """Configure settings for load forecast."""
     settings = {
@@ -146,13 +130,10 @@ def run_prediction(provider_id: str, verbose: bool = False) -> str:
     elif provider_id in ("ElecPriceAkkudoktor",):
         settings = config_elecprice()
         forecast = "elecprice"
-    elif provider_id in ("FeedInTariffFixed",):
-        settings = config_feedintarifffixed()
-        forecast = "feedintariff"
     elif provider_id in ("LoadAkkudoktor",):
-        settings = config_load()
-        forecast = "loadforecast"
-        settings["load"]["LoadAkkudoktor"]["loadakkudoktor_year_energy_wh"] = 1000
+        settings = config_elecprice()
+        forecast = "load"
+        settings["load"]["loadakkudoktor_year_energy"] = 1000
     else:
         raise ValueError(f"Unknown provider '{provider_id}'.")
     settings[forecast]["provider"] = provider_id
@@ -170,7 +151,6 @@ def run_prediction(provider_id: str, verbose: bool = False) -> str:
         print(settings)
         print("\nProvider\n----------")
         print(f"elecprice.provider: {config_eos.elecprice.provider}")
-        print(f"feedintariff.provider: {config_eos.feedintariff.provider}")
         print(f"load.provider: {config_eos.load.provider}")
         print(f"pvforecast.provider: {config_eos.pvforecast.provider}")
         print(f"weather.provider: {config_eos.weather.provider}")

src/akkudoktoreos/config/config.py

@@ -9,42 +9,43 @@ Key features:
 - Managing directory setups for the application
 """
 
-import json
 import os
 import shutil
 from pathlib import Path
 from typing import Any, ClassVar, Optional, Type
 
-import pydantic_settings
-from loguru import logger
 from platformdirs import user_config_dir, user_data_dir
-from pydantic import Field, computed_field, field_validator
+from pydantic import Field, computed_field
+from pydantic_settings import (
+    BaseSettings,
+    JsonConfigSettingsSource,
+    PydanticBaseSettingsSource,
+    SettingsConfigDict,
+)
 
 # settings
 from akkudoktoreos.config.configabc import SettingsBaseModel
-from akkudoktoreos.config.configmigrate import migrate_config_data, migrate_config_file
 from akkudoktoreos.core.cachesettings import CacheCommonSettings
 from akkudoktoreos.core.coreabc import SingletonMixin
 from akkudoktoreos.core.decorators import classproperty
-from akkudoktoreos.core.emsettings import (
-    EnergyManagementCommonSettings,
-)
+from akkudoktoreos.core.emsettings import EnergyManagementCommonSettings
+from akkudoktoreos.core.logging import get_logger
 from akkudoktoreos.core.logsettings import LoggingCommonSettings
-from akkudoktoreos.core.pydantic import PydanticModelNestedValueMixin, merge_models
-from akkudoktoreos.core.version import __version__
-from akkudoktoreos.devices.devices import DevicesCommonSettings
+from akkudoktoreos.core.pydantic import access_nested_value, merge_models
+from akkudoktoreos.devices.settings import DevicesCommonSettings
 from akkudoktoreos.measurement.measurement import MeasurementCommonSettings
 from akkudoktoreos.optimization.optimization import OptimizationCommonSettings
 from akkudoktoreos.prediction.elecprice import ElecPriceCommonSettings
-from akkudoktoreos.prediction.feedintariff import FeedInTariffCommonSettings
 from akkudoktoreos.prediction.load import LoadCommonSettings
 from akkudoktoreos.prediction.prediction import PredictionCommonSettings
 from akkudoktoreos.prediction.pvforecast import PVForecastCommonSettings
 from akkudoktoreos.prediction.weather import WeatherCommonSettings
 from akkudoktoreos.server.server import ServerCommonSettings
-from akkudoktoreos.utils.datetimeutil import to_datetime, to_timezone
+from akkudoktoreos.utils.datetimeutil import to_timezone
 from akkudoktoreos.utils.utils import UtilsCommonSettings
 
+logger = get_logger(__name__)
+
 
 def get_absolute_path(
     basepath: Optional[Path | str], subpath: Optional[Path | str]
@@ -79,15 +80,15 @@ class GeneralSettings(SettingsBaseModel):
     Properties:
         timezone (Optional[str]): Computed time zone string based on the specified latitude
             and longitude.
+
+    Validators:
+        validate_latitude (float): Ensures `latitude` is within the range -90 to 90.
+        validate_longitude (float): Ensures `longitude` is within the range -180 to 180.
     """
 
     _config_folder_path: ClassVar[Optional[Path]] = None
     _config_file_path: ClassVar[Optional[Path]] = None
 
-    version: str = Field(
-        default=__version__, description="Configuration file version. Used to check compatibility."
-    )
-
     data_folder_path: Optional[Path] = Field(
         default=None, description="Path to EOS data directory.", examples=[None, "/home/eos/data"]
     )
@@ -136,25 +137,11 @@ class GeneralSettings(SettingsBaseModel):
         """Path to EOS configuration file."""
         return self._config_file_path
 
-    compatible_versions: ClassVar[list[str]] = [__version__]
 
-    @field_validator("version")
-    @classmethod
-    def check_version(cls, v: str) -> str:
-        if v not in cls.compatible_versions:
-            error = (
-                f"Incompatible configuration version '{v}'. "
-                f"Expected one of: {', '.join(cls.compatible_versions)}."
-            )
-            logger.error(error)
-            raise ValueError(error)
-        return v
-
-
-class SettingsEOS(pydantic_settings.BaseSettings, PydanticModelNestedValueMixin):
+class SettingsEOS(BaseSettings):
     """Settings for all EOS.
 
-    Only used to update the configuration with specific settings.
+    Used by updating the configuration with specific settings only.
     """
 
     general: Optional[GeneralSettings] = Field(
@@ -193,10 +180,6 @@ class SettingsEOS(pydantic_settings.BaseSettings, PydanticModelNestedValueMixin)
         default=None,
         description="Electricity Price Settings",
     )
-    feedintariff: Optional[FeedInTariffCommonSettings] = Field(
-        default=None,
-        description="Feed In Tariff Settings",
-    )
     load: Optional[LoadCommonSettings] = Field(
         default=None,
         description="Load Settings",
@@ -218,7 +201,7 @@ class SettingsEOS(pydantic_settings.BaseSettings, PydanticModelNestedValueMixin)
         description="Utilities Settings",
     )
 
-    model_config = pydantic_settings.SettingsConfigDict(
+    model_config = SettingsConfigDict(
         env_nested_delimiter="__",
         nested_model_default_partial_update=True,
         env_prefix="EOS_",
@@ -241,18 +224,12 @@ class SettingsEOSDefaults(SettingsEOS):
     optimization: OptimizationCommonSettings = OptimizationCommonSettings()
     prediction: PredictionCommonSettings = PredictionCommonSettings()
     elecprice: ElecPriceCommonSettings = ElecPriceCommonSettings()
-    feedintariff: FeedInTariffCommonSettings = FeedInTariffCommonSettings()
     load: LoadCommonSettings = LoadCommonSettings()
     pvforecast: PVForecastCommonSettings = PVForecastCommonSettings()
     weather: WeatherCommonSettings = WeatherCommonSettings()
     server: ServerCommonSettings = ServerCommonSettings()
     utils: UtilsCommonSettings = UtilsCommonSettings()
 
-    def __hash__(self) -> int:
-        # Just for usage in configmigrate, finally overwritten when used by ConfigEOS.
-        # This is mutable, so pydantic does not set a hash.
-        return id(self)
-
 
 class ConfigEOS(SingletonMixin, SettingsEOSDefaults):
     """Singleton configuration handler for the EOS application.
@@ -306,47 +283,36 @@ class ConfigEOS(SingletonMixin, SettingsEOSDefaults):
     ENCODING: ClassVar[str] = "UTF-8"
     CONFIG_FILE_NAME: ClassVar[str] = "EOS.config.json"
 
-    def __hash__(self) -> int:
-        # ConfigEOS is a singleton
-        return hash("config_eos")
-
-    def __eq__(self, other: Any) -> bool:
-        if not isinstance(other, ConfigEOS):
-            return False
-        # ConfigEOS is a singleton
-        return True
-
     @classmethod
     def settings_customise_sources(
         cls,
-        settings_cls: Type[pydantic_settings.BaseSettings],
-        init_settings: pydantic_settings.PydanticBaseSettingsSource,
-        env_settings: pydantic_settings.PydanticBaseSettingsSource,
-        dotenv_settings: pydantic_settings.PydanticBaseSettingsSource,
-        file_secret_settings: pydantic_settings.PydanticBaseSettingsSource,
-    ) -> tuple[pydantic_settings.PydanticBaseSettingsSource, ...]:
-        """Customizes the order and handling of settings sources for a pydantic_settings.BaseSettings subclass.
+        settings_cls: Type[BaseSettings],
+        init_settings: PydanticBaseSettingsSource,
+        env_settings: PydanticBaseSettingsSource,
+        dotenv_settings: PydanticBaseSettingsSource,
+        file_secret_settings: PydanticBaseSettingsSource,
+    ) -> tuple[PydanticBaseSettingsSource, ...]:
+        """Customizes the order and handling of settings sources for a Pydantic BaseSettings subclass.
 
         This method determines the sources for application configuration settings, including
         environment variables, dotenv files and JSON configuration files.
         It ensures that a default configuration file exists and creates one if necessary.
 
         Args:
-            settings_cls (Type[pydantic_settings.BaseSettings]): The Pydantic BaseSettings class for
-                which sources are customized.
-            init_settings (pydantic_settings.PydanticBaseSettingsSource): The initial settings source, typically passed at runtime.
-            env_settings (pydantic_settings.PydanticBaseSettingsSource): Settings sourced from environment variables.
-            dotenv_settings (pydantic_settings.PydanticBaseSettingsSource): Settings sourced from a dotenv file.
-            file_secret_settings (pydantic_settings.PydanticBaseSettingsSource): Unused (needed for parent class interface).
+            settings_cls (Type[BaseSettings]): The Pydantic BaseSettings class for which sources are customized.
+            init_settings (PydanticBaseSettingsSource): The initial settings source, typically passed at runtime.
+            env_settings (PydanticBaseSettingsSource): Settings sourced from environment variables.
+            dotenv_settings (PydanticBaseSettingsSource): Settings sourced from a dotenv file.
+            file_secret_settings (PydanticBaseSettingsSource): Unused (needed for parent class interface).
 
         Returns:
-            tuple[pydantic_settings.PydanticBaseSettingsSource, ...]: A tuple of settings sources in the order they should be applied.
+            tuple[PydanticBaseSettingsSource, ...]: A tuple of settings sources in the order they should be applied.
 
         Behavior:
             1. Checks for the existence of a JSON configuration file in the expected location.
             2. If the configuration file does not exist, creates the directory (if needed) and attempts to copy a
                default configuration file to the location. If the copy fails, uses the default configuration file directly.
-            3. Creates a `pydantic_settings.JsonConfigSettingsSource` for both the configuration file and the default configuration file.
+            3. Creates a `JsonConfigSettingsSource` for both the configuration file and the default configuration file.
             4. Updates class attributes `GeneralSettings._config_folder_path` and
                `GeneralSettings._config_file_path` to reflect the determined paths.
             5. Returns a tuple containing all provided and newly created settings sources in the desired order.
@@ -355,7 +321,13 @@ class ConfigEOS(SingletonMixin, SettingsEOSDefaults):
             - This method logs a warning if the default configuration file cannot be copied.
             - It ensures that a fallback to the default configuration file is always possible.
         """
-        # Ensure we know and have the config folder path and the config file
+        setting_sources = [
+            init_settings,
+            env_settings,
+            dotenv_settings,
+        ]
+
+        file_settings: Optional[JsonConfigSettingsSource] = None
         config_file, exists = cls._get_config_file_path()
         config_dir = config_file.parent
         if not exists:
@@ -366,38 +338,20 @@ class ConfigEOS(SingletonMixin, SettingsEOSDefaults):
                 logger.warning(f"Could not copy default config: {exc}. Using default config...")
                 config_file = cls.config_default_file_path
                 config_dir = config_file.parent
-        # Remember config_dir and config file
+        try:
+            file_settings = JsonConfigSettingsSource(settings_cls, json_file=config_file)
+            setting_sources.append(file_settings)
+        except Exception as e:
+            logger.error(
+                f"Error reading config file '{config_file}' (falling back to default config): {e}"
+            )
+        default_settings = JsonConfigSettingsSource(
+            settings_cls, json_file=cls.config_default_file_path
+        )
         GeneralSettings._config_folder_path = config_dir
         GeneralSettings._config_file_path = config_file
 
-        # All the settings sources in priority sequence
-        setting_sources = [
-            init_settings,
-            env_settings,
-            dotenv_settings,
-        ]
-
-        # Apend file settings to sources
-        file_settings: Optional[pydantic_settings.JsonConfigSettingsSource] = None
-        try:
-            backup_file = config_file.with_suffix(f".{to_datetime(as_string='YYYYMMDDHHmmss')}")
-            if migrate_config_file(config_file, backup_file):
-                # If the config file does have the correct version add it as settings source
-                file_settings = pydantic_settings.JsonConfigSettingsSource(
-                    settings_cls, json_file=config_file
-                )
-                setting_sources.append(file_settings)
-        except Exception as ex:
-            logger.error(
-                f"Error reading config file '{config_file}' (falling back to default config): {ex}"
-            )
-
-        # Append default settings to sources
-        default_settings = pydantic_settings.JsonConfigSettingsSource(
-            settings_cls, json_file=cls.config_default_file_path
-        )
         setting_sources.append(default_settings)
-
         return tuple(setting_sources)
 
     @classproperty
@@ -416,24 +370,19 @@ class ConfigEOS(SingletonMixin, SettingsEOSDefaults):
         Configuration data is loaded from a configuration file or a default one is created if none
         exists.
         """
-        logger.debug("Config init with parameters {} {}", args, kwargs)
-        # Check for singleton guard
         if hasattr(self, "_initialized"):
             return
         self._setup(self, *args, **kwargs)
 
     def _setup(self, *args: Any, **kwargs: Any) -> None:
         """Re-initialize global settings."""
-        logger.debug("Config setup with parameters {} {}", args, kwargs)
-        # Assure settings base knows the singleton EOS configuration
+        # Assure settings base knows EOS configuration
         SettingsBaseModel.config = self
-        # (Re-)load settings - call base class init
+        # (Re-)load settings
         SettingsEOSDefaults.__init__(self, *args, **kwargs)
         # Init config file and data folder pathes
         self._create_initial_config_file()
         self._update_data_folder_path()
-        self._initialized = True
-        logger.debug("Config setup:\n{}", self)
 
     def merge_settings(self, settings: SettingsEOS) -> None:
         """Merges the provided settings into the global settings for EOS, with optional overwrite.
@@ -445,9 +394,7 @@ class ConfigEOS(SingletonMixin, SettingsEOSDefaults):
             ValueError: If the `settings` is not a `SettingsEOS` instance.
         """
         if not isinstance(settings, SettingsEOS):
-            error_msg = f"Settings must be an instance of SettingsEOS: '{settings}'."
-            logger.error(error_msg)
-            raise ValueError(error_msg)
+            raise ValueError(f"Settings must be an instance of SettingsEOS: '{settings}'.")
 
         self.merge_settings_from_dict(settings.model_dump(exclude_none=True, exclude_unset=True))
 
@@ -479,87 +426,31 @@ class ConfigEOS(SingletonMixin, SettingsEOSDefaults):
         """
         self._setup()
 
-    def revert_settings(self, backup_id: str) -> None:
-        """Revert application settings to a stored backup.
+    def set_config_value(self, path: str, value: Any) -> None:
+        """Set a configuration value based on the provided path.
 
-        This method restores configuration values from a backup file identified
-        by `backup_id`. The backup is expected to exist alongside the main
-        configuration file, using the main config file's path but with the given
-        suffix. Any settings previously applied will be overwritten.
+        Supports string paths (with '/' separators) or sequence paths (list/tuple).
+        Trims leading and trailing '/' from string paths.
 
         Args:
-            backup_id (str): The suffix used to locate the backup configuration
-                file. Example: ``".bak"`` or ``".backup"``.
+            path (str): The path to the configuration key (e.g., "key1/key2/key3" or key1/key2/0).
+            value (Any]): The value to set.
+        """
+        access_nested_value(self, path, True, value)
+
+    def get_config_value(self, path: str) -> Any:
+        """Get a configuration value based on the provided path.
+
+        Supports string paths (with '/' separators) or sequence paths (list/tuple).
+        Trims leading and trailing '/' from string paths.
+
+        Args:
+            path (str): The path to the configuration key (e.g., "key1/key2/key3" or key1/key2/0).
 
         Returns:
-            None: The method does not return a value.
-
-        Raises:
-            ValueError: If the backup file cannot be found at the constructed path.
-            json.JSONDecodeError: If the backup file exists but contains invalid JSON.
-            TypeError: If the unpacked backup data fails to match the signature
-                required by ``self._setup()``.
-            OSError: If reading the backup file fails due to I/O issues.
+            Any: The retrieved value.
         """
-        backup_file_path = self.general.config_file_path.with_suffix(f".{backup_id}")
-        if not backup_file_path.exists():
-            error_msg = f"Configuration backup `{backup_id}` not found."
-            logger.error(error_msg)
-            raise ValueError(error_msg)
-
-        with backup_file_path.open("r", encoding="utf-8") as f:
-            backup_data: dict[str, Any] = json.load(f)
-        backup_settings = migrate_config_data(backup_data)
-
-        self._setup(**backup_settings.model_dump(exclude_none=True, exclude_unset=True))
-
-    def list_backups(self) -> dict[str, dict[str, Any]]:
-        """List available configuration backup files and extract metadata.
-
-        Backup files are identified by sharing the same stem as the main config
-        file but having a different suffix. Each backup file is assumed to contain
-        a JSON object.
-
-        The returned dictionary uses `backup_id` (suffix) as keys. The value for
-        each key is a dictionary including:
-        - ``storage_time``: The file modification timestamp in ISO-8601 format.
-        - ``version``: Version information found in the backup file
-            (defaults to ``"unknown"``).
-
-        Returns:
-            dict[str, dict[str, Any]]: Mapping of backup identifiers to metadata.
-
-        Raises:
-            OSError: If directory scanning or file reading fails.
-            json.JSONDecodeError: If a backup file cannot be parsed as JSON.
-        """
-        result: dict[str, dict[str, Any]] = {}
-
-        base_path: Path = self.general.config_file_path
-        parent = base_path.parent
-        stem = base_path.stem
-
-        # Iterate files next to config file
-        for file in parent.iterdir():
-            if file.is_file() and file.stem == stem and file != base_path:
-                backup_id = file.suffix[1:]
-
-                # Read version from file
-                with file.open("r", encoding="utf-8") as f:
-                    data: dict[str, Any] = json.load(f)
-
-                # Extract version safely
-                version = data.get("general", {}).get("version", "unknown")
-
-                # Read file modification time (OS-independent)
-                ts = file.stat().st_mtime
-                storage_time = to_datetime(ts, as_string=True)
-                result[backup_id] = {
-                    "date_time": storage_time,
-                    "version": version,
-                }
-
-        return result
+        return access_nested_value(self, path, False)
 
     def _create_initial_config_file(self) -> None:
         if self.general.config_file_path and not self.general.config_file_path.exists():
@@ -606,15 +497,10 @@ class ConfigEOS(SingletonMixin, SettingsEOSDefaults):
 
     @classmethod
     def _get_config_file_path(cls) -> tuple[Path, bool]:
-        """Find a valid configuration file or return the desired path for a new config file.
-
-        Searches:
-            1. environment variable directory
-            2. user configuration directory
-            3. current working directory
+        """Finds the a valid configuration file or returns the desired path for a new config file.
 
         Returns:
-            tuple[Path, bool]: The path to the configuration file and if there is already a config file there
+            tuple[Path, bool]: The path to the configuration directory and if there is already a config file there
         """
         config_dirs = []
         env_base_dir = os.getenv(cls.EOS_DIR)
@@ -643,7 +529,7 @@ class ConfigEOS(SingletonMixin, SettingsEOSDefaults):
         if not self.general.config_file_path:
             raise ValueError("Configuration file path unknown.")
         with self.general.config_file_path.open("w", encoding="utf-8", newline="\n") as f_out:
-            json_str = super().model_dump_json(indent=4)
+            json_str = super().model_dump_json()
             f_out.write(json_str)
 
     def update(self) -> None:

src/akkudoktoreos/config/configmigrate.py

@@ -1,252 +0,0 @@
-"""Migrate config file to actual version."""
-
-import json
-import shutil
-from pathlib import Path
-from typing import TYPE_CHECKING, Any, Callable, Dict, List, Set, Tuple, Union
-
-from loguru import logger
-
-from akkudoktoreos.core.version import __version__
-
-if TYPE_CHECKING:
-    # There are circular dependencies - only import here for type checking
-    from akkudoktoreos.config.config import SettingsEOSDefaults
-
-# -----------------------------
-# Global migration map constant
-# -----------------------------
-# key: old JSON path, value: either
-#   - str (new model path)
-#   - tuple[str, Callable[[Any], Any]] (new path + transform)
-#   - None (drop)
-MIGRATION_MAP: Dict[str, Union[str, Tuple[str, Callable[[Any], Any]], None]] = {
-    # 0.1.0 -> 0.2.0
-    "devices/batteries/0/initial_soc_percentage": None,
-    "devices/electric_vehicles/0/initial_soc_percentage": None,
-    "elecprice/provider_settings/import_file_path": "elecprice/provider_settings/ElecPriceImport/import_file_path",
-    "elecprice/provider_settings/import_json": "elecprice/provider_settings/ElecPriceImport/import_json",
-    "load/provider_settings/import_file_path": "load/provider_settings/LoadImport/import_file_path",
-    "load/provider_settings/import_json": "load/provider_settings/LoadImport/import_json",
-    "load/provider_settings/loadakkudoktor_year_energy": "load/provider_settings/LoadAkkudoktor/loadakkudoktor_year_energy_kwh",
-    "load/provider_settings/load_vrm_idsite": "load/provider_settings/LoadVrm/load_vrm_idsite",
-    "load/provider_settings/load_vrm_token": "load/provider_settings/LoadVrm/load_vrm_token",
-    "logging/level": "logging/console_level",
-    "logging/root_level": None,
-    "measurement/load0_name": "measurement/load_emr_keys/0",
-    "measurement/load1_name": "measurement/load_emr_keys/1",
-    "measurement/load2_name": "measurement/load_emr_keys/2",
-    "measurement/load3_name": "measurement/load_emr_keys/3",
-    "measurement/load4_name": "measurement/load_emr_keys/4",
-    "optimization/ev_available_charge_rates_percent": (
-        "devices/electric_vehicles/0/charge_rates",
-        lambda v: [x / 100 for x in v],
-    ),
-    "optimization/hours": "optimization/horizon_hours",
-    "optimization/penalty": ("optimization/genetic/penalties/ev_soc_miss", lambda v: float(v)),
-    "pvforecast/provider_settings/import_file_path": "pvforecast/provider_settings/PVForecastImport/import_file_path",
-    "pvforecast/provider_settings/import_json": "pvforecast/provider_settings/PVForecastImport/import_json",
-    "pvforecast/provider_settings/load_vrm_idsite": "pvforecast/provider_settings/PVForecastVrm/load_vrm_idsite",
-    "pvforecast/provider_settings/load_vrm_token": "pvforecast/provider_settings/PVForecastVrm/load_vrm_token",
-    "weather/provider_settings/import_file_path": "weather/provider_settings/WeatherImport/import_file_path",
-    "weather/provider_settings/import_json": "weather/provider_settings/WeatherImport/import_json",
-}
-
-# -----------------------------
-# Global migration stats
-# -----------------------------
-migrated_source_paths: Set[str] = set()
-mapped_count: int = 0
-auto_count: int = 0
-skipped_paths: List[str] = []
-
-
-def migrate_config_data(config_data: Dict[str, Any]) -> "SettingsEOSDefaults":
-    """Migrate configuration data to the current version settings.
-
-    Returns:
-        SettingsEOSDefaults: The migrated settings.
-    """
-    global migrated_source_paths, mapped_count, auto_count, skipped_paths
-
-    # Reset globals at the start of each migration
-    migrated_source_paths = set()
-    mapped_count = 0
-    auto_count = 0
-    skipped_paths = []
-
-    from akkudoktoreos.config.config import SettingsEOSDefaults
-
-    new_config = SettingsEOSDefaults()
-
-    # 1) Apply explicit migration map
-    for old_path, mapping in MIGRATION_MAP.items():
-        new_path = None
-        transform = None
-        if mapping is None:
-            migrated_source_paths.add(old_path.strip("/"))
-            logger.debug(f"🗑️ Migration map: dropping '{old_path}'")
-            continue
-        if isinstance(mapping, tuple):
-            new_path, transform = mapping
-        else:
-            new_path = mapping
-
-        old_value = _get_json_nested_value(config_data, old_path)
-        if old_value is None:
-            migrated_source_paths.add(old_path.strip("/"))
-            mapped_count += 1
-            logger.debug(f"✅ Migrated mapped '{old_path}' → 'None'")
-            continue
-
-        try:
-            if transform:
-                old_value = transform(old_value)
-            new_config.set_nested_value(new_path, old_value)
-            migrated_source_paths.add(old_path.strip("/"))
-            mapped_count += 1
-            logger.debug(f"✅ Migrated mapped '{old_path}' → '{new_path}' = {old_value!r}")
-        except Exception as e:
-            logger.opt(exception=True).warning(
-                f"Failed mapped migration '{old_path}' -> '{new_path}': {e}"
-            )
-
-    # 2) Automatic migration for remaining fields
-    auto_count += _migrate_matching_fields(
-        config_data, new_config, migrated_source_paths, skipped_paths
-    )
-
-    # 3) Ensure version
-    try:
-        new_config.set_nested_value("general/version", __version__)
-    except Exception as e:
-        logger.warning(f"Could not set version on new configuration model: {e}")
-
-    # 4) Log final migration summary
-    logger.info(
-        f"Migration summary: "
-        f"mapped fields: {mapped_count}, automatically migrated: {auto_count}, skipped: {len(skipped_paths)}"
-    )
-    if skipped_paths:
-        logger.debug(f"Skipped paths: {', '.join(skipped_paths)}")
-
-    logger.success(f"Configuration successfully migrated to version {__version__}.")
-    return new_config
-
-
-def migrate_config_file(config_file: Path, backup_file: Path) -> bool:
-    """Migrate configuration file to the current version.
-
-    Returns:
-        bool: True if up-to-date or successfully migrated, False on failure.
-    """
-    global migrated_source_paths, mapped_count, auto_count, skipped_paths
-
-    # Reset globals at the start of each migration
-    migrated_source_paths = set()
-    mapped_count = 0
-    auto_count = 0
-    skipped_paths = []
-
-    try:
-        with config_file.open("r", encoding="utf-8") as f:
-            config_data: Dict[str, Any] = json.load(f)
-    except (FileNotFoundError, json.JSONDecodeError) as e:
-        logger.error(f"Failed to read configuration file '{config_file}': {e}")
-        return False
-
-    match config_data:
-        case {"general": {"version": v}} if v == __version__:
-            logger.debug(f"Configuration file '{config_file}' is up to date (v{v}).")
-            return True
-        case _:
-            logger.info(
-                f"Configuration file '{config_file}' is missing current version info. "
-                f"Starting migration to v{__version__}..."
-            )
-
-    try:
-        # Backup existing file - we already know it is existing
-        try:
-            config_file.replace(backup_file)
-            logger.info(f"Backed up old configuration to '{backup_file}'.")
-        except Exception as e_replace:
-            try:
-                shutil.copy(config_file, backup_file)
-                logger.info(
-                    f"Could not replace; copied old configuration to '{backup_file}' instead."
-                )
-            except Exception as e_copy:
-                logger.warning(
-                    f"Failed to backup existing config (replace: {e_replace}; copy: {e_copy}). Continuing without backup."
-                )
-
-        # Migrate config data
-        new_config = migrate_config_data(config_data)
-
-        # Write migrated configuration
-        try:
-            with config_file.open("w", encoding="utf-8", newline=None) as f_out:
-                json_str = new_config.model_dump_json(indent=4)
-                f_out.write(json_str)
-        except Exception as e_write:
-            logger.error(f"Failed to write migrated configuration to '{config_file}': {e_write}")
-            return False
-
-        return True
-
-    except Exception as e:
-        logger.exception(f"Unexpected error during migration: {e}")
-        return False
-
-
-def _get_json_nested_value(data: dict, path: str) -> Any:
-    """Retrieve a nested value from a JSON-like dict using '/'-separated path."""
-    current: Any = data
-    for part in path.strip("/").split("/"):
-        if isinstance(current, list):
-            try:
-                part_idx = int(part)
-                current = current[part_idx]
-            except (ValueError, IndexError):
-                return None
-        elif isinstance(current, dict):
-            if part not in current:
-                return None
-            current = current[part]
-        else:
-            return None
-    return current
-
-
-def _migrate_matching_fields(
-    source: Dict[str, Any],
-    target_model: Any,
-    migrated_source_paths: Set[str],
-    skipped_paths: List[str],
-    prefix: str = "",
-) -> int:
-    """Recursively copy matching keys from source dict into target_model using set_nested_value.
-
-    Returns:
-        int: number of fields successfully auto-migrated
-    """
-    count: int = 0
-    for key, value in source.items():
-        full_path = f"{prefix}/{key}".strip("/")
-
-        if full_path in migrated_source_paths:
-            continue
-
-        if isinstance(value, dict):
-            count += _migrate_matching_fields(
-                value, target_model, migrated_source_paths, skipped_paths, full_path
-            )
-        else:
-            try:
-                target_model.set_nested_value(full_path, value)
-                count += 1
-            except Exception:
-                skipped_paths.append(full_path)
-                continue
-    return count

src/akkudoktoreos/core/cache.py

@@ -27,18 +27,16 @@ from typing import (
 )
 
 import cachebox
-from loguru import logger
+from pendulum import DateTime, Duration
 from pydantic import Field
 
 from akkudoktoreos.core.coreabc import ConfigMixin, SingletonMixin
+from akkudoktoreos.core.logging import get_logger
 from akkudoktoreos.core.pydantic import PydanticBaseModel
-from akkudoktoreos.utils.datetimeutil import (
-    DateTime,
-    Duration,
-    compare_datetimes,
-    to_datetime,
-    to_duration,
-)
+from akkudoktoreos.utils.datetimeutil import compare_datetimes, to_datetime, to_duration
+
+logger = get_logger(__name__)
+
 
 # ---------------------------------
 # In-Memory Caching Functionality
@@ -48,28 +46,25 @@ from akkudoktoreos.utils.datetimeutil import (
 TCallable = TypeVar("TCallable", bound=Callable[..., Any])
 
 
-def cache_energy_management_store_callback(event: int, key: Any, value: Any) -> None:
-    """Calback function for CacheEnergyManagementStore."""
-    CacheEnergyManagementStore.last_event = event
-    CacheEnergyManagementStore.last_key = key
-    CacheEnergyManagementStore.last_value = value
+def cache_until_update_store_callback(event: int, key: Any, value: Any) -> None:
+    """Calback function for CacheUntilUpdateStore."""
+    CacheUntilUpdateStore.last_event = event
+    CacheUntilUpdateStore.last_key = key
+    CacheUntilUpdateStore.last_value = value
     if event == cachebox.EVENT_MISS:
-        CacheEnergyManagementStore.miss_count += 1
+        CacheUntilUpdateStore.miss_count += 1
     elif event == cachebox.EVENT_HIT:
-        CacheEnergyManagementStore.hit_count += 1
+        CacheUntilUpdateStore.hit_count += 1
     else:
         # unreachable code
         raise NotImplementedError
 
 
-class CacheEnergyManagementStore(SingletonMixin):
+class CacheUntilUpdateStore(SingletonMixin):
     """Singleton-based in-memory LRU (Least Recently Used) cache.
 
     This cache is shared across the application to store results of decorated
-    methods or functions during energy management runs.
-
-    Energy management tasks shall clear the cache at the start of the energy management
-    task.
+    methods or functions until the next EMS (Energy Management System) update.
 
     The cache uses an LRU eviction strategy, storing up to 100 items, with the oldest
     items being evicted once the cache reaches its capacity.
@@ -83,14 +78,14 @@ class CacheEnergyManagementStore(SingletonMixin):
     miss_count: ClassVar[int] = 0
 
     def __init__(self, *args: Any, **kwargs: Any) -> None:
-        """Initializes the `CacheEnergyManagementStore` instance with default parameters.
+        """Initializes the `CacheUntilUpdateStore` instance with default parameters.
 
         The cache uses an LRU eviction strategy with a maximum size of 100 items.
         This cache is a singleton, meaning only one instance will exist throughout
         the application lifecycle.
 
         Example:
-            >>> cache = CacheEnergyManagementStore()
+            >>> cache = CacheUntilUpdateStore()
         """
         if hasattr(self, "_initialized"):
             return
@@ -136,7 +131,7 @@ class CacheEnergyManagementStore(SingletonMixin):
         Example:
             >>> value = cache["user_data"]
         """
-        return CacheEnergyManagementStore.cache[key]
+        return CacheUntilUpdateStore.cache[key]
 
     def __setitem__(self, key: Any, value: Any) -> None:
         """Stores an item in the cache.
@@ -148,15 +143,15 @@ class CacheEnergyManagementStore(SingletonMixin):
         Example:
             >>> cache["user_data"] = {"name": "Alice", "age": 30}
         """
-        CacheEnergyManagementStore.cache[key] = value
+        CacheUntilUpdateStore.cache[key] = value
 
     def __len__(self) -> int:
         """Returns the number of items in the cache."""
-        return len(CacheEnergyManagementStore.cache)
+        return len(CacheUntilUpdateStore.cache)
 
     def __repr__(self) -> str:
-        """Provides a string representation of the CacheEnergyManagementStore object."""
-        return repr(CacheEnergyManagementStore.cache)
+        """Provides a string representation of the CacheUntilUpdateStore object."""
+        return repr(CacheUntilUpdateStore.cache)
 
     def clear(self) -> None:
         """Clears the cache, removing all stored items.
@@ -169,22 +164,22 @@ class CacheEnergyManagementStore(SingletonMixin):
             >>> cache.clear()
         """
         if hasattr(self.cache, "clear") and callable(getattr(self.cache, "clear")):
-            CacheEnergyManagementStore.cache.clear()
-            CacheEnergyManagementStore.last_event = None
-            CacheEnergyManagementStore.last_key = None
-            CacheEnergyManagementStore.last_value = None
-            CacheEnergyManagementStore.miss_count = 0
-            CacheEnergyManagementStore.hit_count = 0
+            CacheUntilUpdateStore.cache.clear()
+            CacheUntilUpdateStore.last_event = None
+            CacheUntilUpdateStore.last_key = None
+            CacheUntilUpdateStore.last_value = None
+            CacheUntilUpdateStore.miss_count = 0
+            CacheUntilUpdateStore.hit_count = 0
         else:
             raise AttributeError(f"'{self.cache.__class__.__name__}' object has no method 'clear'")
 
 
-def cachemethod_energy_management(method: TCallable) -> TCallable:
+def cachemethod_until_update(method: TCallable) -> TCallable:
     """Decorator for in memory caching the result of an instance method.
 
-    This decorator caches the method's result in `CacheEnergyManagementStore`, ensuring
+    This decorator caches the method's result in `CacheUntilUpdateStore`, ensuring
     that subsequent calls with the same arguments return the cached result until the
-    next energy management start.
+    next EMS update cycle.
 
     Args:
         method (Callable): The instance method to be decorated.
@@ -194,14 +189,14 @@ def cachemethod_energy_management(method: TCallable) -> TCallable:
 
     Example:
         >>> class MyClass:
-        >>>     @cachemethod_energy_management
+        >>>     @cachemethod_until_update
         >>>     def expensive_method(self, param: str) -> str:
         >>>         # Perform expensive computation
         >>>         return f"Computed {param}"
     """
 
     @cachebox.cachedmethod(
-        cache=CacheEnergyManagementStore().cache, callback=cache_energy_management_store_callback
+        cache=CacheUntilUpdateStore().cache, callback=cache_until_update_store_callback
     )
     @functools.wraps(method)
     def wrapper(self: Any, *args: Any, **kwargs: Any) -> Any:
@@ -211,12 +206,12 @@ def cachemethod_energy_management(method: TCallable) -> TCallable:
     return wrapper
 
 
-def cache_energy_management(func: TCallable) -> TCallable:
+def cache_until_update(func: TCallable) -> TCallable:
     """Decorator for in memory caching the result of a standalone function.
 
-    This decorator caches the function's result in `CacheEnergyManagementStore`, ensuring
+    This decorator caches the function's result in `CacheUntilUpdateStore`, ensuring
     that subsequent calls with the same arguments return the cached result until the
-    next energy management start.
+    next EMS update cycle.
 
     Args:
         func (Callable): The function to be decorated.
@@ -232,7 +227,7 @@ def cache_energy_management(func: TCallable) -> TCallable:
     """
 
     @cachebox.cached(
-        cache=CacheEnergyManagementStore().cache, callback=cache_energy_management_store_callback
+        cache=CacheUntilUpdateStore().cache, callback=cache_until_update_store_callback
     )
     @functools.wraps(func)
     def wrapper(*args: Any, **kwargs: Any) -> Any:
@@ -443,7 +438,7 @@ class CacheFileStore(ConfigMixin, SingletonMixin):
                     )
 
                 logger.debug(
-                    f"Search: ttl:{ttl_duration}, until:{until_datetime}, at:{at_datetime}, before:{before_datetime} -> hit: {generated_key == cache_file_key}, item: {cache_item.cache_file.seek(0), cache_item.cache_file.read()[:10]}..."
+                    f"Search: ttl:{ttl_duration}, until:{until_datetime}, at:{at_datetime}, before:{before_datetime} -> hit: {generated_key == cache_file_key}, item: {cache_item.cache_file.seek(0), cache_item.cache_file.read()}"
                 )
 
                 if generated_key == cache_file_key:
@@ -961,7 +956,7 @@ def cache_in_file(
                     logger.debug("Used cache file for function: " + func.__name__)
                     cache_file.seek(0)
                     if "b" in mode:
-                        result = pickle.load(cache_file)  # noqa: S301
+                        result = pickle.load(cache_file)
                     else:
                         result = cache_file.read()
                 except Exception as e:

src/akkudoktoreos/core/coreabc.py

@@ -13,14 +13,17 @@ Classes:
 import threading
 from typing import Any, ClassVar, Dict, Optional, Type
 
-from loguru import logger
+from pendulum import DateTime
+from pydantic import computed_field
 
-from akkudoktoreos.core.decorators import classproperty
-from akkudoktoreos.utils.datetimeutil import DateTime
+from akkudoktoreos.core.logging import get_logger
+
+logger = get_logger(__name__)
 
 config_eos: Any = None
 measurement_eos: Any = None
 prediction_eos: Any = None
+devices_eos: Any = None
 ems_eos: Any = None
 
 
@@ -46,9 +49,9 @@ class ConfigMixin:
         ```
     """
 
-    @classproperty
-    def config(cls) -> Any:
-        """Convenience class method/ attribute to retrieve the EOS configuration data.
+    @property
+    def config(self) -> Any:
+        """Convenience method/ attribute to retrieve the EOS configuration data.
 
         Returns:
             ConfigEOS: The configuration.
@@ -86,9 +89,9 @@ class MeasurementMixin:
         ```
     """
 
-    @classproperty
-    def measurement(cls) -> Any:
-        """Convenience class method/ attribute to retrieve the EOS measurement data.
+    @property
+    def measurement(self) -> Any:
+        """Convenience method/ attribute to retrieve the EOS measurement data.
 
         Returns:
             Measurement: The measurement.
@@ -126,9 +129,9 @@ class PredictionMixin:
         ```
     """
 
-    @classproperty
-    def prediction(cls) -> Any:
-        """Convenience class method/ attribute to retrieve the EOS prediction data.
+    @property
+    def prediction(self) -> Any:
+        """Convenience method/ attribute to retrieve the EOS prediction data.
 
         Returns:
             Prediction: The prediction.
@@ -143,6 +146,46 @@ class PredictionMixin:
         return prediction_eos
 
 
+class DevicesMixin:
+    """Mixin class for managing EOS devices simulation data.
+
+    This class serves as a foundational component for EOS-related classes requiring access
+    to global devices simulation data. It provides a `devices` property that dynamically retrieves
+    the devices instance, ensuring up-to-date access to devices simulation results.
+
+    Usage:
+        Subclass this base class to gain access to the `devices` attribute, which retrieves the
+        global devices instance lazily to avoid import-time circular dependencies.
+
+    Attributes:
+        devices (Devices): Property to access the global EOS devices simulation data.
+
+    Example:
+        ```python
+        class MyOptimizationClass(DevicesMixin):
+            def analyze_mydevicesimulation(self):
+                device_simulation_data = self.devices.mydevicesresult
+                # Perform analysis
+        ```
+    """
+
+    @property
+    def devices(self) -> Any:
+        """Convenience method/ attribute to retrieve the EOS devices simulation data.
+
+        Returns:
+            Devices: The devices simulation.
+        """
+        # avoid circular dependency at import time
+        global devices_eos
+        if devices_eos is None:
+            from akkudoktoreos.devices.devices import get_devices
+
+            devices_eos = get_devices()
+
+        return devices_eos
+
+
 class EnergyManagementSystemMixin:
     """Mixin class for managing EOS energy management system.
 
@@ -167,9 +210,9 @@ class EnergyManagementSystemMixin:
         ```
     """
 
-    @classproperty
-    def ems(cls) -> Any:
-        """Convenience class method/ attribute to retrieve the EOS energy management system.
+    @property
+    def ems(self) -> Any:
+        """Convenience method/ attribute to retrieve the EOS energy management system.
 
         Returns:
             EnergyManagementSystem: The energy management system.
@@ -191,21 +234,16 @@ class StartMixin(EnergyManagementSystemMixin):
         - `start_datetime`: The starting datetime of the current or latest energy management.
     """
 
-    @classproperty
-    def ems_start_datetime(cls) -> Optional[DateTime]:
-        """Convenience class method/ attribute to retrieve the start datetime of the current or latest energy management.
+    # Computed field for start_datetime
+    @computed_field  # type: ignore[prop-decorator]
+    @property
+    def start_datetime(self) -> Optional[DateTime]:
+        """Returns the start datetime of the current or latest energy management.
 
         Returns:
             DateTime: The starting datetime of the current or latest energy management, or None.
         """
-        # avoid circular dependency at import time
-        global ems_eos
-        if ems_eos is None:
-            from akkudoktoreos.core.ems import get_ems
-
-            ems_eos = get_ems()
-
-        return ems_eos.start_datetime
+        return self.ems.start_datetime
 
 
 class SingletonMixin:

src/akkudoktoreos/core/dataabc.py

@@ -14,23 +14,13 @@ from abc import abstractmethod
 from collections.abc import MutableMapping, MutableSequence
 from itertools import chain
 from pathlib import Path
-from typing import (
-    Any,
-    Dict,
-    Iterator,
-    List,
-    Optional,
-    Tuple,
-    Type,
-    Union,
-    overload,
-)
+from typing import Any, Dict, Iterator, List, Optional, Tuple, Type, Union, overload
 
 import numpy as np
 import pandas as pd
 import pendulum
-from loguru import logger
 from numpydantic import NDArray, Shape
+from pendulum import DateTime, Duration
 from pydantic import (
     AwareDatetime,
     ConfigDict,
@@ -38,22 +28,18 @@ from pydantic import (
     ValidationError,
     computed_field,
     field_validator,
-    model_validator,
 )
 
 from akkudoktoreos.core.coreabc import ConfigMixin, SingletonMixin, StartMixin
+from akkudoktoreos.core.logging import get_logger
 from akkudoktoreos.core.pydantic import (
     PydanticBaseModel,
     PydanticDateTimeData,
     PydanticDateTimeDataFrame,
 )
-from akkudoktoreos.utils.datetimeutil import (
-    DateTime,
-    Duration,
-    compare_datetimes,
-    to_datetime,
-    to_duration,
-)
+from akkudoktoreos.utils.datetimeutil import compare_datetimes, to_datetime, to_duration
+
+logger = get_logger(__name__)
 
 
 class DataBase(ConfigMixin, StartMixin, PydanticBaseModel):
@@ -71,11 +57,6 @@ class DataRecord(DataBase, MutableMapping):
     Fields can be accessed and mutated both using dictionary-style access (`record['field_name']`)
     and attribute-style access (`record.field_name`).
 
-    The data record also provides configured field like data. Configuration has to be done by the
-    derived class. Configuration is a list of key strings, which is usually taken from the EOS
-    configuration. The internal field for these data `configured_data` is mostly hidden from
-    dictionary-style and attribute-style access.
-
     Attributes:
         date_time (Optional[DateTime]): Aware datetime indicating when the data record applies.
 
@@ -86,42 +67,9 @@ class DataRecord(DataBase, MutableMapping):
 
     date_time: Optional[DateTime] = Field(default=None, description="DateTime")
 
-    configured_data: dict[str, Any] = Field(
-        default_factory=dict,
-        description="Configured field like data",
-        examples=[{"load0_mr": 40421}],
-    )
-
     # Pydantic v2 model configuration
     model_config = ConfigDict(arbitrary_types_allowed=True, populate_by_name=True)
 
-    @model_validator(mode="before")
-    @classmethod
-    def init_configured_field_like_data(cls, data: Any) -> Any:
-        """Extracts configured data keys from the input and assigns them to `configured_data`.
-
-        This validator is called before the model is initialized. It filters out any keys from the input
-        dictionary that are listed in the configured data keys, and moves them into
-        the `configured_data` field of the model. This enables flexible, key-driven population of
-        dynamic data while keeping the model schema clean.
-
-        Args:
-            data (Any): The raw input data used to initialize the model.
-
-        Returns:
-            Any: The modified input data dictionary, with configured keys moved to `configured_data`.
-        """
-        if not isinstance(data, dict):
-            return data
-
-        configured_keys: Union[list[str], set] = cls.configured_data_keys() or set()
-        extracted = {k: data.pop(k) for k in list(data.keys()) if k in configured_keys}
-
-        if extracted:
-            data.setdefault("configured_data", {}).update(extracted)
-
-        return data
-
     @field_validator("date_time", mode="before")
     @classmethod
     def transform_to_datetime(cls, value: Any) -> Optional[DateTime]:
@@ -131,39 +79,18 @@ class DataRecord(DataBase, MutableMapping):
             return None
         return to_datetime(value)
 
-    @classmethod
-    def configured_data_keys(cls) -> Optional[list[str]]:
-        """Return the keys for the configured field like data.
-
-        Can be overwritten by derived classes to define specific field like data. Usually provided
-        by configuration data.
-        """
-        return None
-
     @classmethod
     def record_keys(cls) -> List[str]:
         """Returns the keys of all fields in the data record."""
         key_list = []
         key_list.extend(list(cls.model_fields.keys()))
         key_list.extend(list(cls.__pydantic_decorators__.computed_fields.keys()))
-        # Add also keys that may be added by configuration
-        key_list.remove("configured_data")
-        configured_keys = cls.configured_data_keys()
-        if configured_keys is not None:
-            key_list.extend(configured_keys)
         return key_list
 
     @classmethod
     def record_keys_writable(cls) -> List[str]:
         """Returns the keys of all fields in the data record that are writable."""
-        keys_writable = []
-        keys_writable.extend(list(cls.model_fields.keys()))
-        # Add also keys that may be added by configuration
-        keys_writable.remove("configured_data")
-        configured_keys = cls.configured_data_keys()
-        if configured_keys is not None:
-            keys_writable.extend(configured_keys)
-        return keys_writable
+        return list(cls.model_fields.keys())
 
     def _validate_key_writable(self, key: str) -> None:
         """Verify that a specified key exists and is writable in the current record keys.
@@ -179,40 +106,6 @@ class DataRecord(DataBase, MutableMapping):
                 f"Key '{key}' is not in writable record keys: {self.record_keys_writable()}"
             )
 
-    def __dir__(self) -> list[str]:
-        """Extend the default `dir()` output to include configured field like data keys.
-
-        This enables editor auto-completion and interactive introspection, while hiding the internal
-        `configured_data` dictionary.
-
-        This ensures the configured field like data values appear like native fields,
-        in line with the base model's attribute behavior.
-        """
-        base = super().__dir__()
-        keys = set(base)
-        # Expose configured data keys as attributes
-        configured_keys = self.configured_data_keys()
-        if configured_keys is not None:
-            keys.update(configured_keys)
-        # Explicitly hide the 'configured_data' internal dict
-        keys.discard("configured_data")
-        return sorted(keys)
-
-    def __eq__(self, other: Any) -> bool:
-        """Ensure equality comparison includes the contents of the `configured_data` dict.
-
-        Contents of the `configured_data` dict are in addition to the base model fields.
-        """
-        if not isinstance(other, self.__class__):
-            return NotImplemented
-        # Compare all fields except `configured_data`
-        if self.model_dump(exclude={"configured_data"}) != other.model_dump(
-            exclude={"configured_data"}
-        ):
-            return False
-        # Compare `configured_data` explicitly
-        return self.configured_data == other.configured_data
-
     def __getitem__(self, key: str) -> Any:
         """Retrieve the value of a field by key name.
 
@@ -225,11 +118,9 @@ class DataRecord(DataBase, MutableMapping):
         Raises:
             KeyError: If the specified key does not exist.
         """
-        try:
-            # Let getattr do the work
-            return self.__getattr__(key)
-        except:
-            raise KeyError(f"'{key}' not found in the record fields.")
+        if key in self.model_fields:
+            return getattr(self, key)
+        raise KeyError(f"'{key}' not found in the record fields.")
 
     def __setitem__(self, key: str, value: Any) -> None:
         """Set the value of a field by key name.
@@ -241,10 +132,9 @@ class DataRecord(DataBase, MutableMapping):
         Raises:
             KeyError: If the specified key does not exist in the fields.
         """
-        try:
-            # Let setattr do the work
-            self.__setattr__(key, value)
-        except:
+        if key in self.model_fields:
+            setattr(self, key, value)
+        else:
             raise KeyError(f"'{key}' is not a recognized field.")
 
     def __delitem__(self, key: str) -> None:
@@ -256,9 +146,9 @@ class DataRecord(DataBase, MutableMapping):
         Raises:
             KeyError: If the specified key does not exist in the fields.
         """
-        try:
-            self.__delattr__(key)
-        except:
+        if key in self.model_fields:
+            setattr(self, key, None)  # Optional: set to None instead of deleting
+        else:
             raise KeyError(f"'{key}' is not a recognized field.")
 
     def __iter__(self) -> Iterator[str]:
@@ -267,7 +157,7 @@ class DataRecord(DataBase, MutableMapping):
         Returns:
             Iterator[str]: An iterator over field names.
         """
-        return iter(self.record_keys_writable())
+        return iter(self.model_fields)
 
     def __len__(self) -> int:
         """Return the number of fields in the data record.
@@ -275,7 +165,7 @@ class DataRecord(DataBase, MutableMapping):
         Returns:
             int: The number of defined fields.
         """
-        return len(self.record_keys_writable())
+        return len(self.model_fields)
 
     def __repr__(self) -> str:
         """Provide a string representation of the data record.
@@ -283,7 +173,7 @@ class DataRecord(DataBase, MutableMapping):
         Returns:
             str: A string representation showing field names and their values.
         """
-        field_values = {field: getattr(self, field) for field in self.__class__.model_fields}
+        field_values = {field: getattr(self, field) for field in self.model_fields}
         return f"{self.__class__.__name__}({field_values})"
 
     def __getattr__(self, key: str) -> Any:
@@ -298,13 +188,8 @@ class DataRecord(DataBase, MutableMapping):
         Raises:
             AttributeError: If the field does not exist.
         """
-        if key in self.__class__.model_fields:
+        if key in self.model_fields:
             return getattr(self, key)
-        if key in self.configured_data.keys():
-            return self.configured_data[key]
-        configured_keys = self.configured_data_keys()
-        if configured_keys is not None and key in configured_keys:
-            return None
         raise AttributeError(f"'{self.__class__.__name__}' object has no attribute '{key}'")
 
     def __setattr__(self, key: str, value: Any) -> None:
@@ -317,14 +202,10 @@ class DataRecord(DataBase, MutableMapping):
         Raises:
             AttributeError: If the attribute/field does not exist.
         """
-        if key in self.__class__.model_fields:
+        if key in self.model_fields:
             super().__setattr__(key, value)
-            return
-        configured_keys = self.configured_data_keys()
-        if configured_keys is not None and key in configured_keys:
-            self.configured_data[key] = value
-            return
-        raise AttributeError(f"'{self.__class__.__name__}' object has no attribute '{key}'")
+        else:
+            raise AttributeError(f"'{self.__class__.__name__}' object has no attribute '{key}'")
 
     def __delattr__(self, key: str) -> None:
         """Delete an attribute by setting it to None if it exists as a field.
@@ -335,21 +216,10 @@ class DataRecord(DataBase, MutableMapping):
         Raises:
             AttributeError: If the attribute/field does not exist.
         """
-        if key in self.__class__.model_fields:
-            data: Optional[dict]
-            if key == "configured_data":
-                data = dict()
-            else:
-                data = None
-            setattr(self, key, data)
-            return
-        if key in self.configured_data:
-            del self.configured_data[key]
-            return
-        configured_keys = self.configured_data_keys()
-        if configured_keys is not None and key in configured_keys:
-            return
-        super().__delattr__(key)
+        if key in self.model_fields:
+            setattr(self, key, None)  # Optional: set to None instead of deleting
+        else:
+            super().__delattr__(key)
 
     @classmethod
     def key_from_description(cls, description: str, threshold: float = 0.8) -> Optional[str]:
@@ -484,7 +354,10 @@ class DataSequence(DataBase, MutableSequence):
     @property
     def record_keys(self) -> List[str]:
         """Returns the keys of all fields in the data records."""
-        return self.record_class().record_keys()
+        key_list = []
+        key_list.extend(list(self.record_class().model_fields.keys()))
+        key_list.extend(list(self.record_class().__pydantic_decorators__.computed_fields.keys()))
+        return key_list
 
     @computed_field  # type: ignore[prop-decorator]
     @property
@@ -498,7 +371,7 @@ class DataSequence(DataBase, MutableSequence):
         Returns:
             List[str]: A list of field keys that are writable in the data records.
         """
-        return self.record_class().record_keys_writable()
+        return list(self.record_class().model_fields.keys())
 
     @classmethod
     def record_class(cls) -> Type:
@@ -836,38 +709,6 @@ class DataSequence(DataBase, MutableSequence):
 
         return filtered_data
 
-    def key_to_value(self, key: str, target_datetime: DateTime) -> Optional[float]:
-        """Returns the value corresponding to the specified key that is nearest to the given datetime.
-
-        Args:
-            key (str): The key of the attribute in DataRecord to extract.
-            target_datetime (datetime): The datetime to search nearest to.
-
-        Returns:
-            Optional[float]: The value nearest to the given datetime, or None if no valid records are found.
-
-        Raises:
-            KeyError: If the specified key is not found in any of the DataRecords.
-        """
-        self._validate_key(key)
-
-        # Filter out records with None or NaN values for the key
-        valid_records = [
-            record
-            for record in self.records
-            if record.date_time is not None
-            and getattr(record, key, None) not in (None, float("nan"))
-        ]
-
-        if not valid_records:
-            return None
-
-        # Find the record with datetime nearest to target_datetime
-        target = to_datetime(target_datetime)
-        nearest_record = min(valid_records, key=lambda r: abs(r.date_time - target))
-
-        return getattr(nearest_record, key, None)
-
     def key_to_lists(
         self,
         key: str,
@@ -970,8 +811,7 @@ class DataSequence(DataBase, MutableSequence):
         dates, values = self.key_to_lists(
             key=key, start_datetime=start_datetime, end_datetime=end_datetime, dropna=dropna
         )
-        series = pd.Series(data=values, index=pd.DatetimeIndex(dates), name=key)
-        return series
+        return pd.Series(data=values, index=pd.DatetimeIndex(dates), name=key)
 
     def key_from_series(self, key: str, series: pd.Series) -> None:
         """Update the DataSequence from a Pandas Series.
@@ -1029,11 +869,6 @@ class DataSequence(DataBase, MutableSequence):
             KeyError: If the specified key is not found in any of the DataRecords.
         """
         self._validate_key(key)
-
-        # General check on fill_method
-        if fill_method not in ("ffill", "bfill", "linear", "none", None):
-            raise ValueError(f"Unsupported fill method: {fill_method}")
-
         # Ensure datetime objects are normalized
         start_datetime = to_datetime(start_datetime, to_maxtime=False) if start_datetime else None
         end_datetime = to_datetime(end_datetime, to_maxtime=False) if end_datetime else None
@@ -1046,7 +881,7 @@ class DataSequence(DataBase, MutableSequence):
         values_len = len(values)
 
         if values_len < 1:
-            # No values, assume at least one value set to None
+            # No values, assume at at least one value set to None
             if start_datetime is not None:
                 dates.append(start_datetime - interval)
             else:
@@ -1068,11 +903,6 @@ class DataSequence(DataBase, MutableSequence):
                 # Truncate all values before latest value before start_datetime
                 dates = dates[start_index - 1 :]
                 values = values[start_index - 1 :]
-            # We have a start_datetime, align to start datetime
-            resample_origin = start_datetime
-        else:
-            # We do not have a start_datetime, align resample buckets to midnight of first day
-            resample_origin = "start_day"
 
         if end_datetime is not None:
             if compare_datetimes(dates[-1], end_datetime).lt:
@@ -1093,7 +923,7 @@ class DataSequence(DataBase, MutableSequence):
             if fill_method is None:
                 fill_method = "linear"
             # Resample the series to the specified interval
-            resampled = series.resample(interval, origin=resample_origin).first()
+            resampled = series.resample(interval, origin="start").first()
             if fill_method == "linear":
                 resampled = resampled.interpolate(method="linear")
             elif fill_method == "ffill":
@@ -1107,7 +937,7 @@ class DataSequence(DataBase, MutableSequence):
             if fill_method is None:
                 fill_method = "ffill"
             # Resample the series to the specified interval
-            resampled = series.resample(interval, origin=resample_origin).first()
+            resampled = series.resample(interval, origin="start").first()
             if fill_method == "ffill":
                 resampled = resampled.ffill()
             elif fill_method == "bfill":
@@ -1115,24 +945,12 @@ class DataSequence(DataBase, MutableSequence):
             elif fill_method != "none":
                 raise ValueError(f"Unsupported fill method for non-numeric data: {fill_method}")
 
-        logger.debug(
-            "Resampled for '{}' with length {}: {}...{}",
-            key,
-            len(resampled),
-            resampled[:10],
-            resampled[-10:],
-        )
-
         # Convert the resampled series to a NumPy array
         if start_datetime is not None and len(resampled) > 0:
             resampled = resampled.truncate(before=start_datetime)
         if end_datetime is not None and len(resampled) > 0:
             resampled = resampled.truncate(after=end_datetime.subtract(seconds=1))
         array = resampled.values
-        logger.debug(
-            "Array for '{}' with length {}: {}...{}", key, len(array), array[:10], array[-10:]
-        )
-
         return array
 
     def to_dataframe(
@@ -1380,7 +1198,7 @@ class DataImportMixin:
     the values. `ìnterval` may be used to define the fixed time interval between two values.
 
     On import `self.update_value(datetime, key, value)` is called which has to be provided.
-    Also `self.ems_start_datetime` may be necessary as a default in case `start_datetime`is not given.
+    Also `self.start_datetime` may be necessary as a default in case `start_datetime`is not given.
     """
 
     # Attributes required but defined elsehere.
@@ -1498,7 +1316,7 @@ class DataImportMixin:
                 raise ValueError(f"Invalid start_datetime in import data: {e}")
 
         if start_datetime is None:
-            start_datetime = self.ems_start_datetime  # type: ignore
+            start_datetime = self.start_datetime  # type: ignore
 
         if "interval" in import_data:
             try:
@@ -1589,7 +1407,7 @@ class DataImportMixin:
                 raise ValueError(f"Invalid datetime index in DataFrame: {e}")
         else:
             if start_datetime is None:
-                start_datetime = self.ems_start_datetime  # type: ignore
+                start_datetime = self.start_datetime  # type: ignore
             has_datetime_index = False
 
         # Filter columns based on key_prefix and record_keys_writable
@@ -1646,7 +1464,7 @@ class DataImportMixin:
 
         If start_datetime and or interval is given in the JSON dict it will be used. Otherwise
         the given parameters are used. If None is given start_datetime defaults to
-        'self.ems_start_datetime' and interval defaults to 1 hour.
+        'self.start_datetime' and interval defaults to 1 hour.
 
         Args:
             json_str (str): The JSON string containing the generic data.
@@ -1664,11 +1482,11 @@ class DataImportMixin:
             {
                 "start_datetime": "2024-11-10 00:00:00"
                 "interval": "30 minutes"
-                "loadforecast_power_w": [20.5, 21.0, 22.1],
+                "load_mean": [20.5, 21.0, 22.1],
                 "other_xyz: [10.5, 11.0, 12.1],
             }
             ```
-            and `key_prefix = "load"`, only the "loadforecast_power_w" key will be processed even though
+            and `key_prefix = "load"`, only the "load_mean" key will be processed even though
             both keys are in the record.
         """
         # Try pandas dataframe with orient="split"
@@ -1721,7 +1539,7 @@ class DataImportMixin:
 
         If start_datetime and or interval is given in the JSON dict it will be used. Otherwise
         the given parameters are used. If None is given start_datetime defaults to
-        'self.ems_start_datetime' and interval defaults to 1 hour.
+        'self.start_datetime' and interval defaults to 1 hour.
 
         Args:
             import_file_path (Path): The path to the JSON file containing the generic data.
@@ -1738,11 +1556,11 @@ class DataImportMixin:
             Given a JSON file with the following content:
             ```json
             {
-                "loadforecast_power_w": [20.5, 21.0, 22.1],
+                "load_mean": [20.5, 21.0, 22.1],
                 "other_xyz: [10.5, 11.0, 12.1],
             }
             ```
-            and `key_prefix = "load"`, only the "loadforecast_power_w" key will be processed even though
+            and `key_prefix = "load"`, only the "load_mean" key will be processed even though
             both keys are in the record.
         """
         with import_file_path.open("r", encoding="utf-8", newline=None) as import_file:
@@ -1932,12 +1750,7 @@ class DataContainer(SingletonMixin, DataBase, MutableMapping):
             force_update (bool, optional): If True, forces the providers to update the data even if still cached.
         """
         for provider in self.providers:
-            try:
-                provider.update_data(force_enable=force_enable, force_update=force_update)
-            except Exception as ex:
-                error = f"Provider {provider.provider_id()} fails on update - enabled={provider.enabled()}, force_enable={force_enable}, force_update={force_update}: {ex}"
-                logger.error(error)
-                raise RuntimeError(error)
+            provider.update_data(force_enable=force_enable, force_update=force_update)
 
     def key_to_series(
         self,
@@ -2042,7 +1855,7 @@ class DataContainer(SingletonMixin, DataBase, MutableMapping):
     ) -> pd.DataFrame:
         """Retrieve a dataframe indexed by fixed time intervals for specified keys from the data in each DataProvider.
 
-        Generates a pandas DataFrame using the NumPy arrays for each specified key, ensuring a common time index.
+        Generates a pandas DataFrame using the NumPy arrays for each specified key, ensuring a common time index..
 
         Args:
             keys (list[str]): A list of field names to retrieve.
@@ -2091,15 +1904,8 @@ class DataContainer(SingletonMixin, DataBase, MutableMapping):
                 end_datetime.add(seconds=1)
 
         # Create a DatetimeIndex based on start, end, and interval
-        if start_datetime is None or end_datetime is None:
-            raise ValueError(
-                f"Can not determine datetime range. Got '{start_datetime}'..'{end_datetime}'."
-            )
         reference_index = pd.date_range(
-            start=start_datetime,
-            end=end_datetime,
-            freq=interval,
-            inclusive="left",
+            start=start_datetime, end=end_datetime, freq=interval, inclusive="left"
         )
 
         data = {}

src/akkudoktoreos/core/decorators.py

@@ -1,6 +1,10 @@
 from collections.abc import Callable
 from typing import Any, Optional
 
+from akkudoktoreos.core.logging import get_logger
+
+logger = get_logger(__name__)
+
 
 class classproperty:
     """A decorator to define a read-only property at the class level.
@@ -30,7 +34,7 @@ class classproperty:
                                       argument and returns a value.
 
     Raises:
-        RuntimeError: If `fget` is not defined when `__get__` is called.
+        AssertionError: If `fget` is not defined when `__get__` is called.
     """
 
     def __init__(self, fget: Callable[[Any], Any]) -> None:
@@ -39,6 +43,5 @@ class classproperty:
     def __get__(self, _: Any, owner_cls: Optional[type[Any]] = None) -> Any:
         if owner_cls is None:
             return self
-        if self.fget is None:
-            raise RuntimeError("'fget' not defined when `__get__` is called")
+        assert self.fget is not None
         return self.fget(owner_cls)

src/akkudoktoreos/core/ems.py

@@ -1,50 +1,124 @@
-import traceback
-from asyncio import Lock, get_running_loop
-from concurrent.futures import ThreadPoolExecutor
-from functools import partial
-from typing import ClassVar, Optional
+from typing import Any, ClassVar, Optional
 
-from loguru import logger
-from pydantic import computed_field
+import numpy as np
+from numpydantic import NDArray, Shape
+from pendulum import DateTime
+from pydantic import ConfigDict, Field, computed_field, field_validator, model_validator
+from typing_extensions import Self
 
-from akkudoktoreos.core.cache import CacheEnergyManagementStore
+from akkudoktoreos.core.cache import CacheUntilUpdateStore
 from akkudoktoreos.core.coreabc import ConfigMixin, PredictionMixin, SingletonMixin
-from akkudoktoreos.core.emplan import EnergyManagementPlan
-from akkudoktoreos.core.emsettings import EnergyManagementMode
-from akkudoktoreos.core.pydantic import PydanticBaseModel
-from akkudoktoreos.optimization.genetic.genetic import GeneticOptimization
-from akkudoktoreos.optimization.genetic.geneticparams import (
-    GeneticOptimizationParameters,
-)
-from akkudoktoreos.optimization.genetic.geneticsolution import GeneticSolution
-from akkudoktoreos.optimization.optimization import OptimizationSolution
-from akkudoktoreos.utils.datetimeutil import DateTime, compare_datetimes, to_datetime
+from akkudoktoreos.core.logging import get_logger
+from akkudoktoreos.core.pydantic import ParametersBaseModel, PydanticBaseModel
+from akkudoktoreos.devices.battery import Battery
+from akkudoktoreos.devices.generic import HomeAppliance
+from akkudoktoreos.devices.inverter import Inverter
+from akkudoktoreos.utils.datetimeutil import compare_datetimes, to_datetime
+from akkudoktoreos.utils.utils import NumpyEncoder
 
-# The executor to execute the CPU heavy energy management run
-executor = ThreadPoolExecutor(max_workers=1)
+logger = get_logger(__name__)
+
+
+class EnergyManagementParameters(ParametersBaseModel):
+    pv_prognose_wh: list[float] = Field(
+        description="An array of floats representing the forecasted photovoltaic output in watts for different time intervals."
+    )
+    strompreis_euro_pro_wh: list[float] = Field(
+        description="An array of floats representing the electricity price in euros per watt-hour for different time intervals."
+    )
+    einspeiseverguetung_euro_pro_wh: list[float] | float = Field(
+        description="A float or array of floats representing the feed-in compensation in euros per watt-hour."
+    )
+    preis_euro_pro_wh_akku: float = Field(
+        description="A float representing the cost of battery energy per watt-hour."
+    )
+    gesamtlast: list[float] = Field(
+        description="An array of floats representing the total load (consumption) in watts for different time intervals."
+    )
+
+    @model_validator(mode="after")
+    def validate_list_length(self) -> Self:
+        pv_prognose_length = len(self.pv_prognose_wh)
+        if (
+            pv_prognose_length != len(self.strompreis_euro_pro_wh)
+            or pv_prognose_length != len(self.gesamtlast)
+            or (
+                isinstance(self.einspeiseverguetung_euro_pro_wh, list)
+                and pv_prognose_length != len(self.einspeiseverguetung_euro_pro_wh)
+            )
+        ):
+            raise ValueError("Input lists have different lengths")
+        return self
+
+
+class SimulationResult(ParametersBaseModel):
+    """This object contains the results of the simulation and provides insights into various parameters over the entire forecast period."""
+
+    Last_Wh_pro_Stunde: list[Optional[float]] = Field(description="TBD")
+    EAuto_SoC_pro_Stunde: list[Optional[float]] = Field(
+        description="The state of charge of the EV for each hour."
+    )
+    Einnahmen_Euro_pro_Stunde: list[Optional[float]] = Field(
+        description="The revenue from grid feed-in or other sources in euros per hour."
+    )
+    Gesamt_Verluste: float = Field(
+        description="The total losses in watt-hours over the entire period."
+    )
+    Gesamtbilanz_Euro: float = Field(
+        description="The total balance of revenues minus costs in euros."
+    )
+    Gesamteinnahmen_Euro: float = Field(description="The total revenues in euros.")
+    Gesamtkosten_Euro: float = Field(description="The total costs in euros.")
+    Home_appliance_wh_per_hour: list[Optional[float]] = Field(
+        description="The energy consumption of a household appliance in watt-hours per hour."
+    )
+    Kosten_Euro_pro_Stunde: list[Optional[float]] = Field(
+        description="The costs in euros per hour."
+    )
+    Netzbezug_Wh_pro_Stunde: list[Optional[float]] = Field(
+        description="The grid energy drawn in watt-hours per hour."
+    )
+    Netzeinspeisung_Wh_pro_Stunde: list[Optional[float]] = Field(
+        description="The energy fed into the grid in watt-hours per hour."
+    )
+    Verluste_Pro_Stunde: list[Optional[float]] = Field(
+        description="The losses in watt-hours per hour."
+    )
+    akku_soc_pro_stunde: list[Optional[float]] = Field(
+        description="The state of charge of the battery (not the EV) in percentage per hour."
+    )
+    Electricity_price: list[Optional[float]] = Field(
+        description="Used Electricity Price, including predictions"
+    )
+
+    @field_validator(
+        "Last_Wh_pro_Stunde",
+        "Netzeinspeisung_Wh_pro_Stunde",
+        "akku_soc_pro_stunde",
+        "Netzbezug_Wh_pro_Stunde",
+        "Kosten_Euro_pro_Stunde",
+        "Einnahmen_Euro_pro_Stunde",
+        "EAuto_SoC_pro_Stunde",
+        "Verluste_Pro_Stunde",
+        "Home_appliance_wh_per_hour",
+        "Electricity_price",
+        mode="before",
+    )
+    def convert_numpy(cls, field: Any) -> Any:
+        return NumpyEncoder.convert_numpy(field)[0]
 
 
 class EnergyManagement(SingletonMixin, ConfigMixin, PredictionMixin, PydanticBaseModel):
-    """Energy management."""
+    # Disable validation on assignment to speed up simulation runs.
+    model_config = ConfigDict(
+        validate_assignment=False,
+    )
 
     # Start datetime.
     _start_datetime: ClassVar[Optional[DateTime]] = None
 
     # last run datetime. Used by energy management task
-    _last_run_datetime: ClassVar[Optional[DateTime]] = None
-
-    # energy management plan of latest energy management run with optimization
-    _plan: ClassVar[Optional[EnergyManagementPlan]] = None
-
-    # opimization solution of the latest energy management run
-    _optimization_solution: ClassVar[Optional[OptimizationSolution]] = None
-
-    # Solution of the genetic algorithm of latest energy management run with optimization
-    # For classic API
-    _genetic_solution: ClassVar[Optional[GeneticSolution]] = None
-
-    # energy management lock (for energy management run)
-    _run_lock: ClassVar[Lock] = Lock()
+    _last_datetime: ClassVar[Optional[DateTime]] = None
 
     @computed_field  # type: ignore[prop-decorator]
     @property
@@ -54,15 +128,9 @@ class EnergyManagement(SingletonMixin, ConfigMixin, PredictionMixin, PydanticBas
             EnergyManagement.set_start_datetime()
         return EnergyManagement._start_datetime
 
-    @computed_field  # type: ignore[prop-decorator]
-    @property
-    def last_run_datetime(self) -> Optional[DateTime]:
-        """The datetime the last energy management was run."""
-        return EnergyManagement._last_run_datetime
-
     @classmethod
     def set_start_datetime(cls, start_datetime: Optional[DateTime] = None) -> DateTime:
-        """Set the start datetime for the next energy management run.
+        """Set the start datetime for the next energy management cycle.
 
         If no datetime is provided, the current datetime is used.
 
@@ -81,208 +149,140 @@ class EnergyManagement(SingletonMixin, ConfigMixin, PredictionMixin, PydanticBas
         cls._start_datetime = start_datetime.set(minute=0, second=0, microsecond=0)
         return cls._start_datetime
 
-    @classmethod
-    def plan(cls) -> Optional[EnergyManagementPlan]:
-        """Get the latest energy management plan.
+    # -------------------------
+    # TODO: Take from prediction
+    # -------------------------
 
-        Returns:
-            Optional[EnergyManagementPlan]: The latest energy management plan or None.
-        """
-        return cls._plan
+    load_energy_array: Optional[NDArray[Shape["*"], float]] = Field(
+        default=None,
+        description="An array of floats representing the total load (consumption) in watts for different time intervals.",
+    )
+    pv_prediction_wh: Optional[NDArray[Shape["*"], float]] = Field(
+        default=None,
+        description="An array of floats representing the forecasted photovoltaic output in watts for different time intervals.",
+    )
+    elect_price_hourly: Optional[NDArray[Shape["*"], float]] = Field(
+        default=None,
+        description="An array of floats representing the electricity price in euros per watt-hour for different time intervals.",
+    )
+    elect_revenue_per_hour_arr: Optional[NDArray[Shape["*"], float]] = Field(
+        default=None,
+        description="An array of floats representing the feed-in compensation in euros per watt-hour.",
+    )
 
-    @classmethod
-    def optimization_solution(cls) -> Optional[OptimizationSolution]:
-        """Get the latest optimization solution.
+    # -------------------------
+    # TODO: Move to devices
+    # -------------------------
 
-        Returns:
-            Optional[OptimizationSolution]: The latest optimization solution.
-        """
-        return cls._optimization_solution
+    battery: Optional[Battery] = Field(default=None, description="TBD.")
+    ev: Optional[Battery] = Field(default=None, description="TBD.")
+    home_appliance: Optional[HomeAppliance] = Field(default=None, description="TBD.")
+    inverter: Optional[Inverter] = Field(default=None, description="TBD.")
 
-    @classmethod
-    def genetic_solution(cls) -> Optional[GeneticSolution]:
-        """Get the latest solution of the genetic algorithm.
+    # -------------------------
+    # TODO: Move to devices
+    # -------------------------
 
-        Returns:
-            Optional[GeneticSolution]: The latest solution of the genetic algorithm.
-        """
-        return cls._genetic_solution
+    ac_charge_hours: Optional[NDArray[Shape["*"], float]] = Field(default=None, description="TBD")
+    dc_charge_hours: Optional[NDArray[Shape["*"], float]] = Field(default=None, description="TBD")
+    ev_charge_hours: Optional[NDArray[Shape["*"], float]] = Field(default=None, description="TBD")
 
-    @classmethod
-    def _run(
-        cls,
-        start_datetime: Optional[DateTime] = None,
-        mode: Optional[EnergyManagementMode] = None,
-        genetic_parameters: Optional[GeneticOptimizationParameters] = None,
-        genetic_individuals: Optional[int] = None,
-        genetic_seed: Optional[int] = None,
-        force_enable: Optional[bool] = False,
-        force_update: Optional[bool] = False,
-    ) -> None:
-        """Run the energy management.
-
-        This method initializes the energy management run by setting its
-        start datetime, updating predictions, and optionally starting
-        optimization depending on the selected mode or configuration.
-
-        Args:
-            start_datetime (DateTime, optional): The starting timestamp
-                of the energy management run. Defaults to the current datetime
-                if not provided.
-            mode (EnergyManagementMode, optional): The management mode to use. Must be one of:
-                - "OPTIMIZATION": Runs the optimization process.
-                - "PREDICTION": Updates the forecast without optimization.
-
-                Defaults to the mode defined in the current configuration.
-            genetic_parameters (GeneticOptimizationParameters, optional): The
-                parameter set for the genetic algorithm. If not provided, it will
-                be constructed based on the current configuration and predictions.
-            genetic_individuals (int, optional): The number of individuals for the
-                genetic algorithm. Defaults to the algorithm's internal default (400)
-                if not specified.
-            genetic_seed (int, optional): The seed for the genetic algorithm. Defaults
-                to the algorithm's internal random seed if not specified.
-            force_enable (bool, optional): If True, bypasses any disabled state
-                to force the update process. This is mostly applicable to
-                prediction providers.
-            force_update (bool, optional): If True, forces data to be refreshed
-                even if a cached version is still valid.
-
-        Returns:
-            None
-        """
-        # Ensure there is only one optimization/ energy management run at a time
-        if mode not in (None, "PREDICTION", "OPTIMIZATION"):
-            raise ValueError(f"Unknown energy management mode {mode}.")
-
-        logger.info("Starting energy management run.")
-
-        # Remember/ set the start datetime of this energy management run.
-        # None leads
-        cls.set_start_datetime(start_datetime)
-
-        # Throw away any memory cached results of the last energy management run.
-        CacheEnergyManagementStore().clear()
-
-        if mode is None:
-            mode = cls.config.ems.mode
-        if mode is None or mode == "PREDICTION":
-            # Update the predictions
-            cls.prediction.update_data(force_enable=force_enable, force_update=force_update)
-            logger.info("Energy management run done (predictions updated)")
+    def __init__(self, *args: Any, **kwargs: Any) -> None:
+        if hasattr(self, "_initialized"):
             return
+        super().__init__(*args, **kwargs)
 
-        # Prepare optimization parameters
-        # This also creates default configurations for missing values and updates the predictions
-        logger.info(
-            "Starting energy management prediction update and optimzation parameter preparation."
-        )
-        if genetic_parameters is None:
-            genetic_parameters = GeneticOptimizationParameters.prepare()
-
-        if not genetic_parameters:
-            logger.error(
-                "Energy management run canceled. Could not prepare optimisation parameters."
-            )
-            return
-
-        # Take values from config if not given
-        if genetic_individuals is None:
-            genetic_individuals = cls.config.optimization.genetic.individuals
-        if genetic_seed is None:
-            genetic_seed = cls.config.optimization.genetic.seed
-
-        if cls._start_datetime is None:  # Make mypy happy - already set by us
-            raise RuntimeError("Start datetime not set.")
-
-        logger.info("Starting energy management optimization.")
-        try:
-            optimization = GeneticOptimization(
-                verbose=bool(cls.config.server.verbose),
-                fixed_seed=genetic_seed,
-            )
-            solution = optimization.optimierung_ems(
-                start_hour=cls._start_datetime.hour,
-                parameters=genetic_parameters,
-                ngen=genetic_individuals,
-            )
-        except:
-            logger.exception("Energy management optimization failed.")
-            return
-
-        # Make genetic solution public
-        cls._genetic_solution = solution
-
-        # Make optimization solution public
-        cls._optimization_solution = solution.optimization_solution()
-
-        # Make plan public
-        cls._plan = solution.energy_management_plan()
-
-        logger.debug("Energy management genetic solution:\n{}", cls._genetic_solution)
-        logger.debug("Energy management optimization solution:\n{}", cls._optimization_solution)
-        logger.debug("Energy management plan:\n{}", cls._plan)
-        logger.info("Energy management run done (optimization updated)")
-
-    async def run(
+    def set_parameters(
         self,
-        start_datetime: Optional[DateTime] = None,
-        mode: Optional[EnergyManagementMode] = None,
-        genetic_parameters: Optional[GeneticOptimizationParameters] = None,
-        genetic_individuals: Optional[int] = None,
-        genetic_seed: Optional[int] = None,
+        parameters: EnergyManagementParameters,
+        ev: Optional[Battery] = None,
+        home_appliance: Optional[HomeAppliance] = None,
+        inverter: Optional[Inverter] = None,
+    ) -> None:
+        self.load_energy_array = np.array(parameters.gesamtlast, float)
+        self.pv_prediction_wh = np.array(parameters.pv_prognose_wh, float)
+        self.elect_price_hourly = np.array(parameters.strompreis_euro_pro_wh, float)
+        self.elect_revenue_per_hour_arr = (
+            parameters.einspeiseverguetung_euro_pro_wh
+            if isinstance(parameters.einspeiseverguetung_euro_pro_wh, list)
+            else np.full(
+                len(self.load_energy_array), parameters.einspeiseverguetung_euro_pro_wh, float
+            )
+        )
+        if inverter:
+            self.battery = inverter.battery
+        else:
+            self.battery = None
+        self.ev = ev
+        self.home_appliance = home_appliance
+        self.inverter = inverter
+        self.ac_charge_hours = np.full(self.config.prediction.hours, 0.0)
+        self.dc_charge_hours = np.full(self.config.prediction.hours, 1.0)
+        self.ev_charge_hours = np.full(self.config.prediction.hours, 0.0)
+
+    def set_akku_discharge_hours(self, ds: np.ndarray) -> None:
+        if self.battery:
+            self.battery.set_discharge_per_hour(ds)
+
+    def set_akku_ac_charge_hours(self, ds: np.ndarray) -> None:
+        self.ac_charge_hours = ds
+
+    def set_akku_dc_charge_hours(self, ds: np.ndarray) -> None:
+        self.dc_charge_hours = ds
+
+    def set_ev_charge_hours(self, ds: np.ndarray) -> None:
+        self.ev_charge_hours = ds
+
+    def set_home_appliance_start(self, ds: int, global_start_hour: int = 0) -> None:
+        if self.home_appliance:
+            self.home_appliance.set_starting_time(ds, global_start_hour=global_start_hour)
+
+    def reset(self) -> None:
+        if self.ev:
+            self.ev.reset()
+        if self.battery:
+            self.battery.reset()
+
+    def run(
+        self,
+        start_hour: Optional[int] = None,
         force_enable: Optional[bool] = False,
         force_update: Optional[bool] = False,
     ) -> None:
-        """Run the energy management.
+        """Run energy management.
 
-        This method initializes the energy management run by setting its
-        start datetime, updating predictions, and optionally starting
-        optimization depending on the selected mode or configuration.
+        Sets `start_datetime` to current hour, updates the configuration and the prediction, and
+        starts simulation at current hour.
 
         Args:
-            start_datetime (DateTime, optional): The starting timestamp
-                of the energy management run. Defaults to the current datetime
-                if not provided.
-            mode (EnergyManagementMode, optional): The management mode to use. Must be one of:
-                - "OPTIMIZATION": Runs the optimization process.
-                - "PREDICTION": Updates the forecast without optimization.
-
-                Defaults to the mode defined in the current configuration.
-            genetic_parameters (GeneticOptimizationParameters, optional): The
-                parameter set for the genetic algorithm. If not provided, it will
-                be constructed based on the current configuration and predictions.
-            genetic_individuals (int, optional): The number of individuals for the
-                genetic algorithm. Defaults to the algorithm's internal default (400)
-                if not specified.
-            genetic_seed (int, optional): The seed for the genetic algorithm. Defaults
-                to the algorithm's internal random seed if not specified.
-            force_enable (bool, optional): If True, bypasses any disabled state
-                to force the update process. This is mostly applicable to
-                prediction providers.
-            force_update (bool, optional): If True, forces data to be refreshed
-                even if a cached version is still valid.
-
-        Returns:
-            None
+            start_hour (int, optional): Hour to take as start time for the energy management. Defaults
+            to now.
+            force_enable (bool, optional): If True, forces to update even if disabled. This
+            is mostly relevant to prediction providers.
+            force_update (bool, optional): If True, forces to update the data even if still cached.
         """
-        async with self._run_lock:
-            loop = get_running_loop()
-            # Create a partial function with parameters "baked in"
-            func = partial(
-                EnergyManagement._run,
-                start_datetime=start_datetime,
-                mode=mode,
-                genetic_parameters=genetic_parameters,
-                genetic_individuals=genetic_individuals,
-                genetic_seed=genetic_seed,
-                force_enable=force_enable,
-                force_update=force_update,
-            )
-            # Run optimization in background thread to avoid blocking event loop
-            await loop.run_in_executor(executor, func)
+        # Throw away any cached results of the last run.
+        CacheUntilUpdateStore().clear()
+        self.set_start_hour(start_hour=start_hour)
 
-    async def manage_energy(self) -> None:
+        # Check for run definitions
+        if self.start_datetime is None:
+            error_msg = "Start datetime unknown."
+            logger.error(error_msg)
+            raise ValueError(error_msg)
+        if self.config.prediction.hours is None:
+            error_msg = "Prediction hours unknown."
+            logger.error(error_msg)
+            raise ValueError(error_msg)
+        if self.config.optimization.hours is None:
+            error_msg = "Optimization hours unknown."
+            logger.error(error_msg)
+            raise ValueError(error_msg)
+
+        self.prediction.update_data(force_enable=force_enable, force_update=force_update)
+        # TODO: Create optimisation problem that calls into devices.update_data() for simulations.
+
+    def manage_energy(self) -> None:
         """Repeating task for managing energy.
 
         This task should be executed by the server regularly (e.g., every 10 seconds)
@@ -301,48 +301,218 @@ class EnergyManagement(SingletonMixin, ConfigMixin, PredictionMixin, PydanticBas
         Note: The task maintains the interval even if some intervals are missed.
         """
         current_datetime = to_datetime()
-        interval = self.config.ems.interval  # interval maybe changed in between
 
-        if EnergyManagement._last_run_datetime is None:
+        if EnergyManagement._last_datetime is None:
             # Never run before
             try:
-                # Remember energy run datetime.
-                EnergyManagement._last_run_datetime = current_datetime
                 # Try to run a first energy management. May fail due to config incomplete.
-                await self.run()
+                self.run()
+                # Remember energy run datetime.
+                EnergyManagement._last_datetime = current_datetime
             except Exception as e:
-                trace = "".join(traceback.TracebackException.from_exception(e).format())
-                message = f"EOS init: {e}\n{trace}"
+                message = f"EOS init: {e}"
                 logger.error(message)
             return
 
-        if interval is None or interval == float("nan"):
+        if self.config.ems.interval is None or self.config.ems.interval == float("nan"):
             # No Repetition
             return
 
         if (
-            compare_datetimes(current_datetime, EnergyManagement._last_run_datetime).time_diff
-            < interval
+            compare_datetimes(current_datetime, self._last_datetime).time_diff
+            < self.config.ems.interval
         ):
             # Wait for next run
             return
 
         try:
-            await self.run()
+            self.run()
         except Exception as e:
-            trace = "".join(traceback.TracebackException.from_exception(e).format())
-            message = f"EOS run: {e}\n{trace}"
+            message = f"EOS run: {e}"
             logger.error(message)
 
         # Remember the energy management run - keep on interval even if we missed some intervals
         while (
-            compare_datetimes(current_datetime, EnergyManagement._last_run_datetime).time_diff
-            >= interval
+            compare_datetimes(current_datetime, EnergyManagement._last_datetime).time_diff
+            >= self.config.ems.interval
         ):
-            EnergyManagement._last_run_datetime = EnergyManagement._last_run_datetime.add(
-                seconds=interval
+            EnergyManagement._last_datetime.add(seconds=self.config.ems.interval)
+
+    def set_start_hour(self, start_hour: Optional[int] = None) -> None:
+        """Sets start datetime to given hour.
+
+        Args:
+            start_hour (int, optional): Hour to take as start time for the energy management. Defaults
+            to now.
+        """
+        if start_hour is None:
+            self.set_start_datetime()
+        else:
+            start_datetime = to_datetime().set(hour=start_hour, minute=0, second=0, microsecond=0)
+            self.set_start_datetime(start_datetime)
+
+    def simulate_start_now(self) -> dict[str, Any]:
+        start_hour = to_datetime().now().hour
+        return self.simulate(start_hour)
+
+    def simulate(self, start_hour: int) -> dict[str, Any]:
+        """Simulate energy usage and costs for the given start hour.
+
+        akku_soc_pro_stunde begin of the hour, initial hour state!
+        last_wh_pro_stunde integral of last hour (end state)
+        """
+        # Check for simulation integrity
+        required_attrs = [
+            "load_energy_array",
+            "pv_prediction_wh",
+            "elect_price_hourly",
+            "ev_charge_hours",
+            "ac_charge_hours",
+            "dc_charge_hours",
+            "elect_revenue_per_hour_arr",
+        ]
+        missing_data = [
+            attr.replace("_", " ").title() for attr in required_attrs if getattr(self, attr) is None
+        ]
+
+        if missing_data:
+            logger.error("Mandatory data missing - %s", ", ".join(missing_data))
+            raise ValueError(f"Mandatory data missing: {', '.join(missing_data)}")
+
+        # Pre-fetch data
+        load_energy_array = np.array(self.load_energy_array)
+        pv_prediction_wh = np.array(self.pv_prediction_wh)
+        elect_price_hourly = np.array(self.elect_price_hourly)
+        ev_charge_hours = np.array(self.ev_charge_hours)
+        ac_charge_hours = np.array(self.ac_charge_hours)
+        dc_charge_hours = np.array(self.dc_charge_hours)
+        elect_revenue_per_hour_arr = np.array(self.elect_revenue_per_hour_arr)
+
+        # Fetch objects
+        battery = self.battery
+        assert battery  # to please mypy
+        ev = self.ev
+        home_appliance = self.home_appliance
+        inverter = self.inverter
+
+        if not (len(load_energy_array) == len(pv_prediction_wh) == len(elect_price_hourly)):
+            error_msg = f"Array sizes do not match: Load Curve = {len(load_energy_array)}, PV Forecast = {len(pv_prediction_wh)}, Electricity Price = {len(elect_price_hourly)}"
+            logger.error(error_msg)
+            raise ValueError(error_msg)
+
+        end_hour = len(load_energy_array)
+        total_hours = end_hour - start_hour
+
+        # Pre-allocate arrays for the results, optimized for speed
+        loads_energy_per_hour = np.full((total_hours), np.nan)
+        feedin_energy_per_hour = np.full((total_hours), np.nan)
+        consumption_energy_per_hour = np.full((total_hours), np.nan)
+        costs_per_hour = np.full((total_hours), np.nan)
+        revenue_per_hour = np.full((total_hours), np.nan)
+        soc_per_hour = np.full((total_hours), np.nan)
+        soc_ev_per_hour = np.full((total_hours), np.nan)
+        losses_wh_per_hour = np.full((total_hours), np.nan)
+        home_appliance_wh_per_hour = np.full((total_hours), np.nan)
+        electricity_price_per_hour = np.full((total_hours), np.nan)
+
+        # Set initial state
+        soc_per_hour[0] = battery.current_soc_percentage()
+        if ev:
+            soc_ev_per_hour[0] = ev.current_soc_percentage()
+
+        for hour in range(start_hour, end_hour):
+            hour_idx = hour - start_hour
+
+            # save begin states
+            soc_per_hour[hour_idx] = battery.current_soc_percentage()
+
+            if ev:
+                soc_ev_per_hour[hour_idx] = ev.current_soc_percentage()
+
+            # Accumulate loads and PV generation
+            consumption = load_energy_array[hour]
+            losses_wh_per_hour[hour_idx] = 0.0
+
+            # Home appliances
+            if home_appliance:
+                ha_load = home_appliance.get_load_for_hour(hour)
+                consumption += ha_load
+                home_appliance_wh_per_hour[hour_idx] = ha_load
+
+            # E-Auto handling
+            if ev and ev_charge_hours[hour] > 0:
+                loaded_energy_ev, verluste_eauto = ev.charge_energy(
+                    None, hour, relative_power=ev_charge_hours[hour]
+                )
+                consumption += loaded_energy_ev
+                losses_wh_per_hour[hour_idx] += verluste_eauto
+
+            # Process inverter logic
+            energy_feedin_grid_actual = energy_consumption_grid_actual = losses = eigenverbrauch = (
+                0.0
             )
 
+            hour_ac_charge = ac_charge_hours[hour]
+            hour_dc_charge = dc_charge_hours[hour]
+            hourly_electricity_price = elect_price_hourly[hour]
+            hourly_energy_revenue = elect_revenue_per_hour_arr[hour]
+
+            battery.set_charge_allowed_for_hour(hour_dc_charge, hour)
+
+            if inverter:
+                energy_produced = pv_prediction_wh[hour]
+                (
+                    energy_feedin_grid_actual,
+                    energy_consumption_grid_actual,
+                    losses,
+                    eigenverbrauch,
+                ) = inverter.process_energy(energy_produced, consumption, hour)
+
+            # AC PV Battery Charge
+            if hour_ac_charge > 0.0:
+                battery.set_charge_allowed_for_hour(1, hour)
+                battery_charged_energy_actual, battery_losses_actual = battery.charge_energy(
+                    None, hour, relative_power=hour_ac_charge
+                )
+
+                total_battery_energy = battery_charged_energy_actual + battery_losses_actual
+                consumption += total_battery_energy
+                energy_consumption_grid_actual += total_battery_energy
+                losses_wh_per_hour[hour_idx] += battery_losses_actual
+
+            # Update hourly arrays
+            feedin_energy_per_hour[hour_idx] = energy_feedin_grid_actual
+            consumption_energy_per_hour[hour_idx] = energy_consumption_grid_actual
+            losses_wh_per_hour[hour_idx] += losses
+            loads_energy_per_hour[hour_idx] = consumption
+            electricity_price_per_hour[hour_idx] = hourly_electricity_price
+
+            # Financial calculations
+            costs_per_hour[hour_idx] = energy_consumption_grid_actual * hourly_electricity_price
+            revenue_per_hour[hour_idx] = energy_feedin_grid_actual * hourly_energy_revenue
+
+        total_cost = np.nansum(costs_per_hour)
+        total_losses = np.nansum(losses_wh_per_hour)
+        total_revenue = np.nansum(revenue_per_hour)
+
+        # Prepare output dictionary
+        return {
+            "Last_Wh_pro_Stunde": loads_energy_per_hour,
+            "Netzeinspeisung_Wh_pro_Stunde": feedin_energy_per_hour,
+            "Netzbezug_Wh_pro_Stunde": consumption_energy_per_hour,
+            "Kosten_Euro_pro_Stunde": costs_per_hour,
+            "akku_soc_pro_stunde": soc_per_hour,
+            "Einnahmen_Euro_pro_Stunde": revenue_per_hour,
+            "Gesamtbilanz_Euro": total_cost - total_revenue,
+            "EAuto_SoC_pro_Stunde": soc_ev_per_hour,
+            "Gesamteinnahmen_Euro": total_revenue,
+            "Gesamtkosten_Euro": total_cost,
+            "Verluste_Pro_Stunde": losses_wh_per_hour,
+            "Gesamt_Verluste": total_losses,
+            "Home_appliance_wh_per_hour": home_appliance_wh_per_hour,
+            "Electricity_price": electricity_price_per_hour,
+        }
+
 
 # Initialize the Energy Management System, it is a singleton.
 ems = EnergyManagement()

src/akkudoktoreos/core/emsettings.py

@@ -3,7 +3,6 @@
 Kept in an extra module to avoid cyclic dependencies on package import.
 """
 
-from enum import Enum
 from typing import Optional
 
 from pydantic import Field
@@ -11,13 +10,6 @@ from pydantic import Field
 from akkudoktoreos.config.configabc import SettingsBaseModel
 
 
-class EnergyManagementMode(str, Enum):
-    """Energy management mode."""
-
-    PREDICTION = "PREDICTION"
-    OPTIMIZATION = "OPTIMIZATION"
-
-
 class EnergyManagementCommonSettings(SettingsBaseModel):
     """Energy Management Configuration."""
 
@@ -32,9 +24,3 @@ class EnergyManagementCommonSettings(SettingsBaseModel):
         description="Intervall in seconds between EOS energy management runs.",
         examples=["300"],
     )
-
-    mode: Optional[EnergyManagementMode] = Field(
-        default=None,
-        description="Energy management mode [OPTIMIZATION | PREDICTION].",
-        examples=["OPTIMIZATION", "PREDICTION"],
-    )

src/akkudoktoreos/core/logabc.py

@@ -1,3 +1,20 @@
 """Abstract and base classes for logging."""
 
-LOGGING_LEVELS: list[str] = ["TRACE", "DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"]
+import logging
+
+
+def logging_str_to_level(level_str: str) -> int:
+    """Convert log level string to logging level."""
+    if level_str == "DEBUG":
+        level = logging.DEBUG
+    elif level_str == "INFO":
+        level = logging.INFO
+    elif level_str == "WARNING":
+        level = logging.WARNING
+    elif level_str == "CRITICAL":
+        level = logging.CRITICAL
+    elif level_str == "ERROR":
+        level = logging.ERROR
+    else:
+        raise ValueError(f"Unknown loggin level: {level_str}")
+    return level

src/akkudoktoreos/core/logging.py

@@ -1,245 +1,95 @@
-"""Utility for configuring Loguru loggers."""
+"""Utility functions for handling logging tasks.
+
+Functions:
+----------
+- get_logger: Creates and configures a logger with console and optional rotating file logging.
+
+Example usage:
+--------------
+    # Logger setup
+    >>> logger = get_logger(__name__, log_file="app.log", logging_level="DEBUG")
+    >>> logger.info("Logging initialized.")
+
+Notes:
+------
+- The logger supports rotating log files to prevent excessive log file size.
+"""
 
-import json
 import logging as pylogging
 import os
-import re
-import sys
-from pathlib import Path
-from types import FrameType
-from typing import Any, List, Optional
+from logging.handlers import RotatingFileHandler
+from typing import Optional
 
-import pendulum
-from loguru import logger
-
-from akkudoktoreos.core.logabc import LOGGING_LEVELS
+from akkudoktoreos.core.logabc import logging_str_to_level
 
 
-class InterceptHandler(pylogging.Handler):
-    """A logging handler that redirects standard Python logging messages to Loguru.
+def get_logger(
+    name: str,
+    log_file: Optional[str] = None,
+    logging_level: Optional[str] = None,
+    max_bytes: int = 5000000,
+    backup_count: int = 5,
+) -> pylogging.Logger:
+    """Creates and configures a logger with a given name.
 
-    This handler ensures consistency between the `logging` module and Loguru by intercepting
-    logs sent to the standard logging system and re-emitting them through Loguru with proper
-    formatting and context (including exception info and call depth).
-
-    Attributes:
-        loglevel_mapping (dict): Mapping from standard logging levels to Loguru level names.
-    """
-
-    loglevel_mapping: dict[int, str] = {
-        50: "CRITICAL",
-        40: "ERROR",
-        30: "WARNING",
-        20: "INFO",
-        10: "DEBUG",
-        5: "TRACE",
-        0: "NOTSET",
-    }
-
-    def emit(self, record: pylogging.LogRecord) -> None:
-        """Emits a logging record by forwarding it to Loguru with preserved metadata.
-
-        Args:
-            record (logging.LogRecord): A record object containing log message and metadata.
-        """
-        # Skip DEBUG logs from matplotlib - very noisy
-        if record.name.startswith("matplotlib") and record.levelno <= pylogging.DEBUG:
-            return
-
-        try:
-            level = logger.level(record.levelname).name
-        except AttributeError:
-            level = self.loglevel_mapping.get(record.levelno, "INFO")
-
-        frame: Optional[FrameType] = pylogging.currentframe()
-        depth: int = 2
-        while frame and frame.f_code.co_filename == pylogging.__file__:
-            frame = frame.f_back
-            depth += 1
-
-        log = logger.bind(request_id="app")
-        log.opt(depth=depth, exception=record.exc_info).log(level, record.getMessage())
-
-
-console_handler_id = None
-file_handler_id = None
-
-
-def track_logging_config(config_eos: Any, path: str, old_value: Any, value: Any) -> None:
-    """Track logging config changes."""
-    global console_handler_id, file_handler_id
-
-    if not path.startswith("logging"):
-        raise ValueError(f"Logging shall not track '{path}'")
-
-    if not config_eos.logging.console_level:
-        # No value given - check environment value - may also be None
-        config_eos.logging.console_level = os.getenv("EOS_LOGGING__LEVEL")
-    if not config_eos.logging.file_level:
-        # No value given - check environment value - may also be None
-        config_eos.logging.file_level = os.getenv("EOS_LOGGING__LEVEL")
-
-    # Remove handlers
-    if console_handler_id:
-        try:
-            logger.remove(console_handler_id)
-        except Exception as e:
-            logger.debug("Exception on logger.remove: {}", e, exc_info=True)
-        console_handler_id = None
-    if file_handler_id:
-        try:
-            logger.remove(file_handler_id)
-        except Exception as e:
-            logger.debug("Exception on logger.remove: {}", e, exc_info=True)
-        file_handler_id = None
-
-    # Create handlers with new configuration
-    # Always add console handler
-    if config_eos.logging.console_level not in LOGGING_LEVELS:
-        logger.error(
-            f"Invalid console log level '{config_eos.logging.console_level} - forced to INFO'."
-        )
-        config_eos.logging.console_level = "INFO"
-
-    console_handler_id = logger.add(
-        sys.stderr,
-        enqueue=True,
-        backtrace=True,
-        level=config_eos.logging.console_level,
-        # format=_console_format
-    )
-
-    # Add file handler
-    if config_eos.logging.file_level and config_eos.logging.file_path:
-        if config_eos.logging.file_level not in LOGGING_LEVELS:
-            logger.error(
-                f"Invalid file log level '{config_eos.logging.console_level}' - forced to INFO."
-            )
-            config_eos.logging.file_level = "INFO"
-
-        file_handler_id = logger.add(
-            sink=config_eos.logging.file_path,
-            rotation="100 MB",
-            retention="3 days",
-            enqueue=True,
-            backtrace=True,
-            level=config_eos.logging.file_level,
-            serialize=True,  # JSON dict formatting
-            # format=_file_format
-        )
-
-    # Redirect standard logging to Loguru
-    pylogging.basicConfig(handlers=[InterceptHandler()], level=0)
-    # Redirect uvicorn and fastapi logging to Loguru
-    pylogging.getLogger("uvicorn.access").handlers = [InterceptHandler()]
-    for pylogger_name in ["uvicorn", "uvicorn.error", "fastapi"]:
-        pylogger = pylogging.getLogger(pylogger_name)
-        pylogger.handlers = [InterceptHandler()]
-        pylogger.propagate = False
-
-    logger.info(
-        f"Logger reconfigured - console: {config_eos.logging.console_level}, file: {config_eos.logging.file_level}."
-    )
-
-
-def read_file_log(
-    log_path: Path,
-    limit: int = 100,
-    level: Optional[str] = None,
-    contains: Optional[str] = None,
-    regex: Optional[str] = None,
-    from_time: Optional[str] = None,
-    to_time: Optional[str] = None,
-    tail: bool = False,
-) -> List[dict]:
-    """Read and filter structured log entries from a JSON-formatted log file.
+    The logger supports logging to both the console and an optional log file. File logging is
+    handled by a rotating file handler to prevent excessive log file size.
 
     Args:
-        log_path (Path): Path to the JSON-formatted log file.
-        limit (int, optional): Maximum number of log entries to return. Defaults to 100.
-        level (Optional[str], optional): Filter logs by log level (e.g., "INFO", "ERROR"). Defaults to None.
-        contains (Optional[str], optional): Filter logs that contain this substring in their message. Case-insensitive. Defaults to None.
-        regex (Optional[str], optional): Filter logs whose message matches this regular expression. Defaults to None.
-        from_time (Optional[str], optional): ISO 8601 datetime string to filter logs not earlier than this time. Defaults to None.
-        to_time (Optional[str], optional): ISO 8601 datetime string to filter logs not later than this time. Defaults to None.
-        tail (bool, optional): If True, read the last lines of the file (like `tail -n`). Defaults to False.
+        name (str): The name of the logger, typically `__name__` from the calling module.
+        log_file (Optional[str]): Path to the log file for file logging. If None, no file logging is done.
+        logging_level (Optional[str]): Logging level (e.g., "INFO", "DEBUG"). Defaults to "INFO".
+        max_bytes (int): Maximum size in bytes for log file before rotation. Defaults to 5 MB.
+        backup_count (int): Number of backup log files to keep. Defaults to 5.
 
     Returns:
-        List[dict]: A list of filtered log entries as dictionaries.
+        logging.Logger: Configured logger instance.
 
-    Raises:
-        FileNotFoundError: If the log file does not exist.
-        ValueError: If the datetime strings are invalid or improperly formatted.
-        Exception: For other unforeseen I/O or parsing errors.
+    Example:
+        logger = get_logger(__name__, log_file="app.log", logging_level="DEBUG")
+        logger.info("Application started")
     """
-    if not log_path.exists():
-        raise FileNotFoundError("Log file not found")
+    # Create a logger with the specified name
+    logger = pylogging.getLogger(name)
+    logger.propagate = True
+    # This is already supported by pydantic-settings in LoggingCommonSettings, however in case
+    # loading the config itself fails and to set the level before we load the config, we set it here manually.
+    if logging_level is None and (env_level := os.getenv("EOS_LOGGING__LEVEL")) is not None:
+        logging_level = env_level
+    if logging_level is not None:
+        level = logging_str_to_level(logging_level)
+        logger.setLevel(level)
 
-    try:
-        from_dt = pendulum.parse(from_time) if from_time else None
-        to_dt = pendulum.parse(to_time) if to_time else None
-    except Exception as e:
-        raise ValueError(f"Invalid date/time format: {e}")
+    # The log message format
+    formatter = pylogging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s")
 
-    regex_pattern = re.compile(regex) if regex else None
+    # Prevent loggers from being added multiple times
+    # There may already be a logger from pytest
+    if not logger.handlers:
+        # Create a console handler with a standard output stream
+        console_handler = pylogging.StreamHandler()
+        if logging_level is not None:
+            console_handler.setLevel(level)
+        console_handler.setFormatter(formatter)
 
-    def matches_filters(log: dict) -> bool:
-        if level and log.get("level", {}).get("name") != level.upper():
-            return False
-        if contains and contains.lower() not in log.get("message", "").lower():
-            return False
-        if regex_pattern and not regex_pattern.search(log.get("message", "")):
-            return False
-        if from_dt or to_dt:
-            try:
-                log_time = pendulum.parse(log["time"])
-            except Exception:
-                return False
-            if from_dt and log_time < from_dt:
-                return False
-            if to_dt and log_time > to_dt:
-                return False
-        return True
+        # Add the console handler to the logger
+        logger.addHandler(console_handler)
 
-    matched_logs = []
-    lines: list[str] = []
+    if log_file and len(logger.handlers) < 2:  # We assume a console logger to be the first logger
+        # If a log file path is specified, create a rotating file handler
 
-    if tail:
-        with log_path.open("rb") as f:
-            f.seek(0, 2)
-            end = f.tell()
-            buffer = bytearray()
-            pointer = end
+        # Ensure the log directory exists
+        log_dir = os.path.dirname(log_file)
+        if log_dir and not os.path.exists(log_dir):
+            os.makedirs(log_dir)
 
-            while pointer > 0 and len(lines) < limit * 5:
-                pointer -= 1
-                f.seek(pointer)
-                byte = f.read(1)
-                if byte == b"\n":
-                    if buffer:
-                        line = buffer[::-1].decode("utf-8", errors="ignore")
-                        lines.append(line)
-                        buffer.clear()
-                else:
-                    buffer.append(byte[0])
-            if buffer:
-                line = buffer[::-1].decode("utf-8", errors="ignore")
-                lines.append(line)
-        lines = lines[::-1]
-    else:
-        with log_path.open("r", encoding="utf-8", newline=None) as f_txt:
-            lines = f_txt.readlines()
+        # Create a rotating file handler
+        file_handler = RotatingFileHandler(log_file, maxBytes=max_bytes, backupCount=backup_count)
+        if logging_level is not None:
+            file_handler.setLevel(level)
+        file_handler.setFormatter(formatter)
 
-    for line in lines:
-        if not line.strip():
-            continue
-        try:
-            log = json.loads(line)
-        except json.JSONDecodeError:
-            continue
-        if matches_filters(log):
-            matched_logs.append(log)
-            if len(matched_logs) >= limit:
-                break
+        # Add the file handler to the logger
+        logger.addHandler(file_handler)
 
-    return matched_logs
+    return logger

src/akkudoktoreos/core/logsettings.py

@@ -3,56 +3,41 @@
 Kept in an extra module to avoid cyclic dependencies on package import.
 """
 
-from pathlib import Path
+import logging
 from typing import Optional
 
 from pydantic import Field, computed_field, field_validator
 
 from akkudoktoreos.config.configabc import SettingsBaseModel
-from akkudoktoreos.core.logabc import LOGGING_LEVELS
+from akkudoktoreos.core.logabc import logging_str_to_level
 
 
 class LoggingCommonSettings(SettingsBaseModel):
     """Logging Configuration."""
 
-    console_level: Optional[str] = Field(
+    level: Optional[str] = Field(
         default=None,
-        description="Logging level when logging to console.",
-        examples=LOGGING_LEVELS,
+        description="EOS default logging level.",
+        examples=["INFO", "DEBUG", "WARNING", "ERROR", "CRITICAL"],
     )
 
-    file_level: Optional[str] = Field(
-        default=None,
-        description="Logging level when logging to file.",
-        examples=LOGGING_LEVELS,
-    )
-
-    @computed_field  # type: ignore[prop-decorator]
-    @property
-    def file_path(self) -> Optional[Path]:
-        """Computed log file path based on data output path."""
-        try:
-            path = SettingsBaseModel.config.general.data_output_path / "eos.log"
-        except:
-            # Config may not be fully set up
-            path = None
-        return path
-
     # Validators
-    @field_validator("console_level", "file_level", mode="after")
+    @field_validator("level", mode="after")
     @classmethod
-    def validate_level(cls, value: Optional[str]) -> Optional[str]:
-        """Validate logging level string."""
+    def set_default_logging_level(cls, value: Optional[str]) -> Optional[str]:
+        if isinstance(value, str) and value.upper() == "NONE":
+            value = None
         if value is None:
-            # Nothing to set
             return None
-        if isinstance(value, str):
-            level = value.upper()
-            if level == "NONE":
-                return None
-            if level not in LOGGING_LEVELS:
-                raise ValueError(f"Logging level {value} not supported")
-            value = level
-        else:
-            raise TypeError(f"Invalid {type(value)} of logging level {value}")
+        level = logging_str_to_level(value)
+        logging.getLogger().setLevel(level)
         return value
+
+    # Computed fields
+    @computed_field  # type: ignore[prop-decorator]
+    @property
+    def root_level(self) -> str:
+        """Root logger logging level."""
+        level = logging.getLogger().getEffectiveLevel()
+        level_name = logging.getLevelName(level)
+        return level_name

src/akkudoktoreos/core/version.py

@@ -1,5 +0,0 @@
-"""Version information for akkudoktoreos."""
-
-# For development add `+dev` to previous release
-# For release omit `+dev`.
-__version__ = "0.2.0"

src/akkudoktoreos/data/default.config.json

@@ -1,5 +1,2 @@
 {
-  "general": {
-    "version": "0.2.0"
-  }
 }

src/akkudoktoreos/devices/battery.py

@@ -0,0 +1,249 @@
+from typing import Any, Optional
+
+import numpy as np
+from pydantic import Field, field_validator
+
+from akkudoktoreos.core.logging import get_logger
+from akkudoktoreos.devices.devicesabc import (
+    DeviceBase,
+    DeviceOptimizeResult,
+    DeviceParameters,
+)
+from akkudoktoreos.utils.utils import NumpyEncoder
+
+logger = get_logger(__name__)
+
+
+def max_charging_power_field(description: Optional[str] = None) -> float:
+    if description is None:
+        description = "Maximum charging power in watts."
+    return Field(
+        default=5000,
+        gt=0,
+        description=description,
+    )
+
+
+def initial_soc_percentage_field(description: str) -> int:
+    return Field(default=0, ge=0, le=100, description=description, examples=[42])
+
+
+def discharging_efficiency_field(default_value: float) -> float:
+    return Field(
+        default=default_value,
+        gt=0,
+        le=1,
+        description="A float representing the discharge efficiency of the battery.",
+    )
+
+
+class BaseBatteryParameters(DeviceParameters):
+    """Battery Device Simulation Configuration."""
+
+    device_id: str = Field(description="ID of battery", examples=["battery1"])
+    capacity_wh: int = Field(
+        gt=0,
+        description="An integer representing the capacity of the battery in watt-hours.",
+        examples=[8000],
+    )
+    charging_efficiency: float = Field(
+        default=0.88,
+        gt=0,
+        le=1,
+        description="A float representing the charging efficiency of the battery.",
+    )
+    discharging_efficiency: float = discharging_efficiency_field(0.88)
+    max_charge_power_w: Optional[float] = max_charging_power_field()
+    initial_soc_percentage: int = initial_soc_percentage_field(
+        "An integer representing the state of charge of the battery at the **start** of the current hour (not the current state)."
+    )
+    min_soc_percentage: int = Field(
+        default=0,
+        ge=0,
+        le=100,
+        description="An integer representing the minimum state of charge (SOC) of the battery in percentage.",
+        examples=[10],
+    )
+    max_soc_percentage: int = Field(
+        default=100,
+        ge=0,
+        le=100,
+        description="An integer representing the maximum state of charge (SOC) of the battery in percentage.",
+    )
+
+
+class SolarPanelBatteryParameters(BaseBatteryParameters):
+    max_charge_power_w: Optional[float] = max_charging_power_field()
+
+
+class ElectricVehicleParameters(BaseBatteryParameters):
+    """Battery Electric Vehicle Device Simulation Configuration."""
+
+    device_id: str = Field(description="ID of electric vehicle", examples=["ev1"])
+    discharging_efficiency: float = discharging_efficiency_field(1.0)
+    initial_soc_percentage: int = initial_soc_percentage_field(
+        "An integer representing the current state of charge (SOC) of the battery in percentage."
+    )
+
+
+class ElectricVehicleResult(DeviceOptimizeResult):
+    """Result class containing information related to the electric vehicle's charging and discharging behavior."""
+
+    device_id: str = Field(description="ID of electric vehicle", examples=["ev1"])
+    charge_array: list[float] = Field(
+        description="Hourly charging status (0 for no charging, 1 for charging)."
+    )
+    discharge_array: list[int] = Field(
+        description="Hourly discharging status (0 for no discharging, 1 for discharging)."
+    )
+    discharging_efficiency: float = Field(description="The discharge efficiency as a float..")
+    capacity_wh: int = Field(description="Capacity of the EV’s battery in watt-hours.")
+    charging_efficiency: float = Field(description="Charging efficiency as a float..")
+    max_charge_power_w: int = Field(description="Maximum charging power in watts.")
+    soc_wh: float = Field(
+        description="State of charge of the battery in watt-hours at the start of the simulation."
+    )
+    initial_soc_percentage: int = Field(
+        description="State of charge at the start of the simulation in percentage."
+    )
+
+    @field_validator("discharge_array", "charge_array", mode="before")
+    def convert_numpy(cls, field: Any) -> Any:
+        return NumpyEncoder.convert_numpy(field)[0]
+
+
+class Battery(DeviceBase):
+    """Represents a battery device with methods to simulate energy charging and discharging."""
+
+    def __init__(self, parameters: Optional[BaseBatteryParameters] = None):
+        self.parameters: Optional[BaseBatteryParameters] = None
+        super().__init__(parameters)
+
+    def _setup(self) -> None:
+        """Sets up the battery parameters based on configuration or provided parameters."""
+        assert self.parameters is not None
+        self.capacity_wh = self.parameters.capacity_wh
+        self.initial_soc_percentage = self.parameters.initial_soc_percentage
+        self.charging_efficiency = self.parameters.charging_efficiency
+        self.discharging_efficiency = self.parameters.discharging_efficiency
+
+        # Only assign for storage battery
+        self.min_soc_percentage = (
+            self.parameters.min_soc_percentage
+            if isinstance(self.parameters, SolarPanelBatteryParameters)
+            else 0
+        )
+        self.max_soc_percentage = self.parameters.max_soc_percentage
+
+        # Initialize state of charge
+        if self.parameters.max_charge_power_w is not None:
+            self.max_charge_power_w = self.parameters.max_charge_power_w
+        else:
+            self.max_charge_power_w = self.capacity_wh  # TODO this should not be equal capacity_wh
+        self.discharge_array = np.full(self.hours, 1)
+        self.charge_array = np.full(self.hours, 1)
+        self.soc_wh = (self.initial_soc_percentage / 100) * self.capacity_wh
+        self.min_soc_wh = (self.min_soc_percentage / 100) * self.capacity_wh
+        self.max_soc_wh = (self.max_soc_percentage / 100) * self.capacity_wh
+
+    def to_dict(self) -> dict[str, Any]:
+        """Converts the object to a dictionary representation."""
+        return {
+            "device_id": self.device_id,
+            "capacity_wh": self.capacity_wh,
+            "initial_soc_percentage": self.initial_soc_percentage,
+            "soc_wh": self.soc_wh,
+            "hours": self.hours,
+            "discharge_array": self.discharge_array,
+            "charge_array": self.charge_array,
+            "charging_efficiency": self.charging_efficiency,
+            "discharging_efficiency": self.discharging_efficiency,
+            "max_charge_power_w": self.max_charge_power_w,
+        }
+
+    def reset(self) -> None:
+        """Resets the battery state to its initial values."""
+        self.soc_wh = (self.initial_soc_percentage / 100) * self.capacity_wh
+        self.soc_wh = min(max(self.soc_wh, self.min_soc_wh), self.max_soc_wh)
+        self.discharge_array = np.full(self.hours, 1)
+        self.charge_array = np.full(self.hours, 1)
+
+    def set_discharge_per_hour(self, discharge_array: np.ndarray) -> None:
+        """Sets the discharge values for each hour."""
+        if len(discharge_array) != self.hours:
+            raise ValueError(f"Discharge array must have exactly {self.hours} elements.")
+        self.discharge_array = np.array(discharge_array)
+
+    def set_charge_per_hour(self, charge_array: np.ndarray) -> None:
+        """Sets the charge values for each hour."""
+        if len(charge_array) != self.hours:
+            raise ValueError(f"Charge array must have exactly {self.hours} elements.")
+        self.charge_array = np.array(charge_array)
+
+    def set_charge_allowed_for_hour(self, charge: float, hour: int) -> None:
+        """Sets the charge for a specific hour."""
+        if hour >= self.hours:
+            raise ValueError(f"Hour {hour} is out of range. Must be less than {self.hours}.")
+        self.charge_array[hour] = charge
+
+    def current_soc_percentage(self) -> float:
+        """Calculates the current state of charge in percentage."""
+        return (self.soc_wh / self.capacity_wh) * 100
+
+    def discharge_energy(self, wh: float, hour: int) -> tuple[float, float]:
+        """Discharges energy from the battery."""
+        if self.discharge_array[hour] == 0:
+            return 0.0, 0.0
+
+        max_possible_discharge_wh = (self.soc_wh - self.min_soc_wh) * self.discharging_efficiency
+        max_possible_discharge_wh = max(max_possible_discharge_wh, 0.0)
+
+        max_possible_discharge_wh = min(
+            max_possible_discharge_wh, self.max_charge_power_w
+        )  # TODO make a new cfg variable max_discharge_power_w
+
+        actual_discharge_wh = min(wh, max_possible_discharge_wh)
+        actual_withdrawal_wh = (
+            actual_discharge_wh / self.discharging_efficiency
+            if self.discharging_efficiency > 0
+            else 0.0
+        )
+
+        self.soc_wh -= actual_withdrawal_wh
+        self.soc_wh = max(self.soc_wh, self.min_soc_wh)
+
+        losses_wh = actual_withdrawal_wh - actual_discharge_wh
+        return actual_discharge_wh, losses_wh
+
+    def charge_energy(
+        self, wh: Optional[float], hour: int, relative_power: float = 0.0
+    ) -> tuple[float, float]:
+        """Charges energy into the battery."""
+        if hour is not None and self.charge_array[hour] == 0:
+            return 0.0, 0.0  # Charging not allowed in this hour
+
+        if relative_power > 0.0:
+            wh = self.max_charge_power_w * relative_power
+
+        wh = wh if wh is not None else self.max_charge_power_w
+
+        max_possible_charge_wh = (
+            (self.max_soc_wh - self.soc_wh) / self.charging_efficiency
+            if self.charging_efficiency > 0
+            else 0.0
+        )
+        max_possible_charge_wh = max(max_possible_charge_wh, 0.0)
+
+        effective_charge_wh = min(wh, max_possible_charge_wh)
+        charged_wh = effective_charge_wh * self.charging_efficiency
+
+        self.soc_wh += charged_wh
+        self.soc_wh = min(self.soc_wh, self.max_soc_wh)
+
+        losses_wh = effective_charge_wh - charged_wh
+        return charged_wh, losses_wh
+
+    def current_energy_content(self) -> float:
+        """Returns the current usable energy in the battery."""
+        usable_energy = (self.soc_wh - self.min_soc_wh) * self.discharging_efficiency
+        return max(usable_energy, 0.0)

src/akkudoktoreos/devices/devices.py

@@ -1,418 +1,48 @@
-"""General configuration settings for simulated devices for optimization."""
-
-import json
-import re
-from typing import Any, Optional, TextIO, cast
-
-import numpy as np
-from loguru import logger
-from numpydantic import NDArray, Shape
-from pydantic import Field, computed_field, field_validator, model_validator
-
-from akkudoktoreos.config.configabc import SettingsBaseModel
-from akkudoktoreos.core.cache import CacheFileStore
-from akkudoktoreos.core.coreabc import ConfigMixin, SingletonMixin
-from akkudoktoreos.core.emplan import ResourceStatus
-from akkudoktoreos.core.pydantic import ConfigDict, PydanticBaseModel
-from akkudoktoreos.devices.devicesabc import DevicesBaseSettings
-from akkudoktoreos.utils.datetimeutil import DateTime, TimeWindowSequence, to_datetime
-
-# Default charge rates for battery
-BATTERY_DEFAULT_CHARGE_RATES = np.linspace(0.0, 1.0, 11)  # 0.0, 0.1, ..., 1.0
-
-
-class BatteriesCommonSettings(DevicesBaseSettings):
-    """Battery devices base settings."""
-
-    capacity_wh: int = Field(
-        default=8000,
-        gt=0,
-        description="Capacity [Wh].",
-        examples=[8000],
-    )
-
-    charging_efficiency: float = Field(
-        default=0.88,
-        gt=0,
-        le=1,
-        description="Charging efficiency [0.01 ... 1.00].",
-        examples=[0.88],
-    )
-
-    discharging_efficiency: float = Field(
-        default=0.88,
-        gt=0,
-        le=1,
-        description="Discharge efficiency [0.01 ... 1.00].",
-        examples=[0.88],
-    )
-
-    levelized_cost_of_storage_kwh: float = Field(
-        default=0.0,
-        description="Levelized cost of storage (LCOS), the average lifetime cost of delivering one kWh [€/kWh].",
-        examples=[0.12],
-    )
-
-    max_charge_power_w: Optional[float] = Field(
-        default=5000,
-        gt=0,
-        description="Maximum charging power [W].",
-        examples=[5000],
-    )
-
-    min_charge_power_w: Optional[float] = Field(
-        default=50,
-        gt=0,
-        description="Minimum charging power [W].",
-        examples=[50],
-    )
-
-    charge_rates: Optional[NDArray[Shape["*"], float]] = Field(
-        default=BATTERY_DEFAULT_CHARGE_RATES,
-        description=(
-            "Charge rates as factor of maximum charging power [0.00 ... 1.00]. "
-            "None triggers fallback to default charge-rates."
-        ),
-        examples=[[0.0, 0.25, 0.5, 0.75, 1.0], None],
-    )
-
-    min_soc_percentage: int = Field(
-        default=0,
-        ge=0,
-        le=100,
-        description=(
-            "Minimum state of charge (SOC) as percentage of capacity [%]. "
-            "This is the target SoC for charging"
-        ),
-        examples=[10],
-    )
-
-    max_soc_percentage: int = Field(
-        default=100,
-        ge=0,
-        le=100,
-        description="Maximum state of charge (SOC) as percentage of capacity [%].",
-        examples=[100],
-    )
-
-    @field_validator("charge_rates", mode="before")
-    def validate_and_sort_charge_rates(cls, v: Any) -> NDArray[Shape["*"], float]:
-        # None means fallback to default values
-        if v is None:
-            return BATTERY_DEFAULT_CHARGE_RATES.copy()
-
-        # Convert to numpy array
-        if isinstance(v, str):
-            # Remove brackets and split by comma or whitespace
-            numbers = re.split(r"[,\s]+", v.strip("[]"))
-
-            # Filter out any empty strings and convert to floats
-            arr = np.array([float(x) for x in numbers if x])
-        else:
-            arr = np.array(v, dtype=float)
-
-        # Must not be empty
-        if arr.size == 0:
-            raise ValueError("charge_rates must contain at least one value.")
-
-        # Enforce bounds: 0.0 ≤ x ≤ 1.0
-        if (arr < 0.0).any() or (arr > 1.0).any():
-            raise ValueError("charge_rates must be within [0.0, 1.0].")
-
-        # Remove duplicates + sort
-        arr = np.unique(arr)
-        arr.sort()
-
-        return arr
-
-    @computed_field  # type: ignore[prop-decorator]
-    @property
-    def measurement_key_soc_factor(self) -> str:
-        """Measurement key for the battery state of charge (SoC) as factor of total capacity [0.0 ... 1.0]."""
-        return f"{self.device_id}-soc-factor"
-
-    @computed_field  # type: ignore[prop-decorator]
-    @property
-    def measurement_key_power_l1_w(self) -> str:
-        """Measurement key for the L1 power the battery is charged or discharged with [W]."""
-        return f"{self.device_id}-power-l1-w"
-
-    @computed_field  # type: ignore[prop-decorator]
-    @property
-    def measurement_key_power_l2_w(self) -> str:
-        """Measurement key for the L2 power the battery is charged or discharged with [W]."""
-        return f"{self.device_id}-power-l2-w"
-
-    @computed_field  # type: ignore[prop-decorator]
-    @property
-    def measurement_key_power_l3_w(self) -> str:
-        """Measurement key for the L3 power the battery is charged or discharged with [W]."""
-        return f"{self.device_id}-power-l3-w"
-
-    @computed_field  # type: ignore[prop-decorator]
-    @property
-    def measurement_key_power_3_phase_sym_w(self) -> str:
-        """Measurement key for the symmetric 3 phase power the battery is charged or discharged with [W]."""
-        return f"{self.device_id}-power-3-phase-sym-w"
-
-    @computed_field  # type: ignore[prop-decorator]
-    @property
-    def measurement_keys(self) -> Optional[list[str]]:
-        """Measurement keys for the battery stati that are measurements.
-
-        Battery SoC, power.
-        """
-        keys: list[str] = [
-            self.measurement_key_soc_factor,
-            self.measurement_key_power_l1_w,
-            self.measurement_key_power_l2_w,
-            self.measurement_key_power_l3_w,
-            self.measurement_key_power_3_phase_sym_w,
-        ]
-        return keys
-
-
-class InverterCommonSettings(DevicesBaseSettings):
-    """Inverter devices base settings."""
-
-    max_power_w: Optional[float] = Field(
-        default=None,
-        gt=0,
-        description="Maximum power [W].",
-        examples=[10000],
-    )
-
-    battery_id: Optional[str] = Field(
-        default=None,
-        description="ID of battery controlled by this inverter.",
-        examples=[None, "battery1"],
-    )
-
-    @computed_field  # type: ignore[prop-decorator]
-    @property
-    def measurement_keys(self) -> Optional[list[str]]:
-        """Measurement keys for the inverter stati that are measurements."""
-        keys: list[str] = []
-        return keys
-
-
-class HomeApplianceCommonSettings(DevicesBaseSettings):
-    """Home Appliance devices base settings."""
-
-    consumption_wh: int = Field(
-        gt=0,
-        description="Energy consumption [Wh].",
-        examples=[2000],
-    )
-
-    duration_h: int = Field(
-        gt=0,
-        le=24,
-        description="Usage duration in hours [0 ... 24].",
-        examples=[1],
-    )
-
-    time_windows: Optional[TimeWindowSequence] = Field(
-        default=None,
-        description="Sequence of allowed time windows. Defaults to optimization general time window.",
-        examples=[
-            {
-                "windows": [
-                    {"start_time": "10:00", "duration": "2 hours"},
-                ],
-            },
-        ],
-    )
-
-    @computed_field  # type: ignore[prop-decorator]
-    @property
-    def measurement_keys(self) -> Optional[list[str]]:
-        """Measurement keys for the home appliance stati that are measurements."""
-        keys: list[str] = []
-        return keys
-
-
-class DevicesCommonSettings(SettingsBaseModel):
-    """Base configuration for devices simulation settings."""
-
-    batteries: Optional[list[BatteriesCommonSettings]] = Field(
-        default=None,
-        description="List of battery devices",
-        examples=[[{"device_id": "battery1", "capacity_wh": 8000}]],
-    )
-
-    max_batteries: Optional[int] = Field(
-        default=None,
-        ge=0,
-        description="Maximum number of batteries that can be set",
-        examples=[1, 2],
-    )
-
-    electric_vehicles: Optional[list[BatteriesCommonSettings]] = Field(
-        default=None,
-        description="List of electric vehicle devices",
-        examples=[[{"device_id": "battery1", "capacity_wh": 8000}]],
-    )
-
-    max_electric_vehicles: Optional[int] = Field(
-        default=None,
-        ge=0,
-        description="Maximum number of electric vehicles that can be set",
-        examples=[1, 2],
-    )
-
-    inverters: Optional[list[InverterCommonSettings]] = Field(
-        default=None, description="List of inverters", examples=[[]]
-    )
-
-    max_inverters: Optional[int] = Field(
-        default=None,
-        ge=0,
-        description="Maximum number of inverters that can be set",
-        examples=[1, 2],
-    )
-
-    home_appliances: Optional[list[HomeApplianceCommonSettings]] = Field(
-        default=None, description="List of home appliances", examples=[[]]
-    )
-
-    max_home_appliances: Optional[int] = Field(
-        default=None,
-        ge=0,
-        description="Maximum number of home_appliances that can be set",
-        examples=[1, 2],
-    )
-
-    @computed_field  # type: ignore[prop-decorator]
-    @property
-    def measurement_keys(self) -> Optional[list[str]]:
-        """Return the measurement keys for the resource/ device stati that are measurements."""
-        keys: list[str] = []
-
-        if self.max_batteries and self.batteries:
-            for battery in self.batteries:
-                keys.extend(battery.measurement_keys)
-        if self.max_electric_vehicles and self.electric_vehicles:
-            for electric_vehicle in self.electric_vehicles:
-                keys.extend(electric_vehicle.measurement_keys)
-        return keys
-
-
-# Type used for indexing: (resource_id, optional actuator_id)
-class ResourceKey(PydanticBaseModel):
-    """Key identifying a resource and optionally an actuator."""
-
-    resource_id: str
-    actuator_id: Optional[str] = None
-
-    model_config = ConfigDict(frozen=True)
-
-    def __hash__(self) -> int:
-        """Returns a stable hash based on the resource_id and actuator_id.
-
-        Returns:
-            int: Hash value derived from the resource_id and actuator_id.
-        """
-        return hash(self.resource_id + self.actuator_id if self.actuator_id else "")
-
-    def as_tuple(self) -> tuple[str, Optional[str]]:
-        """Return the key as a tuple for internal dictionary indexing."""
-        return (self.resource_id, self.actuator_id)
-
-    def __eq__(self, other: Any) -> bool:
-        if not isinstance(other, ResourceKey):
-            return NotImplemented
-        return self.resource_id == other.resource_id and self.actuator_id == other.actuator_id
-
-
-class ResourceRegistry(SingletonMixin, ConfigMixin, PydanticBaseModel):
-    """Registry for collecting and retrieving device status reports for simulations.
-
-    Maintains the latest and optionally historical status reports for each resource.
-    """
-
-    keep_history: bool = False
-    history_size: int = 100
-
-    latest: dict[ResourceKey, ResourceStatus] = Field(
-        default_factory=dict,
-        description="Latest resource status that was reported per resource key.",
-        example=[],
-    )
-    history: dict[ResourceKey, list[tuple[DateTime, ResourceStatus]]] = Field(
-        default_factory=dict,
-        description="History of resource stati that were reported per resource key.",
-        example=[],
-    )
-
-    @model_validator(mode="after")
-    def _enforce_history_limits(self) -> "ResourceRegistry":
-        """Ensure history list lengths respect the history_size limit."""
-        if self.keep_history:
-            for key, records in self.history.items():
-                if len(records) > self.history_size:
-                    self.history[key] = records[-self.history_size :]
-        return self
-
-    def update_status(self, key: ResourceKey, status: ResourceStatus) -> None:
-        """Update the latest status and optionally store in history.
-
-        Args:
-            key (ResourceKey): Identifier for the resource.
-            status (ResourceStatus): Status report to store.
-        """
-        self.latest[key] = status
-        if self.keep_history:
-            timestamp = getattr(status, "transition_timestamp", None) or to_datetime()
-            self.history.setdefault(key, []).append((timestamp, status))
-            if len(self.history[key]) > self.history_size:
-                self.history[key] = self.history[key][-self.history_size :]
-
-    def status_latest(self, key: ResourceKey) -> Optional[ResourceStatus]:
-        """Retrieve the most recent status for a resource."""
-        return self.latest.get(key)
-
-    def status_history(self, key: ResourceKey) -> list[tuple[DateTime, ResourceStatus]]:
-        """Retrieve historical status reports for a resource."""
-        if not self.keep_history:
-            raise RuntimeError("History tracking is disabled.")
-        return self.history.get(key, [])
-
-    def status_exists(self, key: ResourceKey) -> bool:
-        """Check if a status report exists for the given resource.
-
-        Args:
-            key (ResourceKey): Identifier for the resource.
-        """
-        return key in self.latest
-
-    def save(self) -> None:
-        """Save the registry to file."""
-        # Make explicit cast to make mypy happy
-        cache_file = cast(
-            TextIO, CacheFileStore().create(key="resource_registry", mode="w+", suffix=".json")
-        )
-        cache_file.seek(0)
-        cache_file.write(self.model_dump_json(indent=4))
-        cache_file.truncate()  # Important to remove leftover data!
-
-    def load(self) -> None:
-        """Load registry state from file and update the current instance."""
-        cache_file = CacheFileStore().get(key="resource_registry")
-        if cache_file:
-            try:
-                cache_file.seek(0)
-                data = json.load(cache_file)
-                loaded = self.__class__.model_validate(data)
-
-                self.keep_history = loaded.keep_history
-                self.history_size = loaded.history_size
-                self.latest = loaded.latest
-                self.history = loaded.history
-            except Exception as e:
-                logger.error("Can not load resource registry: {}", e)
-
-
-def get_resource_registry() -> ResourceRegistry:
-    """Gets the EOS resource registry."""
-    return ResourceRegistry()
+from typing import Optional
+
+from akkudoktoreos.core.coreabc import SingletonMixin
+from akkudoktoreos.core.logging import get_logger
+from akkudoktoreos.devices.battery import Battery
+from akkudoktoreos.devices.devicesabc import DevicesBase
+from akkudoktoreos.devices.generic import HomeAppliance
+from akkudoktoreos.devices.inverter import Inverter
+from akkudoktoreos.devices.settings import DevicesCommonSettings
+
+logger = get_logger(__name__)
+
+
+class Devices(SingletonMixin, DevicesBase):
+    def __init__(self, settings: Optional[DevicesCommonSettings] = None):
+        if hasattr(self, "_initialized"):
+            return
+        super().__init__()
+        if settings is None:
+            settings = self.config.devices
+            if settings is None:
+                return
+
+        # initialize devices
+        if settings.batteries is not None:
+            for battery_params in settings.batteries:
+                self.add_device(Battery(battery_params))
+        if settings.inverters is not None:
+            for inverter_params in settings.inverters:
+                self.add_device(Inverter(inverter_params))
+        if settings.home_appliances is not None:
+            for home_appliance_params in settings.home_appliances:
+                self.add_device(HomeAppliance(home_appliance_params))
+
+        self.post_setup()
+
+    def post_setup(self) -> None:
+        for device in self.devices.values():
+            device.post_setup()
+
+
+# Initialize the Devices  simulation, it is a singleton.
+devices = Devices()
+
+
+def get_devices() -> Devices:
+    """Gets the EOS Devices simulation."""
+    return devices

src/akkudoktoreos/devices/devicesabc.py

@@ -1,131 +1,182 @@
 """Abstract and base classes for devices."""
 
-from enum import StrEnum
+from enum import Enum
+from typing import Optional, Type
 
-from pydantic import Field
+from pendulum import DateTime
+from pydantic import Field, computed_field
 
-from akkudoktoreos.config.configabc import SettingsBaseModel
+from akkudoktoreos.core.coreabc import (
+    ConfigMixin,
+    DevicesMixin,
+    EnergyManagementSystemMixin,
+    PredictionMixin,
+)
+from akkudoktoreos.core.logging import get_logger
+from akkudoktoreos.core.pydantic import ParametersBaseModel
+from akkudoktoreos.utils.datetimeutil import to_duration
+
+logger = get_logger(__name__)
 
 
-class DevicesBaseSettings(SettingsBaseModel):
-    """Base devices setting."""
-
-    device_id: str = Field(
-        default="<unknown>",
-        description="ID of device",
-        examples=["battery1", "ev1", "inverter1", "dishwasher"],
+class DeviceParameters(ParametersBaseModel):
+    device_id: str = Field(description="ID of device", examples="device1")
+    hours: Optional[int] = Field(
+        default=None,
+        gt=0,
+        description="Number of prediction hours. Defaults to global config prediction hours.",
+        examples=[None],
     )
 
 
-class BatteryOperationMode(StrEnum):
-    """Battery Operation Mode.
+class DeviceOptimizeResult(ParametersBaseModel):
+    device_id: str = Field(description="ID of device", examples=["device1"])
+    hours: int = Field(gt=0, description="Number of hours in the simulation.", examples=[24])
 
-    Enumerates the operating modes of a battery in a home energy
-    management simulation. These modes require no direct awareness
-    of electricity prices or carbon intensity — higher-level
-    controllers or optimizers decide when to switch modes.
 
-    Modes
-    -----
-    - IDLE:
-        No charging or discharging.
+class DeviceState(Enum):
+    UNINITIALIZED = 0
+    PREPARED = 1
+    INITIALIZED = 2
 
-    - SELF_CONSUMPTION:
-        Charge from local surplus and discharge to meet local demand.
 
-    - NON_EXPORT:
-        Charge from on-site or local surplus with the goal of
-        minimizing or preventing energy export to the external grid.
-        Discharging to the grid is not allowed.
+class DevicesStartEndMixin(ConfigMixin, EnergyManagementSystemMixin):
+    """A mixin to manage start, end datetimes for devices data.
 
-    - PEAK_SHAVING:
-        Discharge during local demand peaks to reduce grid draw.
-
-    - GRID_SUPPORT_EXPORT:
-        Discharge to support the upstream grid when commanded.
-
-    - GRID_SUPPORT_IMPORT:
-        Charge from the grid when instructed to absorb excess supply.
-
-    - FREQUENCY_REGULATION:
-        Perform fast bidirectional power adjustments based on grid
-        frequency deviations.
-
-    - RAMP_RATE_CONTROL:
-        Smooth changes in local net load or generation.
-
-    - RESERVE_BACKUP:
-        Maintain a minimum state of charge for emergency use.
-
-    - OUTAGE_SUPPLY:
-        Discharge to power critical loads during a grid outage.
-
-    - FORCED_CHARGE:
-        Override all other logic and charge regardless of conditions.
-
-    - FORCED_DISCHARGE:
-        Override all other logic and discharge regardless of conditions.
-
-    - FAULT:
-        Battery is unavailable due to fault or error state.
+    The starting datetime for devices data generation is provided by the energy management
+    system. Device data cannot be computed if this value is `None`.
     """
 
-    IDLE = "IDLE"
-    SELF_CONSUMPTION = "SELF_CONSUMPTION"
-    NON_EXPORT = "NON_EXPORT"
-    PEAK_SHAVING = "PEAK_SHAVING"
-    GRID_SUPPORT_EXPORT = "GRID_SUPPORT_EXPORT"
-    GRID_SUPPORT_IMPORT = "GRID_SUPPORT_IMPORT"
-    FREQUENCY_REGULATION = "FREQUENCY_REGULATION"
-    RAMP_RATE_CONTROL = "RAMP_RATE_CONTROL"
-    RESERVE_BACKUP = "RESERVE_BACKUP"
-    OUTAGE_SUPPLY = "OUTAGE_SUPPLY"
-    FORCED_CHARGE = "FORCED_CHARGE"
-    FORCED_DISCHARGE = "FORCED_DISCHARGE"
-    FAULT = "FAULT"
+    # Computed field for end_datetime and keep_datetime
+    @computed_field  # type: ignore[prop-decorator]
+    @property
+    def end_datetime(self) -> Optional[DateTime]:
+        """Compute the end datetime based on the `start_datetime` and `hours`.
+
+        Ajusts the calculated end time if DST transitions occur within the prediction window.
+
+        Returns:
+            Optional[DateTime]: The calculated end datetime, or `None` if inputs are missing.
+        """
+        if self.ems.start_datetime and self.config.prediction.hours:
+            end_datetime = self.ems.start_datetime + to_duration(
+                f"{self.config.prediction.hours} hours"
+            )
+            dst_change = end_datetime.offset_hours - self.ems.start_datetime.offset_hours
+            logger.debug(
+                f"Pre: {self.ems.start_datetime}..{end_datetime}: DST change: {dst_change}"
+            )
+            if dst_change < 0:
+                end_datetime = end_datetime + to_duration(f"{abs(int(dst_change))} hours")
+            elif dst_change > 0:
+                end_datetime = end_datetime - to_duration(f"{abs(int(dst_change))} hours")
+            logger.debug(
+                f"Pst: {self.ems.start_datetime}..{end_datetime}: DST change: {dst_change}"
+            )
+            return end_datetime
+        return None
+
+    @computed_field  # type: ignore[prop-decorator]
+    @property
+    def total_hours(self) -> Optional[int]:
+        """Compute the hours from `start_datetime` to `end_datetime`.
+
+        Returns:
+            Optional[pendulum.period]: The duration hours, or `None` if either datetime is unavailable.
+        """
+        end_dt = self.end_datetime
+        if end_dt is None:
+            return None
+        duration = end_dt - self.ems.start_datetime
+        return int(duration.total_hours())
 
 
-class ApplianceOperationMode(StrEnum):
-    """Appliance operation modes.
+class DeviceBase(DevicesStartEndMixin, PredictionMixin, DevicesMixin):
+    """Base class for device simulations.
 
-    Modes
-    -----
-    - OFF:
-        Stop or prevent any active operation of the appliance.
+    Enables access to EOS configuration data (attribute `config`), EOS prediction data (attribute
+    `prediction`) and EOS device registry (attribute `devices`).
 
-    - RUN:
-        Start or continue normal operation of the appliance.
+    Behavior:
+        - Several initialization phases (setup, post_setup):
+            - setup: Initialize class attributes from DeviceParameters (pydantic input validation)
+            - post_setup: Set connections between devices
+        - NotImplemented:
+            - hooks during optimization
 
-    - DEFER:
-        Postpone operation to a later time window based on
-        scheduling or optimization criteria.
-
-    - PAUSE:
-        Temporarily suspend an ongoing operation, keeping the
-        option to resume later.
-
-    - RESUME:
-        Continue an operation that was previously paused or
-        deferred.
-
-    - LIMIT_POWER:
-        Run the appliance under reduced power constraints,
-        for example in response to load-management or
-        demand-response signals.
-
-    - FORCED_RUN:
-        Start or maintain operation even if constraints or
-        optimization strategies would otherwise delay or limit it.
-
-    - FAULT:
-        Appliance is unavailable due to fault or error state.
+    Notes:
+        - This class is base to concrete devices like battery, inverter, etc. that are used in optimization.
+        - Not a pydantic model for a low footprint during optimization.
     """
 
-    OFF = "OFF"
-    RUN = "RUN"
-    DEFER = "DEFER"
-    PAUSE = "PAUSE"
-    RESUME = "RESUME"
-    LIMIT_POWER = "LIMIT_POWER"
-    FORCED_RUN = "FORCED_RUN"
-    FAULT = "FAULT"
+    def __init__(self, parameters: Optional[DeviceParameters] = None):
+        self.device_id: str = "<invalid>"
+        self.parameters: Optional[DeviceParameters] = None
+        self.hours = -1
+        if self.total_hours is not None:
+            self.hours = self.total_hours
+
+        self.initialized = DeviceState.UNINITIALIZED
+
+        if parameters is not None:
+            self.setup(parameters)
+
+    def setup(self, parameters: DeviceParameters) -> None:
+        if self.initialized != DeviceState.UNINITIALIZED:
+            return
+
+        self.parameters = parameters
+        self.device_id = self.parameters.device_id
+
+        if self.parameters.hours is not None:
+            self.hours = self.parameters.hours
+        if self.hours < 0:
+            raise ValueError("hours is unset")
+
+        self._setup()
+
+        self.initialized = DeviceState.PREPARED
+
+    def post_setup(self) -> None:
+        if self.initialized.value >= DeviceState.INITIALIZED.value:
+            return
+
+        self._post_setup()
+        self.initialized = DeviceState.INITIALIZED
+
+    def _setup(self) -> None:
+        """Implement custom setup in derived device classes."""
+        pass
+
+    def _post_setup(self) -> None:
+        """Implement custom setup in derived device classes that is run when all devices are initialized."""
+        pass
+
+
+class DevicesBase(DevicesStartEndMixin, PredictionMixin):
+    """Base class for handling device data.
+
+    Enables access to EOS configuration data (attribute `config`) and EOS prediction data (attribute
+    `prediction`).
+    """
+
+    def __init__(self) -> None:
+        super().__init__()
+        self.devices: dict[str, "DeviceBase"] = dict()
+
+    def get_device_by_id(self, device_id: str) -> Optional["DeviceBase"]:
+        return self.devices.get(device_id)
+
+    def add_device(self, device: Optional["DeviceBase"]) -> None:
+        if device is None:
+            return
+        assert device.device_id not in self.devices, f"{device.device_id} already registered"
+        self.devices[device.device_id] = device
+
+    def remove_device(self, device: Type["DeviceBase"] | str) -> bool:
+        if isinstance(device, DeviceBase):
+            device = device.device_id
+        return self.devices.pop(device, None) is not None  # type: ignore[arg-type]
+
+    def reset(self) -> None:
+        self.devices = dict()

src/akkudoktoreos/devices/generic.py

@@ -0,0 +1,81 @@
+from typing import Optional
+
+import numpy as np
+from pydantic import Field
+
+from akkudoktoreos.core.logging import get_logger
+from akkudoktoreos.devices.devicesabc import DeviceBase, DeviceParameters
+
+logger = get_logger(__name__)
+
+
+class HomeApplianceParameters(DeviceParameters):
+    """Home Appliance Device Simulation Configuration."""
+
+    device_id: str = Field(description="ID of home appliance", examples=["dishwasher"])
+    consumption_wh: int = Field(
+        gt=0,
+        description="An integer representing the energy consumption of a household device in watt-hours.",
+        examples=[2000],
+    )
+    duration_h: int = Field(
+        gt=0,
+        description="An integer representing the usage duration of a household device in hours.",
+        examples=[3],
+    )
+
+
+class HomeAppliance(DeviceBase):
+    def __init__(
+        self,
+        parameters: Optional[HomeApplianceParameters] = None,
+    ):
+        self.parameters: Optional[HomeApplianceParameters] = None
+        super().__init__(parameters)
+
+    def _setup(self) -> None:
+        assert self.parameters is not None
+        self.load_curve = np.zeros(self.hours)  # Initialize the load curve with zeros
+        self.duration_h = self.parameters.duration_h
+        self.consumption_wh = self.parameters.consumption_wh
+
+    def set_starting_time(self, start_hour: int, global_start_hour: int = 0) -> None:
+        """Sets the start time of the device and generates the corresponding load curve.
+
+        :param start_hour: The hour at which the device should start.
+        """
+        self.reset_load_curve()
+        # Check if the duration of use is within the available time frame
+        if start_hour + self.duration_h > self.hours:
+            raise ValueError("The duration of use exceeds the available time frame.")
+        if start_hour < global_start_hour:
+            raise ValueError("The start time is earlier than the available time frame.")
+
+        # Calculate power per hour based on total consumption and duration
+        power_per_hour = self.consumption_wh / self.duration_h  # Convert to watt-hours
+
+        # Set the power for the duration of use in the load curve array
+        self.load_curve[start_hour : start_hour + self.duration_h] = power_per_hour
+
+    def reset_load_curve(self) -> None:
+        """Resets the load curve."""
+        self.load_curve = np.zeros(self.hours)
+
+    def get_load_curve(self) -> np.ndarray:
+        """Returns the current load curve."""
+        return self.load_curve
+
+    def get_load_for_hour(self, hour: int) -> float:
+        """Returns the load for a specific hour.
+
+        :param hour: The hour for which the load is queried.
+        :return: The load in watts for the specified hour.
+        """
+        if hour < 0 or hour >= self.hours:
+            raise ValueError("The specified hour is outside the available time frame.")
+
+        return self.load_curve[hour]
+
+    def get_latest_starting_point(self) -> int:
+        """Returns the latest possible start time at which the device can still run completely."""
+        return self.hours - self.duration_h

src/akkudoktoreos/devices/genetic/battery.py

@@ -1,280 +0,0 @@
-from typing import Any, Iterator, Optional
-
-import numpy as np
-
-from akkudoktoreos.devices.devices import BATTERY_DEFAULT_CHARGE_RATES
-from akkudoktoreos.optimization.genetic.geneticdevices import (
-    BaseBatteryParameters,
-    SolarPanelBatteryParameters,
-)
-
-
-class Battery:
-    """Represents a battery device with methods to simulate energy charging and discharging."""
-
-    def __init__(self, parameters: BaseBatteryParameters, prediction_hours: int):
-        self.parameters = parameters
-        self.prediction_hours = prediction_hours
-        self._setup()
-
-    def _setup(self) -> None:
-        """Sets up the battery parameters based on provided parameters."""
-        self.capacity_wh = self.parameters.capacity_wh
-        self.initial_soc_percentage = self.parameters.initial_soc_percentage
-        self.charging_efficiency = self.parameters.charging_efficiency
-        self.discharging_efficiency = self.parameters.discharging_efficiency
-
-        # Charge rates, in case of None use default
-        self.charge_rates = BATTERY_DEFAULT_CHARGE_RATES
-        if self.parameters.charge_rates:
-            charge_rates = np.array(self.parameters.charge_rates, dtype=float)
-            charge_rates = np.unique(charge_rates)
-            charge_rates.sort()
-            self.charge_rates = charge_rates
-
-        # Only assign for storage battery
-        self.min_soc_percentage = (
-            self.parameters.min_soc_percentage
-            if isinstance(self.parameters, SolarPanelBatteryParameters)
-            else 0
-        )
-        self.max_soc_percentage = self.parameters.max_soc_percentage
-
-        # Initialize state of charge
-        if self.parameters.max_charge_power_w is not None:
-            self.max_charge_power_w = self.parameters.max_charge_power_w
-        else:
-            self.max_charge_power_w = self.capacity_wh  # TODO this should not be equal capacity_wh
-        self.discharge_array = np.full(self.prediction_hours, 0)
-        self.charge_array = np.full(self.prediction_hours, 0)
-        self.soc_wh = (self.initial_soc_percentage / 100) * self.capacity_wh
-        self.min_soc_wh = (self.min_soc_percentage / 100) * self.capacity_wh
-        self.max_soc_wh = (self.max_soc_percentage / 100) * self.capacity_wh
-
-    def _lower_charge_rates_desc(self, start_rate: float) -> Iterator[float]:
-        """Yield all charge rates lower than a given rate in descending order.
-
-        Args:
-            charge_rates (np.ndarray): Sorted 1D array of available charge rates.
-            start_rate (float): The reference charge rate.
-
-        Yields:
-            float: Charge rates lower than `start_rate`, in descending order.
-        """
-        charge_rates_fast = self.charge_rates
-
-        # Find the insertion index for start_rate (left-most position)
-        idx = np.searchsorted(charge_rates_fast, start_rate, side="left")
-
-        # Yield values before idx in reverse (descending)
-        return (charge_rates_fast[j] for j in range(idx - 1, -1, -1))
-
-    def to_dict(self) -> dict[str, Any]:
-        """Converts the object to a dictionary representation."""
-        return {
-            "device_id": self.parameters.device_id,
-            "capacity_wh": self.capacity_wh,
-            "initial_soc_percentage": self.initial_soc_percentage,
-            "soc_wh": self.soc_wh,
-            "hours": self.prediction_hours,
-            "discharge_array": self.discharge_array,
-            "charge_array": self.charge_array,
-            "charging_efficiency": self.charging_efficiency,
-            "discharging_efficiency": self.discharging_efficiency,
-            "max_charge_power_w": self.max_charge_power_w,
-        }
-
-    def reset(self) -> None:
-        """Resets the battery state to its initial values."""
-        self.soc_wh = (self.initial_soc_percentage / 100) * self.capacity_wh
-        self.soc_wh = min(max(self.soc_wh, self.min_soc_wh), self.max_soc_wh)
-        self.discharge_array = np.full(self.prediction_hours, 0)
-        self.charge_array = np.full(self.prediction_hours, 0)
-
-    def set_discharge_per_hour(self, discharge_array: np.ndarray) -> None:
-        """Sets the discharge values for each hour."""
-        if len(discharge_array) != self.prediction_hours:
-            raise ValueError(
-                f"Discharge array must have exactly {self.prediction_hours} elements. Got {len(discharge_array)} elements."
-            )
-        self.discharge_array = np.array(discharge_array)
-
-    def set_charge_per_hour(self, charge_array: np.ndarray) -> None:
-        """Sets the charge values for each hour."""
-        if len(charge_array) != self.prediction_hours:
-            raise ValueError(
-                f"Charge array must have exactly {self.prediction_hours} elements. Got {len(charge_array)} elements."
-            )
-        self.charge_array = np.array(charge_array)
-
-    def current_soc_percentage(self) -> float:
-        """Calculates the current state of charge in percentage."""
-        return (self.soc_wh / self.capacity_wh) * 100
-
-    def discharge_energy(self, wh: float, hour: int) -> tuple[float, float]:
-        """Discharge energy from the battery.
-
-        Discharge is limited by:
-        * Requested delivered energy
-        * Remaining energy above minimum SoC
-        * Maximum discharge power
-        * Discharge efficiency
-
-        Args:
-            wh (float): Requested delivered energy in watt-hours.
-            hour (int): Time index. If `self.discharge_array[hour] == 0`,
-                no discharge occurs.
-
-        Returns:
-            tuple[float, float]:
-                delivered_wh (float): Actual delivered energy [Wh].
-                losses_wh (float): Conversion losses [Wh].
-
-        """
-        if self.discharge_array[hour] == 0:
-            return 0.0, 0.0
-
-        # Raw extractable energy above minimum SoC
-        raw_available_wh = max(self.soc_wh - self.min_soc_wh, 0.0)
-
-        # Maximum raw discharge due to power limit
-        max_raw_wh = self.max_charge_power_w  # TODO rename to max_discharge_power_w
-
-        # Actual raw withdrawal (internal)
-        raw_withdrawal_wh = min(raw_available_wh, max_raw_wh)
-
-        # Convert raw to delivered
-        max_deliverable_wh = raw_withdrawal_wh * self.discharging_efficiency
-
-        # Cap by requested delivered energy
-        delivered_wh = min(wh, max_deliverable_wh)
-
-        # Effective raw withdrawal based on what is delivered
-        raw_used_wh = delivered_wh / self.discharging_efficiency
-
-        # Update SoC
-        self.soc_wh -= raw_used_wh
-        self.soc_wh = max(self.soc_wh, self.min_soc_wh)
-
-        # Losses
-        losses_wh = raw_used_wh - delivered_wh
-
-        return delivered_wh, losses_wh
-
-    def charge_energy(
-        self,
-        wh: Optional[float],
-        hour: int,
-        charge_factor: float = 0.0,
-    ) -> tuple[float, float]:
-        """Charge energy into the battery.
-
-        Two **exclusive** modes:
-
-        Mode 1:
-            - `wh is not None` and `charge_factor == 0`
-            → The raw requested charge energy is `wh` (pre-efficiency).
-            → If remaining capacity is insufficient, charging is automatically limited.
-            → No exception is raised due to capacity limits.
-
-        Mode 2:
-            - `wh is None` and `charge_factor > 0`
-            → The raw requested energy is `max_charge_power_w * charge_factor`.
-            → If the request exceeds remaining capacity, the algorithm tries to
-              find a lower charge_factor that is compatible. If such a charge factor
-              exists, this hour’s charge_factor is replaced.
-            → If no charge factor can accommodate charging, the request is ignored
-              (`(0.0, 0.0)` is returned) and a penalty is applied elsewhere.
-
-        Charging is constrained by:
-            • Available SoC headroom (max_soc_wh − soc_wh)
-            • max_charge_power_w
-            • charging_efficiency
-
-        Args:
-            wh (float | None):
-                Requested raw energy [Wh] before efficiency.
-                Must be provided only for Mode 1 (charge_factor must be 0).
-
-            hour (int):
-                Time index. If charging is disabled at this hour (charge_array[hour] == 0),
-                returns `(0.0, 0.0)`.
-
-            charge_factor (float):
-                Fraction (0–1) of max charge power.
-                Must be >0 only in Mode 2 (`wh is None`).
-
-        Returns:
-            tuple[float, float]:
-                stored_wh : float
-                    Energy stored after efficiency [Wh].
-                losses_wh : float
-                    Conversion losses [Wh].
-
-        Raises:
-            ValueError:
-                - If the mode is ambiguous (neither Mode 1 nor Mode 2).
-                - If the final new SoC would exceed capacity_wh.
-
-        Notes:
-            stored_wh = raw_input_wh * charging_efficiency
-            losses_wh = raw_input_wh − stored_wh
-        """
-        # Charging allowed in this hour?
-        if hour is not None and self.charge_array[hour] == 0:
-            return 0.0, 0.0
-
-        # Provide fast (3x..5x) local read access (vs. self.xxx) for repetitive read access
-        soc_wh_fast = self.soc_wh
-        max_charge_power_w_fast = self.max_charge_power_w
-        charging_efficiency_fast = self.charging_efficiency
-
-        # Decide mode & determine raw_request_wh and raw_charge_wh
-        if wh is not None and charge_factor == 0.0:  # mode 1
-            raw_request_wh = wh
-            raw_charge_wh = max(self.max_soc_wh - soc_wh_fast, 0.0) / charging_efficiency_fast
-        elif wh is None and charge_factor > 0.0:  # mode 2
-            raw_request_wh = max_charge_power_w_fast * charge_factor
-            raw_charge_wh = max(self.max_soc_wh - soc_wh_fast, 0.0) / charging_efficiency_fast
-            if raw_request_wh > raw_charge_wh:
-                # Use a lower charge factor
-                lower_charge_factors = self._lower_charge_rates_desc(charge_factor)
-                for charge_factor in lower_charge_factors:
-                    raw_request_wh = max_charge_power_w_fast * charge_factor
-                    if raw_request_wh <= raw_charge_wh:
-                        self.charge_array[hour] = charge_factor
-                        break
-                if raw_request_wh > raw_charge_wh:
-                    # ignore request - penalty for missing SoC will be applied
-                    self.charge_array[hour] = 0
-                    return 0.0, 0.0
-        else:
-            raise ValueError(
-                f"{self.parameters.device_id}: charge_energy must be called either "
-                "with wh != None and charge_factor == 0, or with wh == None and charge_factor > 0."
-            )
-
-        # Remaining capacity
-        max_raw_wh = min(raw_charge_wh, max_charge_power_w_fast)
-
-        # Actual raw intake
-        raw_input_wh = raw_request_wh if raw_request_wh < max_raw_wh else max_raw_wh
-
-        # Apply efficiency
-        stored_wh = raw_input_wh * charging_efficiency_fast
-        new_soc = soc_wh_fast + stored_wh
-
-        if new_soc > self.capacity_wh:
-            raise ValueError(
-                f"{self.parameters.device_id}: SoC {new_soc} Wh exceeds capacity {self.capacity_wh} Wh"
-            )
-
-        self.soc_wh = new_soc
-        losses_wh = raw_input_wh - stored_wh
-
-        return stored_wh, losses_wh
-
-    def current_energy_content(self) -> float:
-        """Returns the current usable energy in the battery."""
-        usable_energy = (self.soc_wh - self.min_soc_wh) * self.discharging_efficiency
-        return max(usable_energy, 0.0)

src/akkudoktoreos/devices/genetic/homeappliance.py

@@ -1,105 +0,0 @@
-import numpy as np
-
-from akkudoktoreos.optimization.genetic.geneticdevices import HomeApplianceParameters
-from akkudoktoreos.utils.datetimeutil import (
-    TimeWindow,
-    TimeWindowSequence,
-    to_datetime,
-    to_duration,
-    to_time,
-)
-
-
-class HomeAppliance:
-    def __init__(
-        self,
-        parameters: HomeApplianceParameters,
-        optimization_hours: int,
-        prediction_hours: int,
-    ):
-        self.parameters: HomeApplianceParameters = parameters
-        self.prediction_hours = prediction_hours
-        self._setup()
-
-    def _setup(self) -> None:
-        """Sets up the home appliance parameters based provided parameters."""
-        self.load_curve = np.zeros(self.prediction_hours)  # Initialize the load curve with zeros
-        self.duration_h = self.parameters.duration_h
-        self.consumption_wh = self.parameters.consumption_wh
-        # setup possible start times
-        if self.parameters.time_windows is None:
-            self.parameters.time_windows = TimeWindowSequence(
-                windows=[
-                    TimeWindow(
-                        start_time=to_time("00:00"),
-                        duration=to_duration(f"{self.prediction_hours} hours"),
-                    ),
-                ]
-            )
-        start_datetime = to_datetime().set(hour=0, minute=0, second=0)
-        duration = to_duration(f"{self.duration_h} hours")
-        self.start_allowed: list[bool] = []
-        for hour in range(0, self.prediction_hours):
-            self.start_allowed.append(
-                self.parameters.time_windows.contains(
-                    start_datetime.add(hours=hour), duration=duration
-                )
-            )
-        start_earliest = self.parameters.time_windows.earliest_start_time(duration, start_datetime)
-        if start_earliest:
-            self.start_earliest = start_earliest.hour
-        else:
-            self.start_earliest = 0
-        start_latest = self.parameters.time_windows.latest_start_time(duration, start_datetime)
-        if start_latest:
-            self.start_latest = start_latest.hour
-        else:
-            self.start_latest = 23
-
-    def set_starting_time(self, start_hour: int, global_start_hour: int = 0) -> int:
-        """Sets the start time of the device and generates the corresponding load curve.
-
-        :param start_hour: The hour at which the device should start.
-        """
-        if not self.start_allowed[start_hour]:
-            # It is not allowed (by the time windows) to start the application at this time
-            if global_start_hour <= self.start_latest:
-                # There is a time window left to start the appliance. Use it
-                start_hour = self.start_latest
-            else:
-                # There is no time window left to run the application
-                # Set the start into tomorrow
-                start_hour = self.start_earliest + 24
-
-        self.reset_load_curve()
-
-        # Calculate power per hour based on total consumption and duration
-        power_per_hour = self.consumption_wh / self.duration_h  # Convert to watt-hours
-
-        # Set the power for the duration of use in the load curve array
-        if start_hour < len(self.load_curve):
-            end_hour = min(start_hour + self.duration_h, self.prediction_hours)
-            self.load_curve[start_hour:end_hour] = power_per_hour
-
-        return start_hour
-
-    def reset_load_curve(self) -> None:
-        """Resets the load curve."""
-        self.load_curve = np.zeros(self.prediction_hours)
-
-    def get_load_curve(self) -> np.ndarray:
-        """Returns the current load curve."""
-        return self.load_curve
-
-    def get_load_for_hour(self, hour: int) -> float:
-        """Returns the load for a specific hour.
-
-        :param hour: The hour for which the load is queried.
-        :return: The load in watts for the specified hour.
-        """
-        if hour < 0 or hour >= self.prediction_hours:
-            raise ValueError(
-                f"The specified hour {hour} is outside the available time frame {self.prediction_hours}."
-            )
-
-        return self.load_curve[hour]

src/akkudoktoreos/devices/heatpump.py

@@ -1,6 +1,6 @@
 from typing import List, Sequence
 
-from loguru import logger
+from akkudoktoreos.core.logging import get_logger
 
 
 class Heatpump:
@@ -22,6 +22,7 @@ class Heatpump:
     def __init__(self, max_heat_output: int, hours: int):
         self.max_heat_output = max_heat_output
         self.hours = hours
+        self.log = get_logger(__name__)
 
     def __check_outside_temperature_range__(self, temp_celsius: float) -> bool:
         """Check if temperature is in valid range between -100 and 100 degree Celsius.
@@ -58,7 +59,7 @@ class Heatpump:
                 f"Outside temperature '{outside_temperature_celsius}' not in range "
                 "(min: -100 Celsius, max: 100 Celsius)"
             )
-            logger.error(err_msg)
+            self.log.error(err_msg)
             raise ValueError(err_msg)
 
     def calculate_heating_output(self, outside_temperature_celsius: float) -> float:
@@ -86,7 +87,7 @@ class Heatpump:
                 f"Outside temperature '{outside_temperature_celsius}' not in range "
                 "(min: -100 Celsius, max: 100 Celsius)"
             )
-            logger.error(err_msg)
+            self.log.error(err_msg)
             raise ValueError(err_msg)
 
     def calculate_heat_power(self, outside_temperature_celsius: float) -> float:
@@ -110,7 +111,7 @@ class Heatpump:
                 f"Outside temperature '{outside_temperature_celsius}' not in range "
                 "(min: -100 Celsius, max: 100 Celsius)"
             )
-            logger.error(err_msg)
+            self.log.error(err_msg)
             raise ValueError(err_msg)
 
     def simulate_24h(self, temperatures: Sequence[float]) -> List[float]:

src/akkudoktoreos/devices/inverter.py

@@ -1,32 +1,49 @@
 from typing import Optional
 
-from loguru import logger
+from pydantic import Field
 
-from akkudoktoreos.devices.genetic.battery import Battery
-from akkudoktoreos.optimization.genetic.geneticdevices import InverterParameters
+from akkudoktoreos.core.logging import get_logger
+from akkudoktoreos.devices.devicesabc import DeviceBase, DeviceParameters
 from akkudoktoreos.prediction.interpolator import get_eos_load_interpolator
 
+logger = get_logger(__name__)
 
-class Inverter:
+
+class InverterParameters(DeviceParameters):
+    """Inverter Device Simulation Configuration."""
+
+    device_id: str = Field(description="ID of inverter", examples=["inverter1"])
+    max_power_wh: float = Field(gt=0, examples=[10000])
+    battery_id: Optional[str] = Field(
+        default=None, description="ID of battery", examples=[None, "battery1"]
+    )
+
+
+class Inverter(DeviceBase):
     def __init__(
         self,
-        parameters: InverterParameters,
-        battery: Optional[Battery] = None,
+        parameters: Optional[InverterParameters] = None,
     ):
-        self.parameters: InverterParameters = parameters
-        self.battery: Optional[Battery] = battery
-        self._setup()
+        self.parameters: Optional[InverterParameters] = None
+        super().__init__(parameters)
 
     def _setup(self) -> None:
-        if self.battery and self.parameters.battery_id != self.battery.parameters.device_id:
-            error_msg = f"Battery ID mismatch - {self.parameters.battery_id} is configured; got {self.battery.parameters.device_id}."
+        assert self.parameters is not None
+        if self.parameters.battery_id is None:
+            # For the moment raise exception
+            # TODO: Make battery configurable by config
+            error_msg = "Battery for PV inverter is mandatory."
             logger.error(error_msg)
-            raise ValueError(error_msg)
+            raise NotImplementedError(error_msg)
         self.self_consumption_predictor = get_eos_load_interpolator()
         self.max_power_wh = (
             self.parameters.max_power_wh
         )  # Maximum power that the inverter can handle
 
+    def _post_setup(self) -> None:
+        assert self.parameters is not None
+        self.battery = self.devices.get_device_by_id(self.parameters.battery_id)
+
     def process_energy(
         self, generation: float, consumption: float, hour: int
     ) -> tuple[float, float, float, float]:
@@ -43,7 +60,6 @@ class Inverter:
                 grid_import = -remaining_power  # Negative indicates feeding into the grid
                 self_consumption = self.max_power_wh
             else:
-                # Calculate scr using cached results per energy management/optimization run
                 scr = self.self_consumption_predictor.calculate_self_consumption(
                     consumption, generation
                 )
@@ -55,12 +71,11 @@ class Inverter:
 
                 if remaining_load_evq > 0:
                     # Akku muss den Restverbrauch decken
-                    if self.battery:
-                        from_battery, discharge_losses = self.battery.discharge_energy(
-                            remaining_load_evq, hour
-                        )
-                        remaining_load_evq -= from_battery  # Restverbrauch nach Akkuentladung
-                        losses += discharge_losses
+                    from_battery, discharge_losses = self.battery.discharge_energy(
+                        remaining_load_evq, hour
+                    )
+                    remaining_load_evq -= from_battery  # Restverbrauch nach Akkuentladung
+                    losses += discharge_losses
 
                     # Wenn der Akku den Restverbrauch nicht vollständig decken kann, wird der Rest ins Netz gezogen
                     if remaining_load_evq > 0:
@@ -71,13 +86,10 @@ class Inverter:
 
                 if remaining_power > 0:
                     # Load battery with excess energy
-                    if self.battery:
-                        charged_energie, charge_losses = self.battery.charge_energy(
-                            remaining_power, hour
-                        )
-                        remaining_surplus = remaining_power - (charged_energie + charge_losses)
-                    else:
-                        remaining_surplus = remaining_power
+                    charged_energie, charge_losses = self.battery.charge_energy(
+                        remaining_power, hour
+                    )
+                    remaining_surplus = remaining_power - (charged_energie + charge_losses)
 
                     # Feed-in to the grid based on remaining capacity
                     if remaining_surplus > self.max_power_wh - consumption:
@@ -97,13 +109,10 @@ class Inverter:
             available_ac_power = max(self.max_power_wh - generation, 0)
 
             # Discharge battery to cover shortfall, if possible
-            if self.battery:
-                battery_discharge, discharge_losses = self.battery.discharge_energy(
-                    min(shortfall, available_ac_power), hour
-                )
-                losses += discharge_losses
-            else:
-                battery_discharge = 0
+            battery_discharge, discharge_losses = self.battery.discharge_energy(
+                min(shortfall, available_ac_power), hour
+            )
+            losses += discharge_losses
 
             # Draw remaining required power from the grid (discharge_losses are already substraved in the battery)
             grid_import = shortfall - battery_discharge

src/akkudoktoreos/devices/settings.py

@@ -0,0 +1,27 @@
+from typing import Optional
+
+from pydantic import Field
+
+from akkudoktoreos.config.configabc import SettingsBaseModel
+from akkudoktoreos.core.logging import get_logger
+from akkudoktoreos.devices.battery import BaseBatteryParameters
+from akkudoktoreos.devices.generic import HomeApplianceParameters
+from akkudoktoreos.devices.inverter import InverterParameters
+
+logger = get_logger(__name__)
+
+
+class DevicesCommonSettings(SettingsBaseModel):
+    """Base configuration for devices simulation settings."""
+
+    batteries: Optional[list[BaseBatteryParameters]] = Field(
+        default=None,
+        description="List of battery/ev devices",
+        examples=[[{"device_id": "battery1", "capacity_wh": 8000}]],
+    )
+    inverters: Optional[list[InverterParameters]] = Field(
+        default=None, description="List of inverters", examples=[[]]
+    )
+    home_appliances: Optional[list[HomeApplianceParameters]] = Field(
+        default=None, description="List of home appliances", examples=[[]]
+    )

src/akkudoktoreos/measurement/measurement.py

@@ -6,69 +6,92 @@ data records for measurements.
 The measurements can be added programmatically or imported from a file or JSON string.
 """
 
-from typing import Any, Optional
+from typing import Any, ClassVar, List, Optional
 
 import numpy as np
-from loguru import logger
 from numpydantic import NDArray, Shape
+from pendulum import DateTime, Duration
 from pydantic import Field, computed_field
 
 from akkudoktoreos.config.configabc import SettingsBaseModel
 from akkudoktoreos.core.coreabc import SingletonMixin
 from akkudoktoreos.core.dataabc import DataImportMixin, DataRecord, DataSequence
-from akkudoktoreos.utils.datetimeutil import DateTime, Duration, to_duration
+from akkudoktoreos.core.logging import get_logger
+from akkudoktoreos.utils.datetimeutil import to_duration
+
+logger = get_logger(__name__)
 
 
 class MeasurementCommonSettings(SettingsBaseModel):
     """Measurement Configuration."""
 
-    load_emr_keys: Optional[list[str]] = Field(
-        default=None,
-        description="The keys of the measurements that are energy meter readings of a load [kWh].",
-        examples=[["load0_emr"]],
+    load0_name: Optional[str] = Field(
+        default=None, description="Name of the load0 source", examples=["Household", "Heat Pump"]
     )
-
-    grid_export_emr_keys: Optional[list[str]] = Field(
-        default=None,
-        description="The keys of the measurements that are energy meter readings of energy export to grid [kWh].",
-        examples=[["grid_export_emr"]],
+    load1_name: Optional[str] = Field(
+        default=None, description="Name of the load1 source", examples=[None]
     )
-
-    grid_import_emr_keys: Optional[list[str]] = Field(
-        default=None,
-        description="The keys of the measurements that are energy meter readings of energy import from grid [kWh].",
-        examples=[["grid_import_emr"]],
+    load2_name: Optional[str] = Field(
+        default=None, description="Name of the load2 source", examples=[None]
     )
-
-    pv_production_emr_keys: Optional[list[str]] = Field(
-        default=None,
-        description="The keys of the measurements that are PV production energy meter readings [kWh].",
-        examples=[["pv1_emr"]],
+    load3_name: Optional[str] = Field(
+        default=None, description="Name of the load3 source", examples=[None]
+    )
+    load4_name: Optional[str] = Field(
+        default=None, description="Name of the load4 source", examples=[None]
     )
-
-    ## Computed fields
-    @computed_field  # type: ignore[prop-decorator]
-    @property
-    def keys(self) -> list[str]:
-        """The keys of the measurements that can be stored."""
-        key_list = []
-        for key in self.__class__.model_fields.keys():
-            if key.endswith("_keys") and (value := getattr(self, key)):
-                key_list.extend(value)
-        return sorted(set(key_list))
 
 
 class MeasurementDataRecord(DataRecord):
-    """Represents a measurement data record containing various measurements at a specific datetime."""
+    """Represents a measurement data record containing various measurements at a specific datetime.
 
-    @classmethod
-    def configured_data_keys(cls) -> Optional[list[str]]:
-        """Return the keys for the configured field like data."""
-        keys = cls.config.measurement.keys
-        # Add measurment keys that are needed/ handled by the resource/ device simulations.
-        if cls.config.devices.measurement_keys:
-            keys.extend(cls.config.devices.measurement_keys)
-        return keys
+    Attributes:
+        date_time (Optional[DateTime]): The datetime of the record.
+    """
+
+    # Single loads, to be aggregated to total load
+    load0_mr: Optional[float] = Field(
+        default=None, ge=0, description="Load0 meter reading [kWh]", examples=[40421]
+    )
+    load1_mr: Optional[float] = Field(
+        default=None, ge=0, description="Load1 meter reading [kWh]", examples=[None]
+    )
+    load2_mr: Optional[float] = Field(
+        default=None, ge=0, description="Load2 meter reading [kWh]", examples=[None]
+    )
+    load3_mr: Optional[float] = Field(
+        default=None, ge=0, description="Load3 meter reading [kWh]", examples=[None]
+    )
+    load4_mr: Optional[float] = Field(
+        default=None, ge=0, description="Load4 meter reading [kWh]", examples=[None]
+    )
+
+    max_loads: ClassVar[int] = 5  # Maximum number of loads that can be set
+
+    grid_export_mr: Optional[float] = Field(
+        default=None, ge=0, description="Export to grid meter reading [kWh]", examples=[1000]
+    )
+
+    grid_import_mr: Optional[float] = Field(
+        default=None, ge=0, description="Import from grid meter reading [kWh]", examples=[1000]
+    )
+
+    # Computed fields
+    @computed_field  # type: ignore[prop-decorator]
+    @property
+    def loads(self) -> List[str]:
+        """Compute a list of active loads."""
+        active_loads = []
+
+        # Loop through loadx
+        for i in range(self.max_loads):
+            load_attr = f"load{i}_mr"
+
+            # Check if either attribute is set and add to active loads
+            if getattr(self, load_attr, None):
+                active_loads.append(load_attr)
+
+        return active_loads
 
 
 class Measurement(SingletonMixin, DataImportMixin, DataSequence):
@@ -77,10 +100,14 @@ class Measurement(SingletonMixin, DataImportMixin, DataSequence):
     Measurements can be provided programmatically or read from JSON string or file.
     """
 
-    records: list[MeasurementDataRecord] = Field(
-        default_factory=list, description="list of measurement data records"
+    records: List[MeasurementDataRecord] = Field(
+        default_factory=list, description="List of measurement data records"
     )
 
+    topics: ClassVar[List[str]] = [
+        "load",
+    ]
+
     def __init__(self, *args: Any, **kwargs: Any) -> None:
         if hasattr(self, "_initialized"):
             return
@@ -116,6 +143,34 @@ class Measurement(SingletonMixin, DataImportMixin, DataSequence):
         # Return ceiling of division to include partial intervals
         return int(np.ceil(diff_seconds / interval_seconds))
 
+    def name_to_key(self, name: str, topic: str) -> Optional[str]:
+        """Provides measurement key for given name and topic."""
+        topic = topic.lower()
+
+        if topic not in self.topics:
+            return None
+
+        topic_keys = [
+            key for key in self.config.measurement.model_fields.keys() if key.startswith(topic)
+        ]
+        key = None
+        if topic == "load":
+            for config_key in topic_keys:
+                if (
+                    config_key.endswith("_name")
+                    and getattr(self.config.measurement, config_key) == name
+                ):
+                    key = topic + config_key[len(topic) : len(topic) + 1] + "_mr"
+                    break
+
+        if key is not None and key not in self.record_keys:
+            # Should never happen
+            error_msg = f"Key '{key}' not available."
+            logger.error(error_msg)
+            raise KeyError(error_msg)
+
+        return key
+
     def _energy_from_meter_readings(
         self,
         key: str,
@@ -200,20 +255,17 @@ class Measurement(SingletonMixin, DataImportMixin, DataSequence):
             end_datetime = self[-1].date_time
         size = self._interval_count(start_datetime, end_datetime, interval)
         load_total_array = np.zeros(size)
-        # Loop through all loads
-        if isinstance(self.config.measurement.load_emr_keys, list):
-            for key in self.config.measurement.load_emr_keys:
-                # Calculate load per interval
-                load_array = self._energy_from_meter_readings(
-                    key=key,
-                    start_datetime=start_datetime,
-                    end_datetime=end_datetime,
-                    interval=interval,
-                )
-                # Add calculated load to total load
-                load_total_array += load_array
-                debug_msg = f"Total load '{key}' calculation: {load_total_array}"
-                logger.debug(debug_msg)
+        # Loop through load<x>_mr
+        for i in range(self.record_class().max_loads):
+            key = f"load{i}_mr"
+            # Calculate load per interval
+            load_array = self._energy_from_meter_readings(
+                key=key, start_datetime=start_datetime, end_datetime=end_datetime, interval=interval
+            )
+            # Add calculated load to total load
+            load_total_array += load_array
+            debug_msg = f"Total load '{key}' calculation: {load_total_array}"
+            logger.debug(debug_msg)
 
         return load_total_array
 

src/akkudoktoreos/optimization/genetic.py

@@ -0,0 +1,679 @@
+import random
+import time
+from typing import Any, Optional
+
+import numpy as np
+from deap import algorithms, base, creator, tools
+from pydantic import Field, field_validator, model_validator
+from typing_extensions import Self
+
+from akkudoktoreos.core.coreabc import (
+    ConfigMixin,
+    DevicesMixin,
+    EnergyManagementSystemMixin,
+)
+from akkudoktoreos.core.ems import EnergyManagementParameters, SimulationResult
+from akkudoktoreos.core.logging import get_logger
+from akkudoktoreos.core.pydantic import ParametersBaseModel
+from akkudoktoreos.devices.battery import (
+    Battery,
+    ElectricVehicleParameters,
+    ElectricVehicleResult,
+    SolarPanelBatteryParameters,
+)
+from akkudoktoreos.devices.generic import HomeAppliance, HomeApplianceParameters
+from akkudoktoreos.devices.inverter import Inverter, InverterParameters
+from akkudoktoreos.utils.utils import NumpyEncoder
+
+logger = get_logger(__name__)
+
+
+class OptimizationParameters(ParametersBaseModel):
+    ems: EnergyManagementParameters
+    pv_akku: Optional[SolarPanelBatteryParameters]
+    inverter: Optional[InverterParameters]
+    eauto: Optional[ElectricVehicleParameters]
+    dishwasher: Optional[HomeApplianceParameters] = None
+    temperature_forecast: Optional[list[Optional[float]]] = Field(
+        default=None,
+        description="An array of floats representing the temperature forecast in degrees Celsius for different time intervals.",
+    )
+    start_solution: Optional[list[float]] = Field(
+        default=None, description="Can be `null` or contain a previous solution (if available)."
+    )
+
+    @model_validator(mode="after")
+    def validate_list_length(self) -> Self:
+        arr_length = len(self.ems.pv_prognose_wh)
+        if self.temperature_forecast is not None and arr_length != len(self.temperature_forecast):
+            raise ValueError("Input lists have different lengths")
+        return self
+
+    @field_validator("start_solution")
+    def validate_start_solution(
+        cls, start_solution: Optional[list[float]]
+    ) -> Optional[list[float]]:
+        if start_solution is not None and len(start_solution) < 2:
+            raise ValueError("Requires at least two values.")
+        return start_solution
+
+
+class OptimizeResponse(ParametersBaseModel):
+    """**Note**: The first value of "Last_Wh_per_hour", "Netzeinspeisung_Wh_per_hour", and "Netzbezug_Wh_per_hour", will be set to null in the JSON output and represented as NaN or None in the corresponding classes' data returns. This approach is adopted to ensure that the current hour's processing remains unchanged."""
+
+    ac_charge: list[float] = Field(
+        description="Array with AC charging values as relative power (0-1), other values set to 0."
+    )
+    dc_charge: list[float] = Field(
+        description="Array with DC charging values as relative power (0-1), other values set to 0."
+    )
+    discharge_allowed: list[int] = Field(
+        description="Array with discharge values (1 for discharge, 0 otherwise)."
+    )
+    eautocharge_hours_float: Optional[list[float]] = Field(description="TBD")
+    result: SimulationResult
+    eauto_obj: Optional[ElectricVehicleResult]
+    start_solution: Optional[list[float]] = Field(
+        default=None,
+        description="An array of binary values (0 or 1) representing a possible starting solution for the simulation.",
+    )
+    washingstart: Optional[int] = Field(
+        default=None,
+        description="Can be `null` or contain an object representing the start of washing (if applicable).",
+    )
+
+    @field_validator(
+        "ac_charge",
+        "dc_charge",
+        "discharge_allowed",
+        mode="before",
+    )
+    def convert_numpy(cls, field: Any) -> Any:
+        return NumpyEncoder.convert_numpy(field)[0]
+
+    @field_validator(
+        "eauto_obj",
+        mode="before",
+    )
+    def convert_eauto(cls, field: Any) -> Any:
+        if isinstance(field, Battery):
+            return ElectricVehicleResult(**field.to_dict())
+        return field
+
+
+class optimization_problem(ConfigMixin, DevicesMixin, EnergyManagementSystemMixin):
+    def __init__(
+        self,
+        verbose: bool = False,
+        fixed_seed: Optional[int] = None,
+    ):
+        """Initialize the optimization problem with the required parameters."""
+        self.opti_param: dict[str, Any] = {}
+        self.fixed_eauto_hours = self.config.prediction.hours - self.config.optimization.hours
+        self.possible_charge_values = self.config.optimization.ev_available_charge_rates_percent
+        self.verbose = verbose
+        self.fix_seed = fixed_seed
+        self.optimize_ev = True
+        self.optimize_dc_charge = False
+        self.fitness_history: dict[str, Any] = {}
+
+        # Set a fixed seed for random operations if provided or in debug mode
+        if self.fix_seed is not None:
+            random.seed(self.fix_seed)
+        elif logger.level == "DEBUG":
+            self.fix_seed = random.randint(1, 100000000000)
+            random.seed(self.fix_seed)
+
+    def decode_charge_discharge(
+        self, discharge_hours_bin: np.ndarray
+    ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+        """Decode the input array into ac_charge, dc_charge, and discharge arrays."""
+        discharge_hours_bin_np = np.array(discharge_hours_bin)
+        len_ac = len(self.possible_charge_values)
+
+        # Categorization:
+        # Idle:       0 .. len_ac-1
+        # Discharge:  len_ac .. 2*len_ac - 1
+        # AC Charge:  2*len_ac .. 3*len_ac - 1
+        # DC optional: 3*len_ac (not allowed), 3*len_ac + 1 (allowed)
+
+        # Idle has no charge, Discharge has binary 1, AC Charge has corresponding values
+        # Idle states
+        idle_mask = (discharge_hours_bin_np >= 0) & (discharge_hours_bin_np < len_ac)
+
+        # Discharge states
+        discharge_mask = (discharge_hours_bin_np >= len_ac) & (discharge_hours_bin_np < 2 * len_ac)
+
+        # AC states
+        ac_mask = (discharge_hours_bin_np >= 2 * len_ac) & (discharge_hours_bin_np < 3 * len_ac)
+        ac_indices = (discharge_hours_bin_np[ac_mask] - 2 * len_ac).astype(int)
+
+        # DC states (if enabled)
+        if self.optimize_dc_charge:
+            dc_not_allowed_state = 3 * len_ac
+            dc_allowed_state = 3 * len_ac + 1
+            dc_charge = np.where(discharge_hours_bin_np == dc_allowed_state, 1, 0)
+        else:
+            dc_charge = np.ones_like(discharge_hours_bin_np, dtype=float)
+
+        # Generate the result arrays
+        discharge = np.zeros_like(discharge_hours_bin_np, dtype=int)
+        discharge[discharge_mask] = 1  # Set Discharge states to 1
+
+        ac_charge = np.zeros_like(discharge_hours_bin_np, dtype=float)
+        ac_charge[ac_mask] = [self.possible_charge_values[i] for i in ac_indices]
+
+        # Idle is just 0, already default.
+
+        return ac_charge, dc_charge, discharge
+
+    def mutate(self, individual: list[int]) -> tuple[list[int]]:
+        """Custom mutation function for the individual."""
+        # Calculate the number of states
+        len_ac = len(self.possible_charge_values)
+        if self.optimize_dc_charge:
+            total_states = 3 * len_ac + 2
+        else:
+            total_states = 3 * len_ac
+
+        # 1. Mutating the charge_discharge part
+        charge_discharge_part = individual[: self.config.prediction.hours]
+        (charge_discharge_mutated,) = self.toolbox.mutate_charge_discharge(charge_discharge_part)
+
+        # Instead of a fixed clamping to 0..8 or 0..6 dynamically:
+        charge_discharge_mutated = np.clip(charge_discharge_mutated, 0, total_states - 1)
+        individual[: self.config.prediction.hours] = charge_discharge_mutated
+
+        # 2. Mutating the EV charge part, if active
+        if self.optimize_ev:
+            ev_charge_part = individual[
+                self.config.prediction.hours : self.config.prediction.hours * 2
+            ]
+            (ev_charge_part_mutated,) = self.toolbox.mutate_ev_charge_index(ev_charge_part)
+            ev_charge_part_mutated[self.config.prediction.hours - self.fixed_eauto_hours :] = [
+                0
+            ] * self.fixed_eauto_hours
+            individual[self.config.prediction.hours : self.config.prediction.hours * 2] = (
+                ev_charge_part_mutated
+            )
+
+        # 3. Mutating the appliance start time, if applicable
+        if self.opti_param["home_appliance"] > 0:
+            appliance_part = [individual[-1]]
+            (appliance_part_mutated,) = self.toolbox.mutate_hour(appliance_part)
+            individual[-1] = appliance_part_mutated[0]
+
+        return (individual,)
+
+    # Method to create an individual based on the conditions
+    def create_individual(self) -> list[int]:
+        # Start with discharge states for the individual
+        individual_components = [
+            self.toolbox.attr_discharge_state() for _ in range(self.config.prediction.hours)
+        ]
+
+        # Add EV charge index values if optimize_ev is True
+        if self.optimize_ev:
+            individual_components += [
+                self.toolbox.attr_ev_charge_index() for _ in range(self.config.prediction.hours)
+            ]
+
+        # Add the start time of the household appliance if it's being optimized
+        if self.opti_param["home_appliance"] > 0:
+            individual_components += [self.toolbox.attr_int()]
+
+        return creator.Individual(individual_components)
+
+    def merge_individual(
+        self,
+        discharge_hours_bin: np.ndarray,
+        eautocharge_hours_index: Optional[np.ndarray],
+        washingstart_int: Optional[int],
+    ) -> list[int]:
+        """Merge the individual components back into a single solution list.
+
+        Parameters:
+            discharge_hours_bin (np.ndarray): Binary discharge hours.
+            eautocharge_hours_index (Optional[np.ndarray]): EV charge hours as integers, or None.
+            washingstart_int (Optional[int]): Dishwasher start time as integer, or None.
+
+        Returns:
+            list[int]: The merged individual solution as a list of integers.
+        """
+        # Start with the discharge hours
+        individual = discharge_hours_bin.tolist()
+
+        # Add EV charge hours if applicable
+        if self.optimize_ev and eautocharge_hours_index is not None:
+            individual.extend(eautocharge_hours_index.tolist())
+        elif self.optimize_ev:
+            # Falls optimize_ev aktiv ist, aber keine EV-Daten vorhanden sind, fügen wir Nullen hinzu
+            individual.extend([0] * self.config.prediction.hours)
+
+        # Add dishwasher start time if applicable
+        if self.opti_param.get("home_appliance", 0) > 0 and washingstart_int is not None:
+            individual.append(washingstart_int)
+        elif self.opti_param.get("home_appliance", 0) > 0:
+            # Falls ein Haushaltsgerät optimiert wird, aber kein Startzeitpunkt vorhanden ist
+            individual.append(0)
+
+        return individual
+
+    def split_individual(
+        self, individual: list[int]
+    ) -> tuple[np.ndarray, Optional[np.ndarray], Optional[int]]:
+        """Split the individual solution into its components.
+
+        Components:
+        1. Discharge hours (binary as int NumPy array),
+        2. Electric vehicle charge hours (float as int NumPy array, if applicable),
+        3. Dishwasher start time (integer if applicable).
+        """
+        # Discharge hours as a NumPy array of ints
+        discharge_hours_bin = np.array(individual[: self.config.prediction.hours], dtype=int)
+
+        # EV charge hours as a NumPy array of ints (if optimize_ev is True)
+        eautocharge_hours_index = (
+            # append ev charging states to individual
+            np.array(
+                individual[self.config.prediction.hours : self.config.prediction.hours * 2],
+                dtype=int,
+            )
+            if self.optimize_ev
+            else None
+        )
+
+        # Washing machine start time as an integer (if applicable)
+        washingstart_int = (
+            int(individual[-1])
+            if self.opti_param and self.opti_param.get("home_appliance", 0) > 0
+            else None
+        )
+
+        return discharge_hours_bin, eautocharge_hours_index, washingstart_int
+
+    def setup_deap_environment(self, opti_param: dict[str, Any], start_hour: int) -> None:
+        """Set up the DEAP environment with fitness and individual creation rules."""
+        self.opti_param = opti_param
+
+        # Remove existing definitions if any
+        for attr in ["FitnessMin", "Individual"]:
+            if attr in creator.__dict__:
+                del creator.__dict__[attr]
+
+        creator.create("FitnessMin", base.Fitness, weights=(-1.0,))
+        creator.create("Individual", list, fitness=creator.FitnessMin)
+
+        self.toolbox = base.Toolbox()
+        len_ac = len(self.possible_charge_values)
+
+        # Total number of states without DC:
+        # Idle: len_ac states
+        # Discharge: len_ac states
+        # AC-Charge: len_ac states
+        # Total without DC: 3 * len_ac
+
+        # With DC: + 2 states
+        if self.optimize_dc_charge:
+            total_states = 3 * len_ac + 2
+        else:
+            total_states = 3 * len_ac
+
+        # State space: 0 .. (total_states - 1)
+        self.toolbox.register("attr_discharge_state", random.randint, 0, total_states - 1)
+
+        # EV attributes
+        if self.optimize_ev:
+            self.toolbox.register(
+                "attr_ev_charge_index",
+                random.randint,
+                0,
+                len_ac - 1,
+            )
+
+        # Household appliance start time
+        self.toolbox.register("attr_int", random.randint, start_hour, 23)
+
+        self.toolbox.register("individual", self.create_individual)
+        self.toolbox.register("population", tools.initRepeat, list, self.toolbox.individual)
+        self.toolbox.register("mate", tools.cxTwoPoint)
+
+        # Mutation operator for charge/discharge states
+        self.toolbox.register(
+            "mutate_charge_discharge", tools.mutUniformInt, low=0, up=total_states - 1, indpb=0.2
+        )
+
+        # Mutation operator for EV states
+        self.toolbox.register(
+            "mutate_ev_charge_index",
+            tools.mutUniformInt,
+            low=0,
+            up=len_ac - 1,
+            indpb=0.2,
+        )
+
+        # Mutation for household appliance
+        self.toolbox.register("mutate_hour", tools.mutUniformInt, low=start_hour, up=23, indpb=0.2)
+
+        # Custom mutate function remains unchanged
+        self.toolbox.register("mutate", self.mutate)
+        self.toolbox.register("select", tools.selTournament, tournsize=3)
+
+    def evaluate_inner(self, individual: list[int]) -> dict[str, Any]:
+        """Simulates the energy management system (EMS) using the provided individual solution.
+
+        This is an internal function.
+        """
+        self.ems.reset()
+        discharge_hours_bin, eautocharge_hours_index, washingstart_int = self.split_individual(
+            individual
+        )
+        if self.opti_param.get("home_appliance", 0) > 0:
+            self.ems.set_home_appliance_start(
+                washingstart_int, global_start_hour=self.ems.start_datetime.hour
+            )
+
+        ac, dc, discharge = self.decode_charge_discharge(discharge_hours_bin)
+
+        self.ems.set_akku_discharge_hours(discharge)
+        # Set DC charge hours only if DC optimization is enabled
+        if self.optimize_dc_charge:
+            self.ems.set_akku_dc_charge_hours(dc)
+        self.ems.set_akku_ac_charge_hours(ac)
+
+        if eautocharge_hours_index is not None:
+            eautocharge_hours_float = np.array(
+                [self.possible_charge_values[i] for i in eautocharge_hours_index],
+                float,
+            )
+            self.ems.set_ev_charge_hours(eautocharge_hours_float)
+        else:
+            self.ems.set_ev_charge_hours(np.full(self.config.prediction.hours, 0))
+
+        return self.ems.simulate(self.ems.start_datetime.hour)
+
+    def evaluate(
+        self,
+        individual: list[int],
+        parameters: OptimizationParameters,
+        start_hour: int,
+        worst_case: bool,
+    ) -> tuple[float]:
+        """Evaluate the fitness of an individual solution based on the simulation results."""
+        try:
+            o = self.evaluate_inner(individual)
+        except Exception as e:
+            return (100000.0,)  # Return a high penalty in case of an exception
+
+        gesamtbilanz = o["Gesamtbilanz_Euro"] * (-1.0 if worst_case else 1.0)
+
+        discharge_hours_bin, eautocharge_hours_index, washingstart_int = self.split_individual(
+            individual
+        )
+
+        # EV 100% & charge not allowed
+        if self.optimize_ev:
+            eauto_soc_per_hour = np.array(o.get("EAuto_SoC_pro_Stunde", []))  # Beispielkey
+
+            if eauto_soc_per_hour is None or eautocharge_hours_index is None:
+                raise ValueError("eauto_soc_per_hour or eautocharge_hours_index is None")
+            min_length = min(eauto_soc_per_hour.size, eautocharge_hours_index.size)
+            eauto_soc_per_hour_tail = eauto_soc_per_hour[-min_length:]
+            eautocharge_hours_index_tail = eautocharge_hours_index[-min_length:]
+
+            # Mask
+            invalid_charge_mask = (eauto_soc_per_hour_tail == 100) & (
+                eautocharge_hours_index_tail > 0
+            )
+
+            if np.any(invalid_charge_mask):
+                invalid_indices = np.where(invalid_charge_mask)[0]
+                if len(invalid_indices) > 1:
+                    eautocharge_hours_index_tail[invalid_indices[1:]] = 0
+
+                eautocharge_hours_index[-min_length:] = eautocharge_hours_index_tail.tolist()
+
+                adjusted_individual = self.merge_individual(
+                    discharge_hours_bin, eautocharge_hours_index, washingstart_int
+                )
+
+                individual[:] = adjusted_individual
+
+        # New check: Activate discharge when battery SoC is 0
+        # battery_soc_per_hour = np.array(
+        #     o.get("akku_soc_pro_stunde", [])
+        # )  # Example key for battery SoC
+
+        # if battery_soc_per_hour is not None:
+        #     if battery_soc_per_hour is None or discharge_hours_bin is None:
+        #         raise ValueError("battery_soc_per_hour or discharge_hours_bin is None")
+        #     min_length = min(battery_soc_per_hour.size, discharge_hours_bin.size)
+        #     battery_soc_per_hour_tail = battery_soc_per_hour[-min_length:]
+        #     discharge_hours_bin_tail = discharge_hours_bin[-min_length:]
+        #     len_ac = len(self.config.optimization.ev_available_charge_rates_percent)
+
+        #     # # Find hours where battery SoC is 0
+        #     # zero_soc_mask = battery_soc_per_hour_tail == 0
+        #     # discharge_hours_bin_tail[zero_soc_mask] = (
+        #     #     len_ac + 2
+        #     # )  # Activate discharge for these hours
+
+        #     # When Battery SoC then set the Discharge randomly to 0 or 1. otherwise it's very unlikely to get a state where a battery can store energy for a longer time
+        #     # Find hours where battery SoC is 0
+        #     zero_soc_mask = battery_soc_per_hour_tail == 0
+        #     # discharge_hours_bin_tail[zero_soc_mask] = (
+        #     # len_ac + 2
+        #     # )  # Activate discharge for these hours
+        #     set_to_len_ac_plus_2 = np.random.rand() < 0.5  # True mit 50% Wahrscheinlichkeit
+
+        #     # Werte setzen basierend auf der zufälligen Entscheidung
+        #     value_to_set = len_ac + 2 if set_to_len_ac_plus_2 else 0
+        #     discharge_hours_bin_tail[zero_soc_mask] = value_to_set
+
+        #     # Merge the updated discharge_hours_bin back into the individual
+        #     adjusted_individual = self.merge_individual(
+        #         discharge_hours_bin, eautocharge_hours_index, washingstart_int
+        #     )
+        #     individual[:] = adjusted_individual
+
+        # More metrics
+        individual.extra_data = (  # type: ignore[attr-defined]
+            o["Gesamtbilanz_Euro"],
+            o["Gesamt_Verluste"],
+            parameters.eauto.min_soc_percentage - self.ems.ev.current_soc_percentage()
+            if parameters.eauto and self.ems.ev
+            else 0,
+        )
+
+        # Adjust total balance with battery value and penalties for unmet SOC
+        restwert_akku = (
+            self.ems.battery.current_energy_content() * parameters.ems.preis_euro_pro_wh_akku
+        )
+        gesamtbilanz += -restwert_akku
+
+        if self.optimize_ev:
+            gesamtbilanz += max(
+                0,
+                (
+                    parameters.eauto.min_soc_percentage - self.ems.ev.current_soc_percentage()
+                    if parameters.eauto and self.ems.ev
+                    else 0
+                )
+                * self.config.optimization.penalty,
+            )
+
+        return (gesamtbilanz,)
+
+    def optimize(
+        self, start_solution: Optional[list[float]] = None, ngen: int = 200
+    ) -> tuple[Any, dict[str, list[Any]]]:
+        """Run the optimization process using a genetic algorithm."""
+        population = self.toolbox.population(n=300)
+        hof = tools.HallOfFame(1)
+        stats = tools.Statistics(lambda ind: ind.fitness.values)
+        stats.register("min", np.min)
+        stats.register("avg", np.mean)
+        stats.register("max", np.max)
+
+        if self.verbose:
+            print("Start optimize:", start_solution)
+
+        # Insert the start solution into the population if provided
+        if start_solution is not None:
+            for _ in range(10):
+                population.insert(0, creator.Individual(start_solution))
+
+        # Run the evolutionary algorithm
+        pop, log = algorithms.eaMuPlusLambda(
+            population,
+            self.toolbox,
+            mu=100,
+            lambda_=150,
+            cxpb=0.6,
+            mutpb=0.4,
+            ngen=ngen,
+            stats=stats,
+            halloffame=hof,
+            verbose=self.verbose,
+        )
+
+        # Store fitness history
+        self.fitness_history = {
+            "gen": log.select("gen"),  # Generation numbers (X-axis)
+            "avg": log.select("avg"),  # Average fitness for each generation (Y-axis)
+            "max": log.select("max"),  # Maximum fitness for each generation (Y-axis)
+            "min": log.select("min"),  # Minimum fitness for each generation (Y-axis)
+        }
+
+        member: dict[str, list[float]] = {"bilanz": [], "verluste": [], "nebenbedingung": []}
+        for ind in population:
+            if hasattr(ind, "extra_data"):
+                extra_value1, extra_value2, extra_value3 = ind.extra_data
+                member["bilanz"].append(extra_value1)
+                member["verluste"].append(extra_value2)
+                member["nebenbedingung"].append(extra_value3)
+
+        return hof[0], member
+
+    def optimierung_ems(
+        self,
+        parameters: OptimizationParameters,
+        start_hour: Optional[int] = None,
+        worst_case: bool = False,
+        ngen: int = 400,
+    ) -> OptimizeResponse:
+        """Perform EMS (Energy Management System) optimization and visualize results."""
+        if start_hour is None:
+            start_hour = self.ems.start_datetime.hour
+
+        einspeiseverguetung_euro_pro_wh = np.full(
+            self.config.prediction.hours, parameters.ems.einspeiseverguetung_euro_pro_wh
+        )
+
+        # TODO: Refactor device setup phase out
+        self.devices.reset()
+
+        # Initialize PV and EV batteries
+        akku: Optional[Battery] = None
+        if parameters.pv_akku:
+            akku = Battery(parameters.pv_akku)
+            self.devices.add_device(akku)
+            akku.set_charge_per_hour(np.full(self.config.prediction.hours, 1))
+
+        eauto: Optional[Battery] = None
+        if parameters.eauto:
+            eauto = Battery(
+                parameters.eauto,
+            )
+            self.devices.add_device(eauto)
+            eauto.set_charge_per_hour(np.full(self.config.prediction.hours, 1))
+            self.optimize_ev = (
+                parameters.eauto.min_soc_percentage - parameters.eauto.initial_soc_percentage >= 0
+            )
+        else:
+            self.optimize_ev = False
+
+        # Initialize household appliance if applicable
+        dishwasher = (
+            HomeAppliance(
+                parameters=parameters.dishwasher,
+            )
+            if parameters.dishwasher is not None
+            else None
+        )
+        self.devices.add_device(dishwasher)
+
+        # Initialize the inverter and energy management system
+        inverter: Optional[Inverter] = None
+        if parameters.inverter:
+            inverter = Inverter(
+                parameters.inverter,
+            )
+            self.devices.add_device(inverter)
+
+        self.devices.post_setup()
+
+        self.ems.set_parameters(
+            parameters.ems,
+            inverter=inverter,
+            ev=eauto,
+            home_appliance=dishwasher,
+        )
+        self.ems.set_start_hour(start_hour)
+
+        # Setup the DEAP environment and optimization process
+        self.setup_deap_environment({"home_appliance": 1 if dishwasher else 0}, start_hour)
+        self.toolbox.register(
+            "evaluate",
+            lambda ind: self.evaluate(ind, parameters, start_hour, worst_case),
+        )
+
+        if self.verbose:
+            start_time = time.time()
+        start_solution, extra_data = self.optimize(parameters.start_solution, ngen=ngen)
+
+        if self.verbose:
+            elapsed_time = time.time() - start_time
+            print(f"Time evaluate inner: {elapsed_time:.4f} sec.")
+        # Perform final evaluation on the best solution
+        o = self.evaluate_inner(start_solution)
+        discharge_hours_bin, eautocharge_hours_index, washingstart_int = self.split_individual(
+            start_solution
+        )
+        eautocharge_hours_float = (
+            [self.possible_charge_values[i] for i in eautocharge_hours_index]
+            if eautocharge_hours_index is not None
+            else None
+        )
+
+        ac_charge, dc_charge, discharge = self.decode_charge_discharge(discharge_hours_bin)
+        # Visualize the results
+        visualize = {
+            "ac_charge": ac_charge.tolist(),
+            "dc_charge": dc_charge.tolist(),
+            "discharge_allowed": discharge.tolist(),
+            "eautocharge_hours_float": eautocharge_hours_float,
+            "result": o,
+            "eauto_obj": self.ems.ev.to_dict(),
+            "start_solution": start_solution,
+            "spuelstart": washingstart_int,
+            "extra_data": extra_data,
+            "fitness_history": self.fitness_history,
+            "fixed_seed": self.fix_seed,
+        }
+        from akkudoktoreos.utils.visualize import prepare_visualize
+
+        prepare_visualize(parameters, visualize, start_hour=start_hour)
+
+        return OptimizeResponse(
+            **{
+                "ac_charge": ac_charge,
+                "dc_charge": dc_charge,
+                "discharge_allowed": discharge,
+                "eautocharge_hours_float": eautocharge_hours_float,
+                "result": SimulationResult(**o),
+                "eauto_obj": self.ems.ev,
+                "start_solution": start_solution,
+                "washingstart": washingstart_int,
+            }
+        )

src/akkudoktoreos/optimization/genetic/geneticabc.py

@@ -1,11 +0,0 @@
-"""Genetic optimization algorithm abstract and base classes."""
-
-from pydantic import ConfigDict
-
-from akkudoktoreos.core.pydantic import PydanticBaseModel
-
-
-class GeneticParametersBaseModel(PydanticBaseModel):
-    """Pydantic base model for parameters for the GENETIC algorithm."""
-
-    model_config = ConfigDict(extra="forbid")

src/akkudoktoreos/optimization/genetic/geneticdevices.py

@@ -1,132 +0,0 @@
-"""Genetic optimization algorithm device interfaces/ parameters."""
-
-from typing import Optional
-
-from pydantic import Field
-
-from akkudoktoreos.optimization.genetic.geneticabc import GeneticParametersBaseModel
-from akkudoktoreos.utils.datetimeutil import TimeWindowSequence
-
-
-class DeviceParameters(GeneticParametersBaseModel):
-    device_id: str = Field(description="ID of device", examples="device1")
-    hours: Optional[int] = Field(
-        default=None,
-        gt=0,
-        description="Number of prediction hours. Defaults to global config prediction hours.",
-        examples=[None],
-    )
-
-
-def max_charging_power_field(description: Optional[str] = None) -> float:
-    if description is None:
-        description = "Maximum charging power in watts."
-    return Field(
-        default=5000,
-        gt=0,
-        description=description,
-    )
-
-
-def initial_soc_percentage_field(description: str) -> int:
-    return Field(default=0, ge=0, le=100, description=description, examples=[42])
-
-
-def discharging_efficiency_field(default_value: float) -> float:
-    return Field(
-        default=default_value,
-        gt=0,
-        le=1,
-        description="A float representing the discharge efficiency of the battery.",
-    )
-
-
-class BaseBatteryParameters(DeviceParameters):
-    """Battery Device Simulation Configuration."""
-
-    device_id: str = Field(description="ID of battery", examples=["battery1"])
-    capacity_wh: int = Field(
-        gt=0,
-        description="An integer representing the capacity of the battery in watt-hours.",
-        examples=[8000],
-    )
-    charging_efficiency: float = Field(
-        default=0.88,
-        gt=0,
-        le=1,
-        description="A float representing the charging efficiency of the battery.",
-    )
-    discharging_efficiency: float = discharging_efficiency_field(0.88)
-    max_charge_power_w: Optional[float] = max_charging_power_field()
-    initial_soc_percentage: int = initial_soc_percentage_field(
-        "An integer representing the state of charge of the battery at the **start** of the current hour (not the current state)."
-    )
-    min_soc_percentage: int = Field(
-        default=0,
-        ge=0,
-        le=100,
-        description="An integer representing the minimum state of charge (SOC) of the battery in percentage.",
-        examples=[10],
-    )
-    max_soc_percentage: int = Field(
-        default=100,
-        ge=0,
-        le=100,
-        description="An integer representing the maximum state of charge (SOC) of the battery in percentage.",
-    )
-    charge_rates: Optional[list[float]] = Field(
-        default=None,
-        description="Charge rates as factor of maximum charging power [0.00 ... 1.00]. None denotes all charge rates are available.",
-        examples=[[0.0, 0.25, 0.5, 0.75, 1.0], None],
-    )
-
-
-class SolarPanelBatteryParameters(BaseBatteryParameters):
-    """PV battery device simulation configuration."""
-
-    max_charge_power_w: Optional[float] = max_charging_power_field()
-
-
-class ElectricVehicleParameters(BaseBatteryParameters):
-    """Battery Electric Vehicle Device Simulation Configuration."""
-
-    device_id: str = Field(description="ID of electric vehicle", examples=["ev1"])
-    discharging_efficiency: float = discharging_efficiency_field(1.0)
-    initial_soc_percentage: int = initial_soc_percentage_field(
-        "An integer representing the current state of charge (SOC) of the battery in percentage."
-    )
-
-
-class HomeApplianceParameters(DeviceParameters):
-    """Home Appliance Device Simulation Configuration."""
-
-    device_id: str = Field(description="ID of home appliance", examples=["dishwasher"])
-    consumption_wh: int = Field(
-        gt=0,
-        description="An integer representing the energy consumption of a household device in watt-hours.",
-        examples=[2000],
-    )
-    duration_h: int = Field(
-        gt=0,
-        description="An integer representing the usage duration of a household device in hours.",
-        examples=[3],
-    )
-    time_windows: Optional[TimeWindowSequence] = Field(
-        default=None,
-        description="List of allowed time windows. Defaults to optimization general time window.",
-        examples=[
-            [
-                {"start_time": "10:00", "duration": "2 hours"},
-            ],
-        ],
-    )
-
-
-class InverterParameters(DeviceParameters):
-    """Inverter Device Simulation Configuration."""
-
-    device_id: str = Field(description="ID of inverter", examples=["inverter1"])
-    max_power_wh: float = Field(gt=0, examples=[10000])
-    battery_id: Optional[str] = Field(
-        default=None, description="ID of battery", examples=[None, "battery1"]
-    )

src/akkudoktoreos/optimization/genetic/geneticparams.py

@@ -1,638 +0,0 @@
-"""GENETIC algorithm paramters.
-
-This module defines the Pydantic-based configuration and input parameter models
-used in the energy optimization routines, including photovoltaic forecasts,
-electricity pricing, and system component parameters.
-
-It also provides a method to assemble these parameters from predictions,
-forecasts, and fallback defaults, preparing them for optimization runs.
-"""
-
-from typing import Optional, Union
-
-from loguru import logger
-from pydantic import Field, field_validator, model_validator
-from typing_extensions import Self
-
-from akkudoktoreos.core.coreabc import (
-    ConfigMixin,
-    MeasurementMixin,
-    PredictionMixin,
-)
-from akkudoktoreos.optimization.genetic.geneticabc import GeneticParametersBaseModel
-from akkudoktoreos.optimization.genetic.geneticdevices import (
-    ElectricVehicleParameters,
-    HomeApplianceParameters,
-    InverterParameters,
-    SolarPanelBatteryParameters,
-)
-from akkudoktoreos.utils.datetimeutil import to_duration
-
-# Do not import directly from akkudoktoreos.core.coreabc
-# EnergyManagementSystemMixin - Creates circular dependency with ems.py
-# StartMixin                  - Creates circular dependency with ems.py
-
-
-class GeneticEnergyManagementParameters(GeneticParametersBaseModel):
-    """Encapsulates energy-related forecasts and costs used in GENETIC optimization."""
-
-    pv_prognose_wh: list[float] = Field(
-        description="An array of floats representing the forecasted photovoltaic output in watts for different time intervals."
-    )
-    strompreis_euro_pro_wh: list[float] = Field(
-        description="An array of floats representing the electricity price in euros per watt-hour for different time intervals."
-    )
-    einspeiseverguetung_euro_pro_wh: Union[list[float], float] = Field(
-        description="A float or array of floats representing the feed-in compensation in euros per watt-hour."
-    )
-    preis_euro_pro_wh_akku: float = Field(
-        description="A float representing the cost of battery energy per watt-hour."
-    )
-    gesamtlast: list[float] = Field(
-        description="An array of floats representing the total load (consumption) in watts for different time intervals."
-    )
-
-    @model_validator(mode="after")
-    def validate_list_length(self) -> Self:
-        """Validate that all input lists are of the same length.
-
-        Raises:
-            ValueError: If input list lengths differ.
-        """
-        pv_prognose_length = len(self.pv_prognose_wh)
-        if (
-            pv_prognose_length != len(self.strompreis_euro_pro_wh)
-            or pv_prognose_length != len(self.gesamtlast)
-            or (
-                isinstance(self.einspeiseverguetung_euro_pro_wh, list)
-                and pv_prognose_length != len(self.einspeiseverguetung_euro_pro_wh)
-            )
-        ):
-            raise ValueError("Input lists have different lengths")
-        return self
-
-
-class GeneticOptimizationParameters(
-    ConfigMixin,
-    MeasurementMixin,
-    PredictionMixin,
-    # EnergyManagementSystemMixin, # Creates circular dependency with ems.py
-    # StartMixin,                  # Creates circular dependency with ems.py
-    GeneticParametersBaseModel,
-):
-    """Main parameter class for running the genetic energy optimization.
-
-    Collects all model and configuration parameters necessary to run the
-    optimization process, such as forecasts, pricing, battery and appliance models.
-    """
-
-    ems: GeneticEnergyManagementParameters
-    pv_akku: Optional[SolarPanelBatteryParameters]
-    inverter: Optional[InverterParameters]
-    eauto: Optional[ElectricVehicleParameters]
-    dishwasher: Optional[HomeApplianceParameters] = None
-    temperature_forecast: Optional[list[Optional[float]]] = Field(
-        default=None,
-        description="An array of floats representing the temperature forecast in degrees Celsius for different time intervals.",
-    )
-    start_solution: Optional[list[float]] = Field(
-        default=None, description="Can be `null` or contain a previous solution (if available)."
-    )
-
-    @model_validator(mode="after")
-    def validate_list_length(self) -> Self:
-        """Ensure that temperature forecast list matches the PV forecast length.
-
-        Raises:
-            ValueError: If list lengths mismatch.
-        """
-        arr_length = len(self.ems.pv_prognose_wh)
-        if self.temperature_forecast is not None and arr_length != len(self.temperature_forecast):
-            raise ValueError("Input lists have different lengths")
-        return self
-
-    @field_validator("start_solution")
-    def validate_start_solution(
-        cls, start_solution: Optional[list[float]]
-    ) -> Optional[list[float]]:
-        """Validate that the starting solution has at least two elements.
-
-        Args:
-            start_solution (list[float]): Optional list of solution values.
-
-        Returns:
-            list[float]: Validated list.
-
-        Raises:
-            ValueError: If the solution is too short.
-        """
-        if start_solution is not None and len(start_solution) < 2:
-            raise ValueError("Requires at least two values.")
-        return start_solution
-
-    @classmethod
-    def prepare(cls) -> "Optional[GeneticOptimizationParameters]":
-        """Prepare optimization parameters from config, forecast and measurement data.
-
-        Fills in values needed for optimization from available configuration, predictions and
-        measurements. If some data is missing, default or demo values are used.
-
-        Parameters start by definition of the genetic algorithm at hour 0 of the actual date
-        (not at start datetime of energy management run)
-
-        Returns:
-            GeneticOptimizationParameters: The fully prepared optimization parameters.
-
-        Raises:
-            ValueError: If required configuration values like start time are missing.
-        """
-        # Avoid circular dependency
-        from akkudoktoreos.core.ems import get_ems
-
-        ems = get_ems()
-
-        # The optimization paramters
-        oparams: "Optional[GeneticOptimizationParameters]" = None
-
-        # Check for run definitions
-        if ems.start_datetime is None:
-            error_msg = "Start datetime unknown."
-            logger.error(error_msg)
-            raise ValueError(error_msg)
-        # Check for general predictions conditions
-        if cls.config.general.latitude is None:
-            default_latitude = 52.52
-            logger.error(f"Latitude unknown - defaulting to {default_latitude}.")
-            cls.config.general.latitude = default_latitude
-        if cls.config.general.longitude is None:
-            default_longitude = 13.405
-            logger.error(f"Longitude unknown - defaulting to {default_longitude}.")
-            cls.config.general.longitude = default_longitude
-        if cls.config.prediction.hours is None:
-            logger.error("Prediction hours unknown - defaulting to 48 hours.")
-            cls.config.prediction.hours = 48
-        if cls.config.prediction.historic_hours is None:
-            logger.error("Prediction historic hours unknown - defaulting to 24 hours.")
-            cls.config.prediction.historic_hours = 24
-        # Check optimization definitions
-        if cls.config.optimization.horizon_hours is None:
-            logger.error("Optimization horizon unknown - defaulting to 24 hours.")
-            cls.config.optimization.horizon_hours = 24
-        if cls.config.optimization.interval is None:
-            logger.error("Optimization interval unknown - defaulting to 3600 seconds.")
-            cls.config.optimization.interval = 3600
-        if cls.config.optimization.interval != 3600:
-            logger.error(
-                "Optimization interval '{}' seconds not supported - forced to 3600 seconds."
-            )
-            cls.config.optimization.interval = 3600
-        # Check genetic algorithm definitions
-        if cls.config.optimization.genetic is None:
-            logger.error(
-                "Genetic optimization configuration not configured - defaulting to demo config."
-            )
-            cls.config.optimization.genetic = {
-                "individuals": 300,
-                "generations": 400,
-                "seed": None,
-                "penalties": {
-                    "ev_soc_miss": 10,
-                },
-            }
-        if cls.config.optimization.genetic.individuals is None:
-            logger.error("Genetic individuals unknown - defaulting to 300.")
-            cls.config.optimization.genetic.individuals = 300
-        if cls.config.optimization.genetic.generations is None:
-            logger.error("Genetic generations unknown - defaulting to 400.")
-            cls.config.optimization.genetic.generations = 400
-        if cls.config.optimization.genetic.penalties is None:
-            logger.error("Genetic penalties unknown - defaulting to demo config.")
-            cls.config.optimization.genetic.penalties = {"ev_soc_miss": 10}
-        if "ev_soc_miss" not in cls.config.optimization.genetic.penalties:
-            logger.error("ev_soc_miss penalty function parameter unknown - defaulting to 100.")
-            cls.config.optimization.genetic.penalties["ev_soc_miss"] = 10
-
-        # Get start solution from last run
-        start_solution = None
-        last_solution = ems.genetic_solution()
-        if last_solution and last_solution.start_solution:
-            start_solution = last_solution.start_solution
-
-        # Add forecast and device data
-        interval = to_duration(cls.config.optimization.interval)
-        power_to_energy_per_interval_factor = cls.config.optimization.interval / 3600
-        parameter_start_datetime = ems.start_datetime.set(hour=0, second=0, microsecond=0)
-        parameter_end_datetime = parameter_start_datetime.add(hours=cls.config.prediction.hours)
-        max_retries = 10
-
-        for attempt in range(1, max_retries + 1):
-            # Collect all the data for optimisation, but do not exceed max retries
-            if attempt > max_retries:
-                error_msg = f"Maximum retries {max_retries} for parameter collection exceeded. Parameter preparation attempt {attempt}."
-                logger.error(error_msg)
-                raise ValueError(error_msg)
-
-            # Assure predictions are uptodate
-            cls.prediction.update_data()
-
-            try:
-                pvforecast_ac_power = (
-                    cls.prediction.key_to_array(
-                        key="pvforecast_ac_power",
-                        start_datetime=parameter_start_datetime,
-                        end_datetime=parameter_end_datetime,
-                        interval=interval,
-                        fill_method="linear",
-                    )
-                    * power_to_energy_per_interval_factor
-                ).tolist()
-            except:
-                logger.exception(
-                    "No PV forecast data available - defaulting to demo data. Parameter preparation attempt {}.",
-                    attempt,
-                )
-                cls.config.merge_settings_from_dict(
-                    {
-                        "pvforecast": {
-                            "provider": "PVForecastAkkudoktor",
-                            "planes": [
-                                {
-                                    "peakpower": 5.0,
-                                    "surface_azimuth": 170,
-                                    "surface_tilt": 7,
-                                    "userhorizon": [20, 27, 22, 20],
-                                    "inverter_paco": 10000,
-                                },
-                                {
-                                    "peakpower": 4.8,
-                                    "surface_azimuth": 90,
-                                    "surface_tilt": 7,
-                                    "userhorizon": [30, 30, 30, 50],
-                                    "inverter_paco": 10000,
-                                },
-                                {
-                                    "peakpower": 1.4,
-                                    "surface_azimuth": 140,
-                                    "surface_tilt": 60,
-                                    "userhorizon": [60, 30, 0, 30],
-                                    "inverter_paco": 2000,
-                                },
-                                {
-                                    "peakpower": 1.6,
-                                    "surface_azimuth": 185,
-                                    "surface_tilt": 45,
-                                    "userhorizon": [45, 25, 30, 60],
-                                    "inverter_paco": 1400,
-                                },
-                            ],
-                        },
-                    }
-                )
-                # Retry
-                continue
-            try:
-                elecprice_marketprice_wh = cls.prediction.key_to_array(
-                    key="elecprice_marketprice_wh",
-                    start_datetime=parameter_start_datetime,
-                    end_datetime=parameter_end_datetime,
-                    interval=interval,
-                    fill_method="ffill",
-                ).tolist()
-            except:
-                logger.exception(
-                    "No Electricity Marketprice forecast data available - defaulting to demo data. Parameter preparation attempt {}.",
-                    attempt,
-                )
-                cls.config.elecprice.provider = "ElecPriceAkkudoktor"
-                # Retry
-                continue
-            try:
-                loadforecast_power_w = cls.prediction.key_to_array(
-                    key="loadforecast_power_w",
-                    start_datetime=parameter_start_datetime,
-                    end_datetime=parameter_end_datetime,
-                    interval=interval,
-                    fill_method="ffill",
-                ).tolist()
-            except:
-                logger.exception(
-                    "No Load forecast data available - defaulting to demo data. Parameter preparation attempt {}.",
-                    attempt,
-                )
-                cls.config.merge_settings_from_dict(
-                    {
-                        "load": {
-                            "provider": "LoadAkkudoktor",
-                            "provider_settings": {
-                                "LoadAkkudoktor": {
-                                    "loadakkudoktor_year_energy_kwh": "3000",
-                                },
-                            },
-                        },
-                    }
-                )
-                # Retry
-                continue
-            try:
-                feed_in_tariff_wh = cls.prediction.key_to_array(
-                    key="feed_in_tariff_wh",
-                    start_datetime=parameter_start_datetime,
-                    end_datetime=parameter_end_datetime,
-                    interval=interval,
-                    fill_method="ffill",
-                ).tolist()
-            except:
-                logger.exception(
-                    "No feed in tariff forecast data available - defaulting to demo data. Parameter preparation attempt {}.",
-                    attempt,
-                )
-                cls.config.merge_settings_from_dict(
-                    {
-                        "feedintariff": {
-                            "provider": "FeedInTariffFixed",
-                            "provider_settings": {
-                                "FeedInTariffFixed": {
-                                    "feed_in_tariff_kwh": 0.078,
-                                },
-                            },
-                        },
-                    }
-                )
-                # Retry
-                continue
-            try:
-                weather_temp_air = cls.prediction.key_to_array(
-                    key="weather_temp_air",
-                    start_datetime=parameter_start_datetime,
-                    end_datetime=parameter_end_datetime,
-                    interval=interval,
-                    fill_method="ffill",
-                ).tolist()
-            except:
-                logger.exception(
-                    "No weather forecast data available - defaulting to demo data. Parameter preparation attempt {}.",
-                    attempt,
-                )
-                cls.config.weather.provider = "BrightSky"
-                # Retry
-                continue
-
-            # Add device data
-
-            # Batteries
-            # ---------
-            if cls.config.devices.max_batteries is None:
-                logger.error("Number of battery devices not configured - defaulting to 1.")
-                cls.config.devices.max_batteries = 1
-            if cls.config.devices.max_batteries == 0:
-                battery_params = None
-                battery_lcos_kwh = 0
-            else:
-                if cls.config.devices.batteries is None:
-                    logger.error("No battery device data available - defaulting to demo data.")
-                    cls.config.devices.batteries = [{"device_id": "battery1", "capacity_wh": 8000}]
-                try:
-                    battery_config = cls.config.devices.batteries[0]
-                    battery_params = SolarPanelBatteryParameters(
-                        device_id=battery_config.device_id,
-                        capacity_wh=battery_config.capacity_wh,
-                        charging_efficiency=battery_config.charging_efficiency,
-                        discharging_efficiency=battery_config.discharging_efficiency,
-                        max_charge_power_w=battery_config.max_charge_power_w,
-                        min_soc_percentage=battery_config.min_soc_percentage,
-                        max_soc_percentage=battery_config.max_soc_percentage,
-                    )
-                except:
-                    logger.exception(
-                        "No battery device data available - defaulting to demo data. Parameter preparation attempt {}.",
-                        attempt,
-                    )
-                    cls.config.devices.batteries = [{"device_id": "battery1", "capacity_wh": 8000}]
-                    # Retry
-                    continue
-                # Levelized cost of ownership
-                if battery_config.levelized_cost_of_storage_kwh is None:
-                    logger.error(
-                        "No battery device LCOS data available - defaulting to 0 €/kWh. Parameter preparation attempt {}.",
-                        attempt,
-                    )
-                    battery_config.levelized_cost_of_storage_kwh = 0
-                battery_lcos_kwh = battery_config.levelized_cost_of_storage_kwh
-                # Initial SOC
-                try:
-                    initial_soc_factor = cls.measurement.key_to_value(
-                        key=battery_config.measurement_key_soc_factor,
-                        target_datetime=ems.start_datetime,
-                    )
-                    if initial_soc_factor > 1.0 or initial_soc_factor < 0.0:
-                        logger.error(
-                            f"Invalid battery initial SoC factor {initial_soc_factor} - defaulting to 0.0."
-                        )
-                        initial_soc_factor = 0.0
-                    # genetic parameter is 0..100 as int
-                    initial_soc_percentage = int(initial_soc_factor * 100)
-                except:
-                    initial_soc_percentage = None
-                if initial_soc_percentage is None:
-                    logger.error(
-                        f"No battery device SoC data (measurement key = '{battery_config.measurement_key_soc_factor}') available - defaulting to 0."
-                    )
-                    initial_soc_percentage = 0
-                battery_params.initial_soc_percentage = initial_soc_percentage
-
-            # Electric Vehicles
-            # -----------------
-            if cls.config.devices.max_electric_vehicles is None:
-                logger.error("Number of electric_vehicle devices not configured - defaulting to 1.")
-                cls.config.devices.max_electric_vehicles = 1
-            if cls.config.devices.max_electric_vehicles == 0:
-                electric_vehicle_params = None
-            else:
-                if cls.config.devices.electric_vehicles is None:
-                    logger.error(
-                        "No electric vehicle device data available - defaulting to demo data."
-                    )
-                    cls.config.devices.max_electric_vehicles = 1
-                    cls.config.devices.electric_vehicles = [
-                        {
-                            "device_id": "ev11",
-                            "capacity_wh": 50000,
-                            "charge_rates": [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0],
-                            "min_soc_percentage": 70,
-                        }
-                    ]
-                try:
-                    electric_vehicle_config = cls.config.devices.electric_vehicles[0]
-                    electric_vehicle_params = ElectricVehicleParameters(
-                        device_id=electric_vehicle_config.device_id,
-                        capacity_wh=electric_vehicle_config.capacity_wh,
-                        charging_efficiency=electric_vehicle_config.charging_efficiency,
-                        discharging_efficiency=electric_vehicle_config.discharging_efficiency,
-                        charge_rates=electric_vehicle_config.charge_rates,
-                        max_charge_power_w=electric_vehicle_config.max_charge_power_w,
-                        min_soc_percentage=electric_vehicle_config.min_soc_percentage,
-                        max_soc_percentage=electric_vehicle_config.max_soc_percentage,
-                    )
-                except:
-                    logger.exception(
-                        "No electric_vehicle device data available - defaulting to demo data. Parameter preparation attempt {}.",
-                        attempt,
-                    )
-                    cls.config.devices.max_electric_vehicles = 1
-                    cls.config.devices.electric_vehicles = [
-                        {
-                            "device_id": "ev12",
-                            "capacity_wh": 50000,
-                            "charge_rates": [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0],
-                            "min_soc_percentage": 70,
-                        }
-                    ]
-                    # Retry
-                    continue
-                # Initial SOC
-                try:
-                    initial_soc_factor = cls.measurement.key_to_value(
-                        key=electric_vehicle_config.measurement_key_soc_factor,
-                        target_datetime=ems.start_datetime,
-                    )
-                    if initial_soc_factor > 1.0 or initial_soc_factor < 0.0:
-                        logger.error(
-                            f"Invalid electric vehicle initial SoC factor {initial_soc_factor} - defaulting to 0.0."
-                        )
-                        initial_soc_factor = 0.0
-                    # genetic parameter is 0..100 as int
-                    initial_soc_percentage = int(initial_soc_factor * 100)
-                except:
-                    initial_soc_percentage = None
-                if initial_soc_percentage is None:
-                    logger.error(
-                        f"No electric vehicle device SoC data (measurement key = '{electric_vehicle_config.measurement_key_soc_factor}') available - defaulting to 0."
-                    )
-                    initial_soc_percentage = 0
-                electric_vehicle_params.initial_soc_percentage = initial_soc_percentage
-
-            # Inverters
-            # ---------
-            if cls.config.devices.max_inverters is None:
-                logger.error("Number of inverter devices not configured - defaulting to 1.")
-                cls.config.devices.max_inverters = 1
-            if cls.config.devices.max_inverters == 0:
-                inverter_params = None
-            else:
-                if cls.config.devices.inverters is None:
-                    logger.error("No inverter device data available - defaulting to demo data.")
-                    cls.config.devices.inverters = [
-                        {
-                            "device_id": "inverter1",
-                            "max_power_w": 10000,
-                            "battery_id": battery_config.device_id,
-                        }
-                    ]
-                try:
-                    inverter_config = cls.config.devices.inverters[0]
-                    inverter_params = InverterParameters(
-                        device_id=inverter_config.device_id,
-                        max_power_wh=inverter_config.max_power_w,
-                        battery_id=inverter_config.battery_id,
-                    )
-                except:
-                    logger.exception(
-                        "No inverter device data available - defaulting to demo data. Parameter preparation attempt {}.",
-                        attempt,
-                    )
-                    cls.config.devices.inverters = [
-                        {
-                            "device_id": "inverter1",
-                            "max_power_w": 10000,
-                            "battery_id": battery_config.device_id,
-                        }
-                    ]
-                    # Retry
-                    continue
-
-            # Home Appliances
-            # ---------------
-            if cls.config.devices.max_home_appliances is None:
-                logger.error("Number of home appliance devices not configured - defaulting to 1.")
-                cls.config.devices.max_home_appliances = 1
-            if cls.config.devices.max_home_appliances == 0:
-                home_appliance_params = None
-            else:
-                home_appliance_params = None
-                if cls.config.devices.home_appliances is None:
-                    logger.error(
-                        "No home appliance device data available - defaulting to demo data."
-                    )
-                    cls.config.devices.home_appliances = [
-                        {
-                            "device_id": "dishwasher1",
-                            "consumption_wh": 2000,
-                            "duration_h": 3.0,
-                            "time_windows": {
-                                "windows": [
-                                    {
-                                        "start_time": "08:00",
-                                        "duration": "5 hours",
-                                    },
-                                    {
-                                        "start_time": "15:00",
-                                        "duration": "3 hours",
-                                    },
-                                ],
-                            },
-                        }
-                    ]
-                try:
-                    home_appliance_config = cls.config.devices.home_appliances[0]
-                    home_appliance_params = HomeApplianceParameters(
-                        device_id=home_appliance_config.device_id,
-                        consumption_wh=home_appliance_config.consumption_wh,
-                        duration_h=home_appliance_config.duration_h,
-                        time_windows=home_appliance_config.time_windows,
-                    )
-                except:
-                    logger.exception(
-                        "No home appliance device data available - defaulting to demo data. Parameter preparation attempt {}.",
-                        attempt,
-                    )
-                    cls.config.devices.home_appliances = [
-                        {
-                            "device_id": "dishwasher1",
-                            "consumption_wh": 2000,
-                            "duration_h": 3.0,
-                            "time_windows": None,
-                        }
-                    ]
-                    # Retry
-                    continue
-
-            # We got all parameter data
-            try:
-                oparams = GeneticOptimizationParameters(
-                    ems=GeneticEnergyManagementParameters(
-                        pv_prognose_wh=pvforecast_ac_power,
-                        strompreis_euro_pro_wh=elecprice_marketprice_wh,
-                        einspeiseverguetung_euro_pro_wh=feed_in_tariff_wh,
-                        gesamtlast=loadforecast_power_w,
-                        preis_euro_pro_wh_akku=battery_lcos_kwh / 1000,
-                    ),
-                    temperature_forecast=weather_temp_air,
-                    pv_akku=battery_params,
-                    eauto=electric_vehicle_params,
-                    inverter=inverter_params,
-                    dishwasher=home_appliance_params,
-                    start_solution=start_solution,
-                )
-            except:
-                logger.exception(
-                    "Can not prepare optimization parameters - will retry. Parameter preparation attempt {}.",
-                    attempt,
-                )
-                oparams = None
-                # Retry
-                continue
-
-            # Parameters prepared
-            break
-
-        return oparams

src/akkudoktoreos/optimization/genetic/geneticsolution.py

@@ -1,607 +0,0 @@
-"""Genetic algorithm optimisation solution."""
-
-from typing import Any, Optional
-
-import pandas as pd
-from loguru import logger
-from pydantic import Field, field_validator
-
-from akkudoktoreos.core.coreabc import (
-    ConfigMixin,
-)
-from akkudoktoreos.core.emplan import (
-    DDBCInstruction,
-    EnergyManagementPlan,
-    FRBCInstruction,
-)
-from akkudoktoreos.core.pydantic import PydanticDateTimeDataFrame
-from akkudoktoreos.devices.devicesabc import (
-    ApplianceOperationMode,
-    BatteryOperationMode,
-)
-from akkudoktoreos.devices.genetic.battery import Battery
-from akkudoktoreos.optimization.genetic.geneticdevices import GeneticParametersBaseModel
-from akkudoktoreos.optimization.optimization import OptimizationSolution
-from akkudoktoreos.prediction.prediction import get_prediction
-from akkudoktoreos.utils.datetimeutil import to_datetime, to_duration
-from akkudoktoreos.utils.utils import NumpyEncoder
-
-
-class DeviceOptimizeResult(GeneticParametersBaseModel):
-    device_id: str = Field(description="ID of device", examples=["device1"])
-    hours: int = Field(gt=0, description="Number of hours in the simulation.", examples=[24])
-
-
-class ElectricVehicleResult(DeviceOptimizeResult):
-    """Result class containing information related to the electric vehicle's charging and discharging behavior."""
-
-    device_id: str = Field(description="ID of electric vehicle", examples=["ev1"])
-    charge_array: list[float] = Field(
-        description="Hourly charging status (0 for no charging, 1 for charging)."
-    )
-    discharge_array: list[int] = Field(
-        description="Hourly discharging status (0 for no discharging, 1 for discharging)."
-    )
-    discharging_efficiency: float = Field(description="The discharge efficiency as a float..")
-    capacity_wh: int = Field(description="Capacity of the EV’s battery in watt-hours.")
-    charging_efficiency: float = Field(description="Charging efficiency as a float..")
-    max_charge_power_w: int = Field(description="Maximum charging power in watts.")
-    soc_wh: float = Field(
-        description="State of charge of the battery in watt-hours at the start of the simulation."
-    )
-    initial_soc_percentage: int = Field(
-        description="State of charge at the start of the simulation in percentage."
-    )
-
-    @field_validator("discharge_array", "charge_array", mode="before")
-    def convert_numpy(cls, field: Any) -> Any:
-        return NumpyEncoder.convert_numpy(field)[0]
-
-
-class GeneticSimulationResult(GeneticParametersBaseModel):
-    """This object contains the results of the simulation and provides insights into various parameters over the entire forecast period."""
-
-    Last_Wh_pro_Stunde: list[float] = Field(description="TBD")
-    EAuto_SoC_pro_Stunde: list[float] = Field(
-        description="The state of charge of the EV for each hour."
-    )
-    Einnahmen_Euro_pro_Stunde: list[float] = Field(
-        description="The revenue from grid feed-in or other sources in euros per hour."
-    )
-    Gesamt_Verluste: float = Field(
-        description="The total losses in watt-hours over the entire period."
-    )
-    Gesamtbilanz_Euro: float = Field(
-        description="The total balance of revenues minus costs in euros."
-    )
-    Gesamteinnahmen_Euro: float = Field(description="The total revenues in euros.")
-    Gesamtkosten_Euro: float = Field(description="The total costs in euros.")
-    Home_appliance_wh_per_hour: list[Optional[float]] = Field(
-        description="The energy consumption of a household appliance in watt-hours per hour."
-    )
-    Kosten_Euro_pro_Stunde: list[float] = Field(description="The costs in euros per hour.")
-    Netzbezug_Wh_pro_Stunde: list[float] = Field(
-        description="The grid energy drawn in watt-hours per hour."
-    )
-    Netzeinspeisung_Wh_pro_Stunde: list[float] = Field(
-        description="The energy fed into the grid in watt-hours per hour."
-    )
-    Verluste_Pro_Stunde: list[float] = Field(description="The losses in watt-hours per hour.")
-    akku_soc_pro_stunde: list[float] = Field(
-        description="The state of charge of the battery (not the EV) in percentage per hour."
-    )
-    Electricity_price: list[float] = Field(
-        description="Used Electricity Price, including predictions"
-    )
-
-    @field_validator(
-        "Last_Wh_pro_Stunde",
-        "Netzeinspeisung_Wh_pro_Stunde",
-        "akku_soc_pro_stunde",
-        "Netzbezug_Wh_pro_Stunde",
-        "Kosten_Euro_pro_Stunde",
-        "Einnahmen_Euro_pro_Stunde",
-        "EAuto_SoC_pro_Stunde",
-        "Verluste_Pro_Stunde",
-        "Home_appliance_wh_per_hour",
-        "Electricity_price",
-        mode="before",
-    )
-    def convert_numpy(cls, field: Any) -> Any:
-        return NumpyEncoder.convert_numpy(field)[0]
-
-
-class GeneticSolution(ConfigMixin, GeneticParametersBaseModel):
-    """**Note**: The first value of "Last_Wh_per_hour", "Netzeinspeisung_Wh_per_hour", and "Netzbezug_Wh_per_hour", will be set to null in the JSON output and represented as NaN or None in the corresponding classes' data returns. This approach is adopted to ensure that the current hour's processing remains unchanged."""
-
-    ac_charge: list[float] = Field(
-        description="Array with AC charging values as relative power (0.0-1.0), other values set to 0."
-    )
-    dc_charge: list[float] = Field(
-        description="Array with DC charging values as relative power (0-1), other values set to 0."
-    )
-    discharge_allowed: list[int] = Field(
-        description="Array with discharge values (1 for discharge, 0 otherwise)."
-    )
-    eautocharge_hours_float: Optional[list[float]] = Field(description="TBD")
-    result: GeneticSimulationResult
-    eauto_obj: Optional[ElectricVehicleResult]
-    start_solution: Optional[list[float]] = Field(
-        default=None,
-        description="An array of binary values (0 or 1) representing a possible starting solution for the simulation.",
-    )
-    washingstart: Optional[int] = Field(
-        default=None,
-        description="Can be `null` or contain an object representing the start of washing (if applicable).",
-    )
-
-    @field_validator(
-        "ac_charge",
-        "dc_charge",
-        "discharge_allowed",
-        mode="before",
-    )
-    def convert_numpy(cls, field: Any) -> Any:
-        return NumpyEncoder.convert_numpy(field)[0]
-
-    @field_validator(
-        "eauto_obj",
-        mode="before",
-    )
-    def convert_eauto(cls, field: Any) -> Any:
-        if isinstance(field, Battery):
-            return ElectricVehicleResult(**field.to_dict())
-        return field
-
-    def _battery_operation_from_solution(
-        self,
-        ac_charge: float,
-        dc_charge: float,
-        discharge_allowed: bool,
-    ) -> tuple[BatteryOperationMode, float]:
-        """Maps low-level solution to a representative operation mode and factor.
-
-        Args:
-            ac_charge (float): Allowed AC-side charging power (relative units).
-            dc_charge (float): Allowed DC-side charging power (relative units).
-            discharge_allowed (bool): Whether discharging is permitted.
-
-        Returns:
-            tuple[BatteryOperationMode, float]:
-                A tuple containing:
-                - `BatteryOperationMode`: the representative high-level operation mode.
-                - `float`: the operation factor corresponding to the active signal.
-
-        Notes:
-            - The mapping prioritizes AC charge > DC charge > discharge.
-            - Multiple strategies can produce the same low-level signals; this function
-            returns a representative mode based on a defined priority order.
-        """
-        # (0,0,0) → Nothing allowed
-        if ac_charge <= 0.0 and dc_charge <= 0.0 and not discharge_allowed:
-            return BatteryOperationMode.IDLE, 1.0
-
-        # (0,0,1) → Discharge only
-        if ac_charge <= 0.0 and dc_charge <= 0.0 and discharge_allowed:
-            return BatteryOperationMode.PEAK_SHAVING, 1.0
-
-        # (ac>0,0,0) → AC charge only
-        if ac_charge > 0.0 and dc_charge <= 0.0 and not discharge_allowed:
-            return BatteryOperationMode.GRID_SUPPORT_IMPORT, ac_charge
-
-        # (0,dc>0,0) → DC charge only
-        if ac_charge <= 0.0 and dc_charge > 0.0 and not discharge_allowed:
-            return BatteryOperationMode.NON_EXPORT, dc_charge
-
-        # (ac>0,dc>0,0) → Both charge paths, no discharge
-        if ac_charge > 0.0 and dc_charge > 0.0 and not discharge_allowed:
-            return BatteryOperationMode.FORCED_CHARGE, ac_charge
-
-        # (ac>0,0,1) → AC charge + discharge - does not make sense
-        if ac_charge > 0.0 and dc_charge <= 0.0 and discharge_allowed:
-            raise ValueError(
-                f"Illegal state: ac_charge: {ac_charge} and discharge_allowed: {discharge_allowed}"
-            )
-
-        # (0,dc>0,1) → DC charge + discharge
-        if ac_charge <= 0.0 and dc_charge > 0.0 and discharge_allowed:
-            return BatteryOperationMode.SELF_CONSUMPTION, dc_charge
-
-        # (ac>0,dc>0,1) → Fully flexible - does not make sense
-        if ac_charge > 0.0 and dc_charge > 0.0 and discharge_allowed:
-            raise ValueError(
-                f"Illegal state: ac_charge: {ac_charge} and discharge_allowed: {discharge_allowed}"
-            )
-
-        # Fallback → safe idle
-        return BatteryOperationMode.IDLE, 1.0
-
-    def optimization_solution(self) -> OptimizationSolution:
-        """Provide the genetic solution as a general optimization solution.
-
-        The battery modes are controlled by the grid control triggers:
-        - ac_charge: charge from grid
-        - discharge_allowed: discharge to grid
-
-        The following battery modes are supported:
-        - SELF_CONSUMPTION:    ac_charge == 0 and discharge_allowed == 0
-        - GRID_SUPPORT_EXPORT: ac_charge == 0 and discharge_allowed == 1
-        - GRID_SUPPORT_IMPORT: ac_charge  > 0 and discharge_allowed == 0 or 1
-        """
-        from akkudoktoreos.core.ems import get_ems
-
-        start_datetime = get_ems().start_datetime
-        start_day_hour = start_datetime.in_timezone(self.config.general.timezone).hour
-        interval_hours = 1
-        power_to_energy_per_interval_factor = 1.0
-
-        # --- Create index based on list length and interval ---
-        # Ensure we only use the minimum of results and commands if differing
-        periods = min(len(self.result.Kosten_Euro_pro_Stunde), len(self.ac_charge) - start_day_hour)
-        time_index = pd.date_range(
-            start=start_datetime,
-            periods=periods,
-            freq=f"{interval_hours}h",
-        )
-        n_points = len(time_index)
-        end_datetime = start_datetime.add(hours=n_points)
-
-        # Fill solution into dataframe with correct column names
-        # - load_energy_wh: Load of all energy consumers in wh"
-        # - grid_energy_wh: Grid energy feed in (negative) or consumption (positive) in wh"
-        # - costs_amt: Costs in money amount"
-        # - revenue_amt: Revenue in money amount"
-        # - losses_energy_wh: Energy losses in wh"
-        # - <device-id>_<operation>_op_mode: Operation mode of the device (1.0 when active)."
-        # - <device-id>_<operation>_op_factor: Operation mode factor of the device."
-        # - <device-id>_soc_factor: State of charge of a battery/ electric vehicle device as factor of total capacity."
-        # - <device-id>_energy_wh: Energy consumption (positive) of a device in wh."
-
-        solution = pd.DataFrame(
-            {
-                "date_time": time_index,
-                # result starts at start_day_hour
-                "load_energy_wh": self.result.Last_Wh_pro_Stunde[:n_points],
-                "grid_feedin_energy_wh": self.result.Netzeinspeisung_Wh_pro_Stunde[:n_points],
-                "grid_consumption_energy_wh": self.result.Netzbezug_Wh_pro_Stunde[:n_points],
-                "costs_amt": self.result.Kosten_Euro_pro_Stunde[:n_points],
-                "revenue_amt": self.result.Einnahmen_Euro_pro_Stunde[:n_points],
-                "losses_energy_wh": self.result.Verluste_Pro_Stunde[:n_points],
-            },
-            index=time_index,
-        )
-
-        # Add battery data
-        solution["battery1_soc_factor"] = [
-            v / 100
-            for v in self.result.akku_soc_pro_stunde[:n_points]  # result starts at start_day_hour
-        ]
-        operation: dict[str, list[float]] = {
-            "genetic_ac_charge_factor": [],
-            "genetic_dc_charge_factor": [],
-            "genetic_discharge_allowed_factor": [],
-        }
-        # ac_charge, dc_charge, discharge_allowed start at hour 0 of start day
-        for hour_idx, rate in enumerate(self.ac_charge):
-            if hour_idx < start_day_hour:
-                continue
-            if hour_idx >= start_day_hour + n_points:
-                break
-            ac_charge_hour = self.ac_charge[hour_idx]
-            dc_charge_hour = self.dc_charge[hour_idx]
-            discharge_allowed_hour = bool(self.discharge_allowed[hour_idx])
-            operation_mode, operation_mode_factor = self._battery_operation_from_solution(
-                ac_charge_hour, dc_charge_hour, discharge_allowed_hour
-            )
-            operation["genetic_ac_charge_factor"].append(ac_charge_hour)
-            operation["genetic_dc_charge_factor"].append(dc_charge_hour)
-            operation["genetic_discharge_allowed_factor"].append(discharge_allowed_hour)
-            for mode in BatteryOperationMode:
-                mode_key = f"battery1_{mode.lower()}_op_mode"
-                factor_key = f"battery1_{mode.lower()}_op_factor"
-                if mode_key not in operation.keys():
-                    operation[mode_key] = []
-                    operation[factor_key] = []
-                if mode == operation_mode:
-                    operation[mode_key].append(1.0)
-                    operation[factor_key].append(operation_mode_factor)
-                else:
-                    operation[mode_key].append(0.0)
-                    operation[factor_key].append(0.0)
-        for key in operation.keys():
-            if len(operation[key]) != n_points:
-                error_msg = f"instruction {key} has invalid length {len(operation[key])} - expected {n_points}"
-                logger.error(error_msg)
-                raise ValueError(error_msg)
-            solution[key] = operation[key]
-
-        # Add EV battery solution
-        # eautocharge_hours_float start at hour 0 of start day
-        # result.EAuto_SoC_pro_Stunde start at start_datetime.hour
-        if self.eauto_obj:
-            if self.eautocharge_hours_float is None:
-                # Electric vehicle is full enough. No load times.
-                solution[f"{self.eauto_obj.device_id}_soc_factor"] = [
-                    self.eauto_obj.initial_soc_percentage / 100.0
-                ] * n_points
-                solution["genetic_ev_charge_factor"] = [0.0] * n_points
-                # operation modes
-                operation_mode = BatteryOperationMode.IDLE
-                for mode in BatteryOperationMode:
-                    mode_key = f"{self.eauto_obj.device_id}_{mode.lower()}_op_mode"
-                    factor_key = f"{self.eauto_obj.device_id}_{mode.lower()}_op_factor"
-                    if mode == operation_mode:
-                        solution[mode_key] = [1.0] * n_points
-                        solution[factor_key] = [1.0] * n_points
-                    else:
-                        solution[mode_key] = [0.0] * n_points
-                        solution[factor_key] = [0.0] * n_points
-            else:
-                solution[f"{self.eauto_obj.device_id}_soc_factor"] = [
-                    v / 100 for v in self.result.EAuto_SoC_pro_Stunde[:n_points]
-                ]
-                operation = {
-                    "genetic_ev_charge_factor": [],
-                }
-                for hour_idx, rate in enumerate(self.eautocharge_hours_float):
-                    if hour_idx < start_day_hour:
-                        continue
-                    if hour_idx >= start_day_hour + n_points:
-                        break
-                    operation["genetic_ev_charge_factor"].append(rate)
-                    operation_mode, operation_mode_factor = self._battery_operation_from_solution(
-                        rate, 0.0, False
-                    )
-                    for mode in BatteryOperationMode:
-                        mode_key = f"{self.eauto_obj.device_id}_{mode.lower()}_op_mode"
-                        factor_key = f"{self.eauto_obj.device_id}_{mode.lower()}_op_factor"
-                        if mode_key not in operation.keys():
-                            operation[mode_key] = []
-                            operation[factor_key] = []
-                        if mode == operation_mode:
-                            operation[mode_key].append(1.0)
-                            operation[factor_key].append(operation_mode_factor)
-                        else:
-                            operation[mode_key].append(0.0)
-                            operation[factor_key].append(0.0)
-                for key in operation.keys():
-                    if len(operation[key]) != n_points:
-                        error_msg = f"instruction {key} has invalid length {len(operation[key])} - expected {n_points}"
-                        logger.error(error_msg)
-                        raise ValueError(error_msg)
-                    solution[key] = operation[key]
-
-        # Add home appliance data
-        if self.washingstart:
-            # result starts at start_day_hour
-            solution["homeappliance1_energy_wh"] = self.result.Home_appliance_wh_per_hour[:n_points]
-
-        # Fill prediction into dataframe with correct column names
-        # - pvforecast_ac_energy_wh_energy_wh: PV energy prediction (positive) in wh
-        # - elec_price_amt_kwh: Electricity price prediction in money per kwh
-        # - weather_temp_air_celcius: Temperature in °C"
-        # - loadforecast_energy_wh: Load energy prediction in wh
-        # - loadakkudoktor_std_energy_wh: Load energy standard deviation prediction in wh
-        # - loadakkudoktor_mean_energy_wh: Load mean energy prediction in wh
-        prediction = pd.DataFrame(
-            {
-                "date_time": time_index,
-            },
-            index=time_index,
-        )
-        pred = get_prediction()
-
-        if "pvforecast_ac_power" in pred.record_keys:
-            prediction["pvforecast_ac_energy_wh"] = (
-                pred.key_to_array(
-                    key="pvforecast_ac_power",
-                    start_datetime=start_datetime,
-                    end_datetime=end_datetime,
-                    interval=to_duration(f"{interval_hours} hours"),
-                    fill_method="linear",
-                )
-                * power_to_energy_per_interval_factor
-            ).tolist()
-        if "pvforecast_dc_power" in pred.record_keys:
-            prediction["pvforecast_dc_energy_wh"] = (
-                pred.key_to_array(
-                    key="pvforecast_dc_power",
-                    start_datetime=start_datetime,
-                    end_datetime=end_datetime,
-                    interval=to_duration(f"{interval_hours} hours"),
-                    fill_method="linear",
-                )
-                * power_to_energy_per_interval_factor
-            ).tolist()
-        if "elecprice_marketprice_wh" in pred.record_keys:
-            prediction["elec_price_amt_kwh"] = (
-                pred.key_to_array(
-                    key="elecprice_marketprice_wh",
-                    start_datetime=start_datetime,
-                    end_datetime=end_datetime,
-                    interval=to_duration(f"{interval_hours} hours"),
-                    fill_method="ffill",
-                )
-                * 1000
-            ).tolist()
-        if "feed_in_tariff_wh" in pred.record_keys:
-            prediction["feed_in_tariff_amt_kwh"] = (
-                pred.key_to_array(
-                    key="feed_in_tariff_wh",
-                    start_datetime=start_datetime,
-                    end_datetime=end_datetime,
-                    interval=to_duration(f"{interval_hours} hours"),
-                    fill_method="linear",
-                )
-                * 1000
-            ).tolist()
-        if "weather_temp_air" in pred.record_keys:
-            prediction["weather_air_temp_celcius"] = pred.key_to_array(
-                key="weather_temp_air",
-                start_datetime=start_datetime,
-                end_datetime=end_datetime,
-                interval=to_duration(f"{interval_hours} hours"),
-                fill_method="linear",
-            ).tolist()
-        if "loadforecast_power_w" in pred.record_keys:
-            prediction["loadforecast_energy_wh"] = (
-                pred.key_to_array(
-                    key="loadforecast_power_w",
-                    start_datetime=start_datetime,
-                    end_datetime=end_datetime,
-                    interval=to_duration(f"{interval_hours} hours"),
-                    fill_method="linear",
-                )
-                * power_to_energy_per_interval_factor
-            ).tolist()
-        if "loadakkudoktor_std_power_w" in pred.record_keys:
-            prediction["loadakkudoktor_std_energy_wh"] = (
-                pred.key_to_array(
-                    key="loadakkudoktor_std_power_w",
-                    start_datetime=start_datetime,
-                    end_datetime=end_datetime,
-                    interval=to_duration(f"{interval_hours} hours"),
-                    fill_method="linear",
-                )
-                * power_to_energy_per_interval_factor
-            ).tolist()
-        if "loadakkudoktor_mean_power_w" in pred.record_keys:
-            prediction["loadakkudoktor_mean_energy_wh"] = (
-                pred.key_to_array(
-                    key="loadakkudoktor_mean_power_w",
-                    start_datetime=start_datetime,
-                    end_datetime=end_datetime,
-                    interval=to_duration(f"{interval_hours} hours"),
-                    fill_method="linear",
-                )
-                * power_to_energy_per_interval_factor
-            ).tolist()
-
-        optimization_solution = OptimizationSolution(
-            id=f"optimization-genetic@{to_datetime(as_string=True)}",
-            generated_at=to_datetime(),
-            comment="Optimization solution derived from GeneticSolution.",
-            valid_from=start_datetime,
-            valid_until=start_datetime.add(hours=self.config.optimization.horizon_hours),
-            total_losses_energy_wh=self.result.Gesamt_Verluste,
-            total_revenues_amt=self.result.Gesamteinnahmen_Euro,
-            total_costs_amt=self.result.Gesamtkosten_Euro,
-            fitness_score={
-                self.result.Gesamtkosten_Euro,
-            },
-            prediction=PydanticDateTimeDataFrame.from_dataframe(prediction),
-            solution=PydanticDateTimeDataFrame.from_dataframe(solution),
-        )
-
-        return optimization_solution
-
-    def energy_management_plan(self) -> EnergyManagementPlan:
-        """Provide the genetic solution as an energy management plan."""
-        from akkudoktoreos.core.ems import get_ems
-
-        start_datetime = get_ems().start_datetime
-        start_day_hour = start_datetime.in_timezone(self.config.general.timezone).hour
-        plan = EnergyManagementPlan(
-            id=f"plan-genetic@{to_datetime(as_string=True)}",
-            generated_at=to_datetime(),
-            instructions=[],
-            comment="Energy management plan derived from GeneticSolution.",
-        )
-
-        # Add battery instructions (fill rate based control)
-        last_operation_mode: Optional[str] = None
-        last_operation_mode_factor: Optional[float] = None
-        resource_id = "battery1"
-        # ac_charge, dc_charge, discharge_allowed start at hour 0 of start day
-        logger.debug("BAT: {} - {}", resource_id, self.ac_charge[start_day_hour:])
-        for hour_idx, rate in enumerate(self.ac_charge):
-            if hour_idx < start_day_hour:
-                continue
-            operation_mode, operation_mode_factor = self._battery_operation_from_solution(
-                self.ac_charge[hour_idx],
-                self.dc_charge[hour_idx],
-                bool(self.discharge_allowed[hour_idx]),
-            )
-            if (
-                operation_mode == last_operation_mode
-                and operation_mode_factor == last_operation_mode_factor
-            ):
-                # Skip, we already added the instruction
-                continue
-            last_operation_mode = operation_mode
-            last_operation_mode_factor = operation_mode_factor
-            execution_time = start_datetime.add(hours=hour_idx - start_day_hour)
-            plan.add_instruction(
-                FRBCInstruction(
-                    resource_id=resource_id,
-                    execution_time=execution_time,
-                    actuator_id=resource_id,
-                    operation_mode_id=operation_mode,
-                    operation_mode_factor=operation_mode_factor,
-                )
-            )
-
-        # Add EV battery instructions (fill rate based control)
-        # eautocharge_hours_float start at hour 0 of start day
-        if self.eauto_obj:
-            resource_id = self.eauto_obj.device_id
-            if self.eautocharge_hours_float is None:
-                # Electric vehicle is full enough. No load times.
-                logger.debug("EV: {} - SoC >= min, no optimization", resource_id)
-                plan.add_instruction(
-                    FRBCInstruction(
-                        resource_id=resource_id,
-                        execution_time=start_datetime,
-                        actuator_id=resource_id,
-                        operation_mode_id=BatteryOperationMode.IDLE,
-                        operation_mode_factor=1.0,
-                    )
-                )
-            else:
-                last_operation_mode = None
-                last_operation_mode_factor = None
-                logger.debug(
-                    "EV: {} - {}", resource_id, self.eautocharge_hours_float[start_day_hour:]
-                )
-                for hour_idx, rate in enumerate(self.eautocharge_hours_float):
-                    if hour_idx < start_day_hour:
-                        continue
-                    operation_mode, operation_mode_factor = self._battery_operation_from_solution(
-                        rate, 0.0, False
-                    )
-                    if (
-                        operation_mode == last_operation_mode
-                        and operation_mode_factor == last_operation_mode_factor
-                    ):
-                        # Skip, we already added the instruction
-                        continue
-                    last_operation_mode = operation_mode
-                    last_operation_mode_factor = operation_mode_factor
-                    execution_time = start_datetime.add(hours=hour_idx - start_day_hour)
-                    plan.add_instruction(
-                        FRBCInstruction(
-                            resource_id=resource_id,
-                            execution_time=execution_time,
-                            actuator_id=resource_id,
-                            operation_mode_id=operation_mode,
-                            operation_mode_factor=operation_mode_factor,
-                        )
-                    )
-
-        # Add home appliance instructions (demand driven based control)
-        if self.washingstart:
-            resource_id = "homeappliance1"
-            operation_mode = ApplianceOperationMode.RUN  # type: ignore[assignment]
-            operation_mode_factor = 1.0
-            execution_time = start_datetime.add(hours=self.washingstart - start_day_hour)
-            plan.add_instruction(
-                DDBCInstruction(
-                    resource_id=resource_id,
-                    execution_time=execution_time,
-                    actuator_id=resource_id,
-                    operation_mode_id=operation_mode,
-                    operation_mode_factor=operation_mode_factor,
-                )
-            )
-
-        return plan

src/akkudoktoreos/optimization/optimization.py

@@ -1,141 +1,40 @@
-from typing import Optional, Union
+from typing import List, Optional
 
-from pydantic import Field, model_validator
+from pydantic import Field
 
 from akkudoktoreos.config.configabc import SettingsBaseModel
-from akkudoktoreos.core.pydantic import (
-    PydanticBaseModel,
-    PydanticDateTimeDataFrame,
-)
-from akkudoktoreos.utils.datetimeutil import DateTime
+from akkudoktoreos.core.logging import get_logger
 
-
-class GeneticCommonSettings(SettingsBaseModel):
-    """General Genetic Optimization Algorithm Configuration."""
-
-    individuals: Optional[int] = Field(
-        default=300,
-        ge=10,
-        description="Number of individuals (solutions) to generate for the (initial) generation [>= 10]. Defaults to 300.",
-        examples=[300],
-    )
-
-    generations: Optional[int] = Field(
-        default=400,
-        ge=10,
-        description="Number of generations to evaluate the optimal solution [>= 10]. Defaults to 400.",
-        examples=[400],
-    )
-
-    seed: Optional[int] = Field(
-        default=None,
-        ge=0,
-        description="Fixed seed for genetic algorithm. Defaults to 'None' which means random seed.",
-        examples=[None],
-    )
-
-    penalties: Optional[dict[str, Union[float, int, str]]] = Field(
-        default=None,
-        description="A dictionary of penalty function parameters consisting of a penalty function parameter name and the associated value.",
-        examples=[
-            {"ev_soc_miss": 10},
-        ],
-    )
+logger = get_logger(__name__)
 
 
 class OptimizationCommonSettings(SettingsBaseModel):
-    """General Optimization Configuration."""
+    """General Optimization Configuration.
 
-    horizon_hours: Optional[int] = Field(
-        default=24,
-        ge=0,
-        description="The general time window within which the energy optimization goal shall be achieved [h]. Defaults to 24 hours.",
-        examples=[24],
+    Attributes:
+        hours (int): Number of hours for optimizations.
+    """
+
+    hours: Optional[int] = Field(
+        default=48, ge=0, description="Number of hours into the future for optimizations."
     )
 
-    interval: Optional[int] = Field(
-        default=3600,
-        ge=15 * 60,
-        le=60 * 60,
-        description="The optimization interval [sec].",
-        examples=[60 * 60, 15 * 60],
-    )
-
-    algorithm: Optional[str] = Field(
-        default="GENETIC",
-        description="The optimization algorithm.",
-        examples=["GENETIC"],
-    )
-
-    genetic: Optional[GeneticCommonSettings] = Field(
-        default=None,
-        description="Genetic optimization algorithm configuration.",
-        examples=[{"individuals": 400, "seed": None, "penalties": {"ev_soc_miss": 10}}],
-    )
-
-    @model_validator(mode="after")
-    def _enforce_algorithm_configuration(self) -> "OptimizationCommonSettings":
-        """Ensure algorithm default configuration is set."""
-        if self.algorithm is not None:
-            if self.algorithm.lower() == "genetic" and self.genetic is None:
-                self.genetic = GeneticCommonSettings()
-        return self
-
-
-class OptimizationSolution(PydanticBaseModel):
-    """General Optimization Solution."""
-
-    id: str = Field(..., description="Unique ID for the optimization solution.")
-
-    generated_at: DateTime = Field(..., description="Timestamp when the solution was generated.")
-
-    comment: Optional[str] = Field(
-        default=None, description="Optional comment or annotation for the solution."
-    )
-
-    valid_from: Optional[DateTime] = Field(
-        default=None, description="Start time of the optimization solution."
-    )
-
-    valid_until: Optional[DateTime] = Field(
-        default=None,
-        description="End time of the optimization solution.",
-    )
-
-    total_losses_energy_wh: float = Field(
-        description="The total losses in watt-hours over the entire period."
-    )
-
-    total_revenues_amt: float = Field(description="The total revenues [money amount].")
-
-    total_costs_amt: float = Field(description="The total costs [money amount].")
-
-    fitness_score: set[float] = Field(description="The fitness score as a set of fitness values.")
-
-    prediction: PydanticDateTimeDataFrame = Field(
-        description=(
-            "Datetime data frame with time series prediction data per optimization interval:"
-            "- pv_energy_wh: PV energy prediction (positive) in wh"
-            "- elec_price_amt_kwh: Electricity price prediction in money per kwh"
-            "- feed_in_tariff_amt_kwh: Feed in tariff prediction in money per kwh"
-            "- weather_temp_air_celcius: Temperature in °C"
-            "- loadforecast_energy_wh: Load mean energy prediction in wh"
-            "- loadakkudoktor_std_energy_wh: Load energy standard deviation prediction in wh"
-            "- loadakkudoktor_mean_energy_wh: Load mean energy prediction in wh"
-        )
-    )
-
-    solution: PydanticDateTimeDataFrame = Field(
-        description=(
-            "Datetime data frame with time series solution data per optimization interval:"
-            "- load_energy_wh: Load of all energy consumers in wh"
-            "- grid_energy_wh: Grid energy feed in (negative) or consumption (positive) in wh"
-            "- costs_amt: Costs in money amount"
-            "- revenue_amt: Revenue in money amount"
-            "- losses_energy_wh: Energy losses in wh"
-            "- <device-id>_operation_mode_id: Operation mode id of the device."
-            "- <device-id>_operation_mode_factor: Operation mode factor of the device."
-            "- <device-id>_soc_factor: State of charge of a battery/ electric vehicle device as factor of total capacity."
-            "- <device-id>_energy_wh: Energy consumption (positive) of a device in wh."
-        )
+    penalty: Optional[int] = Field(default=10, description="Penalty factor used in optimization.")
+
+    ev_available_charge_rates_percent: Optional[List[float]] = Field(
+        default=[
+            0.0,
+            6.0 / 16.0,
+            # 7.0 / 16.0,
+            8.0 / 16.0,
+            # 9.0 / 16.0,
+            10.0 / 16.0,
+            # 11.0 / 16.0,
+            12.0 / 16.0,
+            # 13.0 / 16.0,
+            14.0 / 16.0,
+            # 15.0 / 16.0,
+            1.0,
+        ],
+        description="Charge rates available for the EV in percent of maximum charge.",
     )

src/akkudoktoreos/optimization/optimizationabc.py

@@ -2,14 +2,14 @@
 
 from pydantic import ConfigDict
 
-from akkudoktoreos.core.coreabc import (
-    ConfigMixin,
-    EnergyManagementSystemMixin,
-    PredictionMixin,
-)
+from akkudoktoreos.core.coreabc import ConfigMixin, PredictionMixin
+from akkudoktoreos.core.logging import get_logger
+from akkudoktoreos.core.pydantic import PydanticBaseModel
+
+logger = get_logger(__name__)
 
 
-class OptimizationBase(ConfigMixin, PredictionMixin, EnergyManagementSystemMixin):
+class OptimizationBase(ConfigMixin, PredictionMixin, PydanticBaseModel):
     """Base class for handling optimization data.
 
     Enables access to EOS configuration data (attribute `config`) and EOS prediction data (attribute

src/akkudoktoreos/prediction/elecprice.py

@@ -1,28 +1,9 @@
 from typing import Optional
 
-from pydantic import Field, field_validator
+from pydantic import Field
 
 from akkudoktoreos.config.configabc import SettingsBaseModel
-from akkudoktoreos.prediction.elecpriceabc import ElecPriceProvider
 from akkudoktoreos.prediction.elecpriceimport import ElecPriceImportCommonSettings
-from akkudoktoreos.prediction.prediction import get_prediction
-
-prediction_eos = get_prediction()
-
-# Valid elecprice providers
-elecprice_providers = [
-    provider.provider_id()
-    for provider in prediction_eos.providers
-    if isinstance(provider, ElecPriceProvider)
-]
-
-
-class ElecPriceCommonProviderSettings(SettingsBaseModel):
-    """Electricity Price Prediction Provider Configuration."""
-
-    ElecPriceImport: Optional[ElecPriceImportCommonSettings] = Field(
-        default=None, description="ElecPriceImport settings", examples=[None]
-    )
 
 
 class ElecPriceCommonSettings(SettingsBaseModel):
@@ -34,35 +15,9 @@ class ElecPriceCommonSettings(SettingsBaseModel):
         examples=["ElecPriceAkkudoktor"],
     )
     charges_kwh: Optional[float] = Field(
-        default=None,
-        ge=0,
-        description="Electricity price charges [€/kWh]. Will be added to variable market price.",
-        examples=[0.21],
-    )
-    vat_rate: Optional[float] = Field(
-        default=1.19,
-        ge=0,
-        description="VAT rate factor applied to electricity price when charges are used.",
-        examples=[1.19],
+        default=None, ge=0, description="Electricity price charges (€/kWh).", examples=[0.21]
     )
 
-    provider_settings: ElecPriceCommonProviderSettings = Field(
-        default_factory=ElecPriceCommonProviderSettings,
-        description="Provider settings",
-        examples=[
-            # Example 1: Empty/default settings (all providers None)
-            {
-                "ElecPriceImport": None,
-            },
-        ],
+    provider_settings: Optional[ElecPriceImportCommonSettings] = Field(
+        default=None, description="Provider settings", examples=[None]
     )
-
-    # Validators
-    @field_validator("provider", mode="after")
-    @classmethod
-    def validate_provider(cls, value: Optional[str]) -> Optional[str]:
-        if value is None or value in elecprice_providers:
-            return value
-        raise ValueError(
-            f"Provider '{value}' is not a valid electricity price provider: {elecprice_providers}."
-        )

src/akkudoktoreos/prediction/elecpriceabc.py

@@ -9,8 +9,11 @@ from typing import List, Optional
 
 from pydantic import Field, computed_field
 
+from akkudoktoreos.core.logging import get_logger
 from akkudoktoreos.prediction.predictionabc import PredictionProvider, PredictionRecord
 
+logger = get_logger(__name__)
+
 
 class ElecPriceDataRecord(PredictionRecord):
     """Represents a electricity price data record containing various price attributes at a specific datetime.

src/akkudoktoreos/prediction/elecpriceakkudoktor.py

@@ -11,15 +11,17 @@ from typing import Any, List, Optional, Union
 import numpy as np
 import pandas as pd
 import requests
-from loguru import logger
 from pydantic import ValidationError
 from statsmodels.tsa.holtwinters import ExponentialSmoothing
 
 from akkudoktoreos.core.cache import cache_in_file
+from akkudoktoreos.core.logging import get_logger
 from akkudoktoreos.core.pydantic import PydanticBaseModel
 from akkudoktoreos.prediction.elecpriceabc import ElecPriceProvider
 from akkudoktoreos.utils.datetimeutil import to_datetime, to_duration
 
+logger = get_logger(__name__)
+
 
 class AkkudoktorElecPriceMeta(PydanticBaseModel):
     start_timestamp: str
@@ -102,13 +104,12 @@ class ElecPriceAkkudoktor(ElecPriceProvider):
             - add the file cache again.
         """
         source = "https://api.akkudoktor.net"
-        if not self.ems_start_datetime:
-            raise ValueError(f"Start DateTime not set: {self.ems_start_datetime}")
+        assert self.start_datetime  # mypy fix
         # Try to take data from 5 weeks back for prediction
-        date = to_datetime(self.ems_start_datetime - to_duration("35 days"), as_string="YYYY-MM-DD")
+        date = to_datetime(self.start_datetime - to_duration("35 days"), as_string="YYYY-MM-DD")
         last_date = to_datetime(self.end_datetime, as_string="YYYY-MM-DD")
         url = f"{source}/prices?start={date}&end={last_date}&tz={self.config.general.timezone}"
-        response = requests.get(url, timeout=10)
+        response = requests.get(url)
         logger.debug(f"Response from {url}: {response}")
         response.raise_for_status()  # Raise an error for bad responses
         akkudoktor_data = self._validate_data(response.content)
@@ -147,8 +148,7 @@ class ElecPriceAkkudoktor(ElecPriceProvider):
         """
         # Get Akkudoktor electricity price data
         akkudoktor_data = self._request_forecast(force_update=force_update)  # type: ignore
-        if not self.ems_start_datetime:
-            raise ValueError(f"Start DateTime not set: {self.ems_start_datetime}")
+        assert self.start_datetime  # mypy fix
 
         # Assumption that all lists are the same length and are ordered chronologically
         # in ascending order and have the same timestamps.
@@ -178,21 +178,18 @@ class ElecPriceAkkudoktor(ElecPriceProvider):
         )
 
         amount_datasets = len(self.records)
-        if not highest_orig_datetime:  # mypy fix
-            error_msg = f"Highest original datetime not available: {highest_orig_datetime}"
-            logger.error(error_msg)
-            raise ValueError(error_msg)
+        assert highest_orig_datetime  # mypy fix
 
         # some of our data is already in the future, so we need to predict less. If we got less data we increase the prediction hours
         needed_hours = int(
             self.config.prediction.hours
-            - ((highest_orig_datetime - self.ems_start_datetime).total_seconds() // 3600)
+            - ((highest_orig_datetime - self.start_datetime).total_seconds() // 3600)
         )
 
         if needed_hours <= 0:
             logger.warning(
-                f"No prediction needed. needed_hours={needed_hours}, hours={self.config.prediction.hours},highest_orig_datetime {highest_orig_datetime}, start_datetime {self.ems_start_datetime}"
-            )  # this might keep data longer than self.ems_start_datetime + self.config.prediction.hours in the records
+                f"No prediction needed. needed_hours={needed_hours}, hours={self.config.prediction.hours},highest_orig_datetime {highest_orig_datetime}, start_datetime {self.start_datetime}"
+            )  # this might keep data longer than self.start_datetime + self.config.prediction.hours in the records
             return
 
         if amount_datasets > 800:  # we do the full ets with seasons of 1 week

src/akkudoktoreos/prediction/elecpriceenergycharts.py

@@ -1,257 +0,0 @@
-"""Retrieves and processes electricity price forecast data from Energy-Charts.
-
-This module provides classes and mappings to manage electricity price data obtained from the
-Energy-Charts API, including support for various electricity price attributes such as temperature,
-humidity, cloud cover, and solar irradiance. The data is mapped to the `ElecPriceDataRecord`
-format, enabling consistent access to forecasted and historical electricity price attributes.
-"""
-
-from datetime import datetime
-from typing import Any, List, Optional, Union
-
-import numpy as np
-import pandas as pd
-import requests
-from loguru import logger
-from pydantic import ValidationError
-from statsmodels.tsa.holtwinters import ExponentialSmoothing
-
-from akkudoktoreos.core.cache import cache_in_file
-from akkudoktoreos.core.pydantic import PydanticBaseModel
-from akkudoktoreos.prediction.elecpriceabc import ElecPriceProvider
-from akkudoktoreos.utils.datetimeutil import to_datetime, to_duration
-
-
-class EnergyChartsElecPrice(PydanticBaseModel):
-    license_info: str
-    unix_seconds: List[int]
-    price: List[float]
-    unit: str
-    deprecated: bool
-
-
-class ElecPriceEnergyCharts(ElecPriceProvider):
-    """Fetch and process electricity price forecast data from Energy-Charts.
-
-    ElecPriceEnergyCharts is a singleton-based class that retrieves electricity price forecast data
-    from the Energy-Charts API and maps it to `ElecPriceDataRecord` fields, applying
-    any necessary scaling or unit corrections. It manages the forecast over a range
-    of hours into the future and retains historical data.
-
-    Attributes:
-        hours (int, optional): Number of hours in the future for the forecast.
-        historic_hours (int, optional): Number of past hours for retaining data.
-        start_datetime (datetime, optional): Start datetime for forecasts, defaults to the current datetime.
-        end_datetime (datetime, computed): The forecast's end datetime, computed based on `start_datetime` and `hours`.
-        keep_datetime (datetime, computed): The datetime to retain historical data, computed from `start_datetime` and `historic_hours`.
-
-    Methods:
-        provider_id(): Returns a unique identifier for the provider.
-        _request_forecast(): Fetches the forecast from the Energy-Charts API.
-        _update_data(): Processes and updates forecast data from Energy-Charts in ElecPriceDataRecord format.
-    """
-
-    highest_orig_datetime: Optional[datetime] = None
-
-    @classmethod
-    def provider_id(cls) -> str:
-        """Return the unique identifier for the Energy-Charts provider."""
-        return "ElecPriceEnergyCharts"
-
-    @classmethod
-    def _validate_data(cls, json_str: Union[bytes, Any]) -> EnergyChartsElecPrice:
-        """Validate Energy-Charts Electricity Price forecast data."""
-        try:
-            energy_charts_data = EnergyChartsElecPrice.model_validate_json(json_str)
-        except ValidationError as e:
-            error_msg = ""
-            for error in e.errors():
-                field = " -> ".join(str(x) for x in error["loc"])
-                message = error["msg"]
-                error_type = error["type"]
-                error_msg += f"Field: {field}\nError: {message}\nType: {error_type}\n"
-            logger.error(f"Energy-Charts schema change: {error_msg}")
-            raise ValueError(error_msg)
-        return energy_charts_data
-
-    @cache_in_file(with_ttl="1 hour")
-    def _request_forecast(self, start_date: Optional[str] = None) -> EnergyChartsElecPrice:
-        """Fetch electricity price forecast data from Energy-Charts API.
-
-        This method sends a request to Energy-Charts API to retrieve forecast data for a specified
-        date range. The response data is parsed and returned as JSON for further processing.
-
-        Returns:
-            dict: The parsed JSON response from Energy-Charts API containing forecast data.
-
-        Raises:
-            ValueError: If the API response does not include expected `electricity price` data.
-        """
-        source = "https://api.energy-charts.info"
-        if start_date is None:
-            # Try to take data from 5 weeks back for prediction
-            start_date = to_datetime(
-                self.ems_start_datetime - to_duration("35 days"), as_string="YYYY-MM-DD"
-            )
-
-        last_date = to_datetime(self.end_datetime, as_string="YYYY-MM-DD")
-        url = f"{source}/price?bzn=DE-LU&start={start_date}&end={last_date}"
-        response = requests.get(url, timeout=30)
-        logger.debug(f"Response from {url}: {response}")
-        response.raise_for_status()  # Raise an error for bad responses
-        energy_charts_data = self._validate_data(response.content)
-        # We are working on fresh data (no cache), report update time
-        self.update_datetime = to_datetime(in_timezone=self.config.general.timezone)
-        return energy_charts_data
-
-    def _parse_data(self, energy_charts_data: EnergyChartsElecPrice) -> pd.Series:
-        # Assumption that all lists are the same length and are ordered chronologically
-        # in ascending order and have the same timestamps.
-
-        # Get charges_kwh in wh
-        charges_wh = (self.config.elecprice.charges_kwh or 0) / 1000
-
-        # Initialize
-        highest_orig_datetime = None  # newest datetime from the api after that we want to update.
-        series_data = pd.Series(dtype=float)  # Initialize an empty series
-
-        # Iterate over timestamps and prices together
-        for unix_sec, price_eur_per_mwh in zip(
-            energy_charts_data.unix_seconds, energy_charts_data.price
-        ):
-            orig_datetime = to_datetime(unix_sec, in_timezone=self.config.general.timezone)
-
-            # Track the latest datetime
-            if highest_orig_datetime is None or orig_datetime > highest_orig_datetime:
-                highest_orig_datetime = orig_datetime
-
-            # Convert EUR/MWh to EUR/Wh, apply charges and VAT if charges > 0
-            if charges_wh > 0:
-                vat_rate = self.config.elecprice.vat_rate or 1.19
-                price_wh = ((price_eur_per_mwh / 1_000_000) + charges_wh) * vat_rate
-            else:
-                price_wh = price_eur_per_mwh / 1_000_000
-
-            # Store in series
-            series_data.at[orig_datetime] = price_wh
-
-        return series_data
-
-    def _cap_outliers(self, data: np.ndarray, sigma: int = 2) -> np.ndarray:
-        mean = data.mean()
-        std = data.std()
-        lower_bound = mean - sigma * std
-        upper_bound = mean + sigma * std
-        capped_data = data.clip(min=lower_bound, max=upper_bound)
-        return capped_data
-
-    def _predict_ets(self, history: np.ndarray, seasonal_periods: int, hours: int) -> np.ndarray:
-        clean_history = self._cap_outliers(history)
-        model = ExponentialSmoothing(
-            clean_history, seasonal="add", seasonal_periods=seasonal_periods
-        ).fit()
-        return model.forecast(hours)
-
-    def _predict_median(self, history: np.ndarray, hours: int) -> np.ndarray:
-        clean_history = self._cap_outliers(history)
-        return np.full(hours, np.median(clean_history))
-
-    def _update_data(
-        self, force_update: Optional[bool] = False
-    ) -> None:  # tuple[np.ndarray, np.ndarray, np.ndarray]:
-        """Update forecast data in the ElecPriceDataRecord format.
-
-        Retrieves data from Energy-Charts, maps each Energy-Charts field to the corresponding
-        `ElecPriceDataRecord` and applies any necessary scaling.
-
-        The final mapped and processed data is inserted into the sequence as `ElecPriceDataRecord`.
-        """
-        # New prices are available every day at 14:00
-        now = pd.Timestamp.now(tz=self.config.general.timezone)
-        midnight = now.normalize()
-        hours_ahead = 23 if now.time() < pd.Timestamp("14:00").time() else 47
-        end = midnight + pd.Timedelta(hours=hours_ahead)
-
-        if not self.ems_start_datetime:
-            raise ValueError(f"Start DateTime not set: {self.ems_start_datetime}")
-
-        # Determine if update is needed and how many days
-        past_days = 35
-        if self.highest_orig_datetime:
-            history_series = self.key_to_series(
-                key="elecprice_marketprice_wh", start_datetime=self.ems_start_datetime
-            )
-            # If history lower, then start_datetime
-            if history_series.index.min() <= self.ems_start_datetime:
-                past_days = 0
-
-            needs_update = end > self.highest_orig_datetime
-        else:
-            needs_update = True
-
-        if needs_update:
-            logger.info(
-                f"Update ElecPriceEnergyCharts is needed, last in history: {self.highest_orig_datetime}"
-            )
-            # Set start_date try to take data from 5 weeks back for prediction
-            start_date = to_datetime(
-                self.ems_start_datetime - to_duration(f"{past_days} days"), as_string="YYYY-MM-DD"
-            )
-            # Get Energy-Charts electricity price data
-            energy_charts_data = self._request_forecast(
-                start_date=start_date, force_update=force_update
-            )  # type: ignore
-
-            # Parse and store data
-            series_data = self._parse_data(energy_charts_data)
-            self.highest_orig_datetime = series_data.index.max()
-            self.key_from_series("elecprice_marketprice_wh", series_data)
-        else:
-            logger.info(
-                f"No Update ElecPriceEnergyCharts is needed, last in history: {self.highest_orig_datetime}"
-            )
-
-        # Generate history array for prediction
-        history = self.key_to_array(
-            key="elecprice_marketprice_wh",
-            end_datetime=self.highest_orig_datetime,
-            fill_method="linear",
-        )
-
-        amount_datasets = len(self.records)
-        if not self.highest_orig_datetime:  # mypy fix
-            error_msg = f"Highest original datetime not available: {self.highest_orig_datetime}"
-            logger.error(error_msg)
-            raise ValueError(error_msg)
-
-        # some of our data is already in the future, so we need to predict less. If we got less data we increase the prediction hours
-        needed_hours = int(
-            self.config.prediction.hours
-            - ((self.highest_orig_datetime - self.ems_start_datetime).total_seconds() // 3600)
-        )
-
-        if needed_hours <= 0:
-            logger.warning(
-                f"No prediction needed. needed_hours={needed_hours}, hours={self.config.prediction.hours},highest_orig_datetime {self.highest_orig_datetime}, start_datetime {self.ems_start_datetime}"
-            )  # this might keep data longer than self.ems_start_datetime + self.config.prediction.hours in the records
-            return
-
-        if amount_datasets > 800:  # we do the full ets with seasons of 1 week
-            prediction = self._predict_ets(history, seasonal_periods=168, hours=needed_hours)
-        elif amount_datasets > 168:  # not enough data to do seasons of 1 week, but enough for 1 day
-            prediction = self._predict_ets(history, seasonal_periods=24, hours=needed_hours)
-        elif amount_datasets > 0:  # not enough data for ets, do median
-            prediction = self._predict_median(history, hours=needed_hours)
-        else:
-            logger.error("No data available for prediction")
-            raise ValueError("No data available")
-
-        # write predictions into the records, update if exist.
-        prediction_series = pd.Series(
-            data=prediction,
-            index=[
-                self.highest_orig_datetime + to_duration(f"{i + 1} hours")
-                for i in range(len(prediction))
-            ],
-        )
-        self.key_from_series("elecprice_marketprice_wh", prediction_series)

src/akkudoktoreos/prediction/elecpriceimport.py

@@ -9,13 +9,15 @@ format, enabling consistent access to forecasted and historical elecprice attrib
 from pathlib import Path
 from typing import Optional, Union
 
-from loguru import logger
 from pydantic import Field, field_validator
 
 from akkudoktoreos.config.configabc import SettingsBaseModel
+from akkudoktoreos.core.logging import get_logger
 from akkudoktoreos.prediction.elecpriceabc import ElecPriceProvider
 from akkudoktoreos.prediction.predictionabc import PredictionImportProvider
 
+logger = get_logger(__name__)
+
 
 class ElecPriceImportCommonSettings(SettingsBaseModel):
     """Common settings for elecprice data import from file or JSON String."""
@@ -61,16 +63,15 @@ class ElecPriceImport(ElecPriceProvider, PredictionImportProvider):
         return "ElecPriceImport"
 
     def _update_data(self, force_update: Optional[bool] = False) -> None:
-        if self.config.elecprice.provider_settings.ElecPriceImport is None:
+        if self.config.elecprice.provider_settings is None:
             logger.debug(f"{self.provider_id()} data update without provider settings.")
             return
-        if self.config.elecprice.provider_settings.ElecPriceImport.import_file_path:
+        if self.config.elecprice.provider_settings.import_file_path:
             self.import_from_file(
-                self.config.elecprice.provider_settings.ElecPriceImport.import_file_path,
+                self.config.elecprice.provider_settings.import_file_path,
                 key_prefix="elecprice",
             )
-        if self.config.elecprice.provider_settings.ElecPriceImport.import_json:
+        if self.config.elecprice.provider_settings.import_json:
             self.import_from_json(
-                self.config.elecprice.provider_settings.ElecPriceImport.import_json,
-                key_prefix="elecprice",
+                self.config.elecprice.provider_settings.import_json, key_prefix="elecprice"
             )

src/akkudoktoreos/prediction/feedintariff.py

@@ -1,61 +0,0 @@
-from typing import Optional
-
-from pydantic import Field, field_validator
-
-from akkudoktoreos.config.configabc import SettingsBaseModel
-from akkudoktoreos.prediction.feedintariffabc import FeedInTariffProvider
-from akkudoktoreos.prediction.feedintarifffixed import FeedInTariffFixedCommonSettings
-from akkudoktoreos.prediction.feedintariffimport import FeedInTariffImportCommonSettings
-from akkudoktoreos.prediction.prediction import get_prediction
-
-prediction_eos = get_prediction()
-
-# Valid feedintariff providers
-feedintariff_providers = [
-    provider.provider_id()
-    for provider in prediction_eos.providers
-    if isinstance(provider, FeedInTariffProvider)
-]
-
-
-class FeedInTariffCommonProviderSettings(SettingsBaseModel):
-    """Feed In Tariff Prediction Provider Configuration."""
-
-    FeedInTariffFixed: Optional[FeedInTariffFixedCommonSettings] = Field(
-        default=None, description="FeedInTariffFixed settings", examples=[None]
-    )
-    FeedInTariffImport: Optional[FeedInTariffImportCommonSettings] = Field(
-        default=None, description="FeedInTariffImport settings", examples=[None]
-    )
-
-
-class FeedInTariffCommonSettings(SettingsBaseModel):
-    """Feed In Tariff Prediction Configuration."""
-
-    provider: Optional[str] = Field(
-        default=None,
-        description="Feed in tariff provider id of provider to be used.",
-        examples=["FeedInTariffFixed", "FeedInTarifImport"],
-    )
-
-    provider_settings: FeedInTariffCommonProviderSettings = Field(
-        default_factory=FeedInTariffCommonProviderSettings,
-        description="Provider settings",
-        examples=[
-            # Example 1: Empty/default settings (all providers None)
-            {
-                "FeedInTariffFixed": None,
-                "FeedInTariffImport": None,
-            },
-        ],
-    )
-
-    # Validators
-    @field_validator("provider", mode="after")
-    @classmethod
-    def validate_provider(cls, value: Optional[str]) -> Optional[str]:
-        if value is None or value in feedintariff_providers:
-            return value
-        raise ValueError(
-            f"Provider '{value}' is not a valid feed in tariff provider: {feedintariff_providers}."
-        )

src/akkudoktoreos/prediction/feedintariffabc.py

@@ -1,58 +0,0 @@
-"""Abstract and base classes for feed in tariff predictions.
-
-Notes:
-    - Ensure appropriate API keys or configurations are set up if required by external data sources.
-"""
-
-from abc import abstractmethod
-from typing import List, Optional
-
-from pydantic import Field, computed_field
-
-from akkudoktoreos.prediction.predictionabc import PredictionProvider, PredictionRecord
-
-
-class FeedInTariffDataRecord(PredictionRecord):
-    """Represents a feed in tariff data record containing various price attributes at a specific datetime.
-
-    Attributes:
-        date_time (Optional[AwareDatetime]): The datetime of the record.
-
-    """
-
-    feed_in_tariff_wh: Optional[float] = Field(None, description="Feed in tariff per Wh (€/Wh)")
-
-    # Computed fields
-    @computed_field  # type: ignore[prop-decorator]
-    @property
-    def feed_in_tariff_kwh(self) -> Optional[float]:
-        """Feed in tariff per kWh (€/kWh).
-
-        Convenience attribute calculated from `feed_in_tariff_wh`.
-        """
-        if self.feed_in_tariff_wh is None:
-            return None
-        return self.feed_in_tariff_wh * 1000.0
-
-
-class FeedInTariffProvider(PredictionProvider):
-    """Abstract base class for feed in tariff providers.
-
-    FeedInTariffProvider is a thread-safe singleton, ensuring only one instance of this class is created.
-
-    Configuration variables:
-        feed in tariff_provider (str): Prediction provider for feed in tarif.
-    """
-
-    # overload
-    records: List[FeedInTariffDataRecord] = Field(
-        default_factory=list, description="List of FeedInTariffDataRecord records"
-    )
-
-    @classmethod
-    @abstractmethod
-    def provider_id(cls) -> str:
-        return "FeedInTariffProvider"
-
-    def enabled(self) -> bool:
-        return self.provider_id() == self.config.feedintariff.provider

src/akkudoktoreos/prediction/feedintarifffixed.py

@@ -1,48 +0,0 @@
-"""Provides feed in tariff data."""
-
-from typing import Optional
-
-from loguru import logger
-from pydantic import Field
-
-from akkudoktoreos.config.configabc import SettingsBaseModel
-from akkudoktoreos.prediction.feedintariffabc import FeedInTariffProvider
-from akkudoktoreos.utils.datetimeutil import to_datetime
-
-
-class FeedInTariffFixedCommonSettings(SettingsBaseModel):
-    """Common settings for elecprice fixed price."""
-
-    feed_in_tariff_kwh: Optional[float] = Field(
-        default=None,
-        ge=0,
-        description="Electricity price feed in tariff [€/kWH].",
-        examples=[0.078],
-    )
-
-
-class FeedInTariffFixed(FeedInTariffProvider):
-    """Fixed price feed in tariff data.
-
-    FeedInTariffFixed is a singleton-based class that retrieves elecprice data.
-    """
-
-    @classmethod
-    def provider_id(cls) -> str:
-        """Return the unique identifier for the FeedInTariffFixed provider."""
-        return "FeedInTariffFixed"
-
-    def _update_data(self, force_update: Optional[bool] = False) -> None:
-        error_msg = "Feed in tariff not provided"
-        try:
-            feed_in_tariff = (
-                self.config.feedintariff.provider_settings.FeedInTariffFixed.feed_in_tariff_kwh
-            )
-        except:
-            logger.exception(error_msg)
-            raise ValueError(error_msg)
-        if feed_in_tariff is None:
-            logger.error(error_msg)
-            raise ValueError(error_msg)
-        feed_in_tariff_wh = feed_in_tariff / 1000
-        self.update_value(to_datetime(), "feed_in_tariff_wh", feed_in_tariff_wh)