Normann 480adf8100
Data prefetch ems for feature (#420)
* Pre-fetch data

* maintanance and extend tests

* comment clean up

* nansum usage (to be save)

* Feature/config nested (#421)

* Nested config, devices registry

 * All config now nested.
    - Use default config from model field default values. If providers
      should be enabled by default, non-empty default config file could
      be provided again.
    - Environment variable support with EOS_ prefix and __ between levels,
      e.g. EOS_SERVER__EOS_SERVER_PORT=8503 where all values are case
      insensitive.
      For more information see:
      https://docs.pydantic.dev/latest/concepts/pydantic_settings/#parsing-environment-variable-values
    - Use devices as registry for configured devices. DeviceBase as base
      class with for now just initializion support (in the future expand
      to operations during optimization).
    - Strip down ConfigEOS to the only configuration instance. Reload
      from file or reset to defaults is possible.

 * Fix multi-initialization of derived SingletonMixin classes.

* Documentation: Support nested config

 * Add examples to pydantic models.

* EOSdash: Support nested types

* Rename settings variables (remove prefixes)

* Fix API endpoint

* Fix EOSdash startup (docker)

 * Docker: Copy the same directory structure (src/) to support the
   lifespan startup of EOSdash.
   Use EOS_SERVER_EOSDASH_SESSKEY environment variable to provide
   EOSdash with session key.

* PR review

* PVForecast: planes as nested config (list)

* Update manual documentation for nested config.

 * Add config_file_path, config_folder_path back to general
   (ConfigCommonSettings). Overwrite in docs generation.

* Config: Move lat/long/timezone from prediction to general

* Docs: Add global example documentation.

 * merge_models: Use deecopy to not change input data.

* EOSdash: Sort config by name

* Review comments

* Feature/config nested dependabot req. (#415)

* Bump numpydantic from 1.6.4 to 1.6.7 (#413)

Bumps [numpydantic](https://github.com/p2p-ld/numpydantic) from 1.6.4 to 1.6.7.
- [Release notes](https://github.com/p2p-ld/numpydantic/releases)
- [Changelog](https://github.com/p2p-ld/numpydantic/blob/main/docs/changelog.md)
- [Commits](https://github.com/p2p-ld/numpydantic/compare/v1.6.4...v1.6.7)

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* Bump timezonefinder from 6.5.7 to 6.5.8 (#414)

Bumps [timezonefinder](https://github.com/jannikmi/timezonefinder) from 6.5.7 to 6.5.8.
- [Release notes](https://github.com/jannikmi/timezonefinder/releases)
- [Changelog](https://github.com/jannikmi/timezonefinder/blob/master/CHANGELOG.rst)
- [Commits](https://github.com/jannikmi/timezonefinder/compare/6.5.7...6.5.8)

---
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- dependency-name: timezonefinder
  dependency-type: direct:production
  update-type: version-update:semver-patch
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* Bump pydantic from 2.10.5 to 2.10.6 (#412)

Bumps [pydantic](https://github.com/pydantic/pydantic) from 2.10.5 to 2.10.6.
- [Release notes](https://github.com/pydantic/pydantic/releases)
- [Changelog](https://github.com/pydantic/pydantic/blob/main/HISTORY.md)
- [Commits](https://github.com/pydantic/pydantic/compare/v2.10.5...v2.10.6)

---
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- dependency-name: pydantic
  dependency-type: direct:production
  update-type: version-update:semver-patch
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* Bump fastapi[standard] from 0.115.6 to 0.115.7 (#411)

Bumps [fastapi[standard]](https://github.com/fastapi/fastapi) from 0.115.6 to 0.115.7.
- [Release notes](https://github.com/fastapi/fastapi/releases)
- [Commits](https://github.com/fastapi/fastapi/compare/0.115.6...0.115.7)

---
updated-dependencies:
- dependency-name: fastapi[standard]
  dependency-type: direct:production
  update-type: version-update:semver-patch
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* Readme: Add hint for interfering ports on Synology Closes #408 (#419)

* Pics or it didn't happen (#402)

* inverter added

* png creation

* save svg into cache folder

* mypy

* comment

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* inverter, prediction.hours

* self.config.general.data_cache_path

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2025-01-26 19:12:14 +01:00
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Energy System Simulation and Optimization

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.

Documentation can be found at Akkudoktor-EOS.

Getting Involved

See CONTRIBUTING.md.

Installation

The project requires Python 3.10 or newer. Official docker images can be found at akkudoktor/eos.

Following sections describe how to locally start the EOS server on http://localhost:8503.

Run from source

Install dependencies in virtual environment:

Linux:

python -m venv .venv
.venv/bin/pip install -r requirements.txt

Windows:

python -m venv .venv
 .venv\Scripts\pip install -r requirements.txt

Finally, start the EOS server:

Linux:

.venv/bin/python src/akkudoktoreos/server/eos.py

Windows:

.venv\Scripts\python src/akkudoktoreos/server/eos.py

Docker

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). If the ports are occupied, you will need to reconfigure the exported ports accordingly.

Configuration

This project uses the EOS.config.json file to manage configuration settings.

Default Configuration

A default configuration file default.config.json is provided. This file contains all the necessary configuration keys with their default values.

Custom Configuration

Users can specify a custom configuration directory by setting the environment variable EOS_DIR.

  • 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.

Configuration Updates

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.

Classes and Functionalities

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:

  • Battery: Simulates a battery storage system, including capacity, state of charge, and now charge and discharge losses.

  • PVForecast: Provides forecast data for photovoltaic generation, based on weather data and historical generation data.

  • Load: Models the load requirements of a household or business, enabling the prediction of future energy demand.

  • Heatpump: Simulates a heat pump, including its energy consumption and efficiency under various operating conditions.

  • Strompreis: Provides information on electricity prices, enabling optimization of energy consumption and generation based on tariff information.

  • EMS: The Energy Management System (EMS) coordinates the interaction between the various components, performs optimization, and simulates the operation of the entire energy system.

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.

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.

Server API

See the Swagger API documentation for detailed information: EOS OpenAPI Spec

Further resources

Description
This repository features an Energy Optimization System (EOS) that optimizes energy distribution, usage for batteries, heat pumps& household devices. It includes predictive models for electricity prices (planned), load forecasting& dynamic optimization to maximize energy efficiency & minimize costs. Founder Dr. Andreas Schmitz (YouTube @akkudoktor)
Readme 32 MiB
Languages
Python 99.3%
Makefile 0.6%
Dockerfile 0.1%