EOS/CHANGELOG.md

# 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/).

## Unreleased

### Energy planning update: read before upgrading

This update brings the complete GENETIC energy planner to the current EOS interfaces.
It can plan in 15-minute steps, consider battery export, schedule EV charging before
departure and fit flexible household loads into their allowed time windows. Batteries and
inverters are now described by **device profiles**: the technical data of a model (limits over
temperature and state of charge, efficiency curves) comes from a profile, and the settings of a
device only hold how the installed unit differs from it. New measurement tools make missing
data, energy use and battery-capacity estimates easier to inspect. New providers add
electricity prices, feed-in tariffs and PV forecasts, including a local, calibratable PV
model. EOSdash gains a plan page, a setup assistant and a profile editor.

**Existing installations need an integration check before upgrading.** Device settings
now use stable IDs instead of list positions. The new `POST /v1/optimize` takes hardware
and schedules from EOS configuration; its request body supplies only current observations,
forecasts and a previous plan. The legacy `POST /optimize` remains available with GENETIC0;
changing that URL alone does not migrate an integration to the new planner.

Automatic configuration migration handles supported old settings, but cannot rewrite
Home Assistant automations, Node-RED flows or custom scripts. It moves battery and inverter
settings into the profile adjustment and **drops the configured efficiencies**: the profile
efficiencies apply instead, which can change plans. The configuration is now saved to the
configuration file automatically. Back up configuration and stored data, then check device IDs,
battery and inverter profiles, tariff units, battery measurements and the resulting plans. Read the
[upgrade guide](docs/akkudoktoreos/upgrade-genetic.md) for the compatibility changes and a short
test procedure. These changes are not yet a tagged release.

### Added

#### Energy planning

- Complete GENETIC planning at 15- or 60-minute intervals, timestamp-aligned reuse of
  previous plans, explicitly enabled battery export, EV deadlines and flexible consumers.
- Forecast continuation and AUTO/FIXED valuation of energy left in the battery, separate
  from battery wear costs. Continuation helps choose the plan; it does not add commands
  beyond the configured control horizon.
- Optional `ems.notify_url`: EOS sends an HTTP POST with a small JSON event after every
  completed optimization, so a client can fetch the new plan right away instead of polling.
- Configuration-driven `POST /v1/optimize` and a PDF report of the stored GENETIC result.
- Optional periodic full charge of a stationary battery for GENETIC, e.g. to recalibrate an LFP
  battery: `full_charge_soc_percentage` and `full_charge_allow_grid` of the battery, and the
  interval and detection threshold from the battery profile or its `profile_adjustment`
  (`full_charge_interval_days`, unset = off; `full_charge_detect_percentage`). The last full
  charge is taken from the measured state of charge.
- Optional size limit for the GENETIC fitness cache: `optimization.genetic.fitness_cache_max_entries`
  (`null` = unbounded, the default; `0` = off; N = at most N keys). Bounding it lowers peak memory of
  a run by up to ~150 MB on small devices without changing the plan.
- Parallel fitness evaluation for GENETIC: `optimization.genetic.workers` (unset = automatic:
  at most two worker processes, one CPU core and a container CPU limit stay free; `1` = one
  process as before) and `optimization.genetic.pin_workers` (workers pinned to the last
  cores, default on). For a fixed seed the plan is identical to a single-process run.
- Time windows (e.g. for flexible loads and fixed prices) accept `month` (1–12) and an explicit
  `end_time`. A wall-clock `end_time` stays correct across DST changes, unlike a fixed `duration`,
  which becomes optional.

#### Devices

- Battery profiles: a profile describes a battery model, with charge and discharge limit maps
  over temperature and state of charge, efficiency curves over the C-rate, technical SoC bounds,
  hard temperature cutoffs and, for a model, its nominal capacity and power limits. A battery or
  electric vehicle selects it by `battery_profile` (default: the packaged `generic-lfp`) and
  sets the differences of the installed unit in `profile_adjustment` (capacity, power limits of
  the installation, periodic full charge). The effective values are resolved at the measured
  temperature, SoC and power and shown as `effective_*` fields. GENETIC simulates the battery
  with the limits and efficiencies of the profile over the SoC.
- Inverter profiles: nominal AC power, DC→AC and AC→DC efficiency curves over the load, the AC
  charge power limit of the model and an optional power derating over the ambient temperature
  (measurement key `<device_id>-temperature-degc`). An inverter selects it by `inverter_profile`
  (default: the packaged `generic-hybrid`) and sets `nominal_power_w`, `max_power_w` and
  `max_ac_charge_power_w` in `profile_adjustment`; an AC charge limit of 0 disables AC charging.
  GENETIC uses the load-dependent efficiency of the profile.
- User profiles are stored in the user data directory and override packaged profiles of the
  same name. The REST API `/v1/profile` lists, reads (document and JSON schema), saves and
  deletes profiles; saved profiles apply at once.
- A missing, unreadable or invalid profile never cancels an optimization run: the packaged
  generic profile, and the former defaults if that is not usable either, are used and an error
  is logged.

#### Predictions and providers

- Electricity price provider `ElecPriceTibber`.
- Feed-in tariff providers: `FeedInTariffEnergyCharts` (day-ahead market price, extended by an
  ETS forecast beyond the published period), `FeedInTariffSMARD`, `FeedInTariffAkkudoktor`,
  `FeedInTariffTibber` and `FeedInTariffDvhubOnline`.
- Electricity fee framework (`elecfee`) with the providers `ElecFeeFixed` and `ElecFeeImport`:
  fees per Wh and percentage surcharges (e.g. grid fees, VAT) on consumed energy, and deductions
  from the feed-in tariff. It replaces the former `elecprice.charges_kwh` / `vat_rate` fields.
- PV forecast providers giving operators more forecast sources to choose from in addition to
  Akkudoktor, VRM and Import:
  - `PVForecastPVLib` — forecast from a pvlib system model and the EOS weather forecast, with
    module and inverter model databases (from pvlib and SAM) offered by new endpoints.
  - `PVForecastPVNode` — native 15-minute forecasts from the pvnode.com API.
  - `PVForecastForecastSolar` — forecasts from the free Forecast.Solar API.
  - `PVForecastSolcast` — forecasts from the Solcast rooftop-site API.
  - `PVForecastHomeAssistant` — reads a forecast from a Home Assistant entity attribute, as
    provided by common Home Assistant PV forecast integrations.
  - `PVForecastAdaptiveAkkudoktor` (opt-in) — learns per plane from measured PV power: daily
    energy and shape calibration, weather-model weighting and corrections relative to
    reference arrays, with the fitted parameters and errors in the status API.
- Optional local, calibratable forecasting within `PVForecastAkkudoktor`; the remote
  backend remains the default. It can model snow on the modules
  (`pvforecast.akkudoktor.snow_enabled`), corrected by the measured PV production.
- Measurement sample, energy, household-balance and battery-capacity APIs with data-quality
  and coverage information. A capacity estimate does not overwrite configured capacity.

#### Server and EOSdash

- Optional `general.timezone_override`: an IANA timezone name used as `general.timezone`
  instead of the lookup from latitude/longitude. Unset (default), the lookup is unchanged.
- `GET /v1/health` reports `started_at`, the start time of the server process, so a client
  notices a restart also in a container (where the pid is always 1).
- EOSdash can be reached through an external reverse proxy (`server.eosdash_public_url`).
- EOSdash **Plan** page: the energy management plan as a timeline per device, the plan table,
  and a reworked solution chart (KPI row, totals, units menu, value axes for the displayed
  columns); the page settings are kept in the configuration.
- EOSdash **Setup** page: a setup assistant explains the basic settings step by step,
  gives examples, reports what is still missing and shows the configuration cards of the
  current step: location, horizons and energy management, PV system, weather, prices and fees,
  load, devices, measurements, Home Assistant (what EOS reads and writes, with a template sensor
  and an automation as example), save the configuration.
- EOSdash **Profiles** page: lists the user and packaged battery and inverter profiles, edits
  them as JSON (preview without saving, save, save as, delete, revert to the packaged profile)
  and shows the limit maps as heat maps and the efficiency and derating curves.
- A guide for running EOS on small devices (memory and CPU settings).

### Changed / compatibility

- Device collections are maps keyed by device ID. Old list-based configuration migrates,
  but integrations using paths such as `devices.batteries.0` must use the actual device ID.
  GENETIC and GENETIC0 have separate algorithm settings.
- Battery and inverter settings migrate to the device profiles: `capacity_wh` becomes
  `profile_adjustment.nominal_capacity_wh`, the battery power limits and the full charge interval
  and threshold move into `profile_adjustment`, an inverter's `max_power_w` becomes
  `profile_adjustment.nominal_power_w` and `max_ac_charge_power_w` moves into
  `profile_adjustment`. The battery and inverter efficiencies (including
  `dc_to_ac_efficiency_curve`), `ac_charge_limits_total_charge` and `cell_chemistry` are dropped
  with an error log: the profile values apply. A generic profile without own power limits is
  limited to the former default of 5000 W. Home Assistant configuration paths follow.
- GENETIC0 no longer replaces configured devices by demo devices; a device that cannot be
  prepared is left out of the run.
- GENETIC rejects incomplete control forecasts instead of inventing usable inputs. Unsupported
  device counts and inconsistent battery links also fail explicitly. A missing, future, invalid
  or stale state of charge no longer cancels the run: an explicit SoC in the request takes
  precedence, otherwise the latest fresh measurement is used, otherwise 0 % is assumed for that
  battery or EV and logged. The measurement age limit (`measurement_max_age_seconds`) defaults to
  five minutes.
- Imported sale prices, including zero and negative prices, are preserved. Corrected
  battery/inverter limits, losses and wear costs can change the chosen plan and its costs.
- The electricity-fee framework replaces old `elecprice.charges_kwh` / `vat_rate` fields.
  Those old fields are dropped during migration: configure and verify fees explicitly.
- Without PV or price predictions, the GENETIC0 parameter preparation falls back to demo
  providers that need no internet service: `PVForecastPVLib` and `ElecPriceFixed`.
- The configuration is saved to the configuration file automatically by default.
- Quarter-hour clients must use the returned timestamps and interval, not assume 24 hourly
  values. Runtime forecast energy is Wh per slot and prices are currency per Wh.
- The optimization API uses English field names, e.g. `pv_battery` (`pv_akku`), `ev` (`eauto`),
  `ev_charge_hours_float` (`eautocharge_hours_float`). The German names are still accepted on
  input and returned as deprecated fields. The legacy endpoints `/strompreis`, `/gesamtlast` and
  `/gesamtlast_simple` are deprecated.
- Configuration source priority: command line, environment variables, dotenv, configuration
  file, defaults. Runtime configuration changes take priority over environment and file values
  for explicitly updated keys; command-line settings retain higher priority.
- Time windows: date, weekday and month restrictions apply to the start of a window, so an
  overnight window (e.g. Friday 22:00 to Saturday 06:00) belongs to the day it starts.
- The Docker image is built in stages with uv; EOSdash binds to `0.0.0.0` in the image, so the
  Home Assistant ingress reaches it.
- Lower idle memory: matplotlib (PDF reports), statsmodels (price fallback forecast), pvlib,
  bs4/lxml, scipy (self-consumption table), the GENETIC0 optimizer and the timezone lookup
  data are imported on first use instead of at server start. Results are unchanged.
- New setting `optimization.self_consumption_interpolator` (`scipy` by default, `numpy`).
  With `numpy` the self-consumption probability tables are evaluated without SciPy, so a
  GENETIC run no longer loads SciPy (about 35 MB less memory). Results are bit-identical to
  SciPy's Linux builds. The tables are read without SciPy in both cases.
- Faster GENETIC runs with unchanged plans (about 60 % less run time on a Raspberry Pi 4):
  table lookups, the appliance starts and the battery state decoding are worked out once per
  run instead of once per candidate, the slot simulation and the battery bookkeeping work on
  plain numbers instead of single NumPy elements, the terminal value report is built for the
  final solution only, offspring are created without `deepcopy`, and identical genomes of a
  generation are evaluated once also when the fitness cache is off.
- Data management (predictions, measurements, database) runs asynchronously.

### Fixed

- Failed optimization requests no longer return a previous successful result as if it
  belonged to the failed run; successful results and plans are published together.
- The optimization no longer fails after a server restart.
- The device IDs in the solution follow the configuration.
- Restored JSON measurements retain their timestamps and values.
- Configuration updates made at runtime, e.g. by `PUT /v1/config`, are no longer discarded when the
  same configuration key is set in the EOS configuration file or in the environment
  ([#1303](https://github.com/Akkudoktor-EOS/EOS/issues/1303)).
- Environment variables are applied to the configuration on server startup instead of taking effect
  only after the first configuration change, and are no longer overridden by default server
  settings.
- GENETIC reads its horizon, interval and prediction settings once at the start of a run. A
  configuration change during a run (for example a client adapting the control horizon) no
  longer fails the run with an out-of-bounds error; it applies to the next run.
- Updating a read-only computed configuration field, e.g. `adapter.homeassistant.eos_solution_entity_ids`
  via `PUT /v1/config/{path}`, now reports a read-only error instead of claiming the field does
  not exist ([#913](https://github.com/Akkudoktor-EOS/EOS/issues/913)).
- Configuration migration keeps map entries with `/` in their keys; prediction providers are
  validated before the prediction exists.
- Planning: the AC setpoint caps the total charge where the inverter does; EVs can finish
  charging within the final slot; full SoC targets are accepted; energy below the minimum state
  of charge is preserved; GENETIC battery discharge is limited by the discharge power.
- DST: GENETIC0 parameter arrays stay aligned with the start hour; time windows and appliance
  scheduling honor DST boundaries; Tibber prices with mixed UTC offsets around a DST change are
  read correctly; naive date/time strings are parsed in local time.
- Energy-Charts prices and feed-in tariffs honor the interval coverage and cadence of the source.
- Price interpolation, Akkudoktor API requests and Forecast.Solar power resampling.
- `PVForecastPVLib` applies the inverter AC limit (`inverter_paco`) and the system loss.
- DVhub feed-in tariff update failures are reported instead of being silent.
- A failed prediction update no longer crashes the server.
- Database: records persist and reload, compaction runs, `delete_by_datetime(None, None)` wipes
  all data, and NaN values are dropped when reading series.
- The file cache (`cache_in_file`) keys stay the same across restarts, so the cache keeps hitting.
- Parallel GENETIC evaluation workers stop reliably at the end of a run; fitness-cache memory is
  released after each run.
- Home Assistant adapter: config entities with the state `0` or `off` are applied, unknown
  configuration paths are reported instead of adding a device, legacy measurement keys are
  resolved, and device states are stamped with the run time.
- EOSdash: shows the Home Assistant add-on version, links the API docs with a reachable host,
  renders predictions without provider-specific load statistics, keeps the scroll position after
  actions and shows no horizontal scrollbar.
- Dropping root privileges no longer fails on non-Unix systems.

### Security

- Unexpected errors no longer expose exception details in API problem responses.
- A URL redirection from a remote source was removed (code scanning alert 6).

### Development

- Filename linter and coding guidelines for AI agents (`AGENTS.md`, `ai/skills/`), an AI
  development sandbox, and agent self-review with a CI check for agent-assisted pull requests.
- Browser tests of EOSdash in headless Chromium (`pytest --browser-test`).
- Tests and documentation generators no longer touch the developer's configuration files.
- Development setup with uv.

## 0.3.0 (2026-03-17)

Akkudoktor-EOS can now be run as Home Assistant add-on and standalone.
As Home Assistant add-on EOS uses ingress to fully integrate the EOSdash dashboard
in Home Assistant.

Adapters for Home Assistant and NodeRed integration are added. These adapters
provide a simplified interface to these HEMS besides the standard REST interface.

The prediction and measurement data can now be backed by a database. The database allows
to keep historic prediction data and measurement data for long time without keeping
it in memory. The database supports backend selection, compression, incremental data load,
automatic data saving to storage, automatic vacuum and compaction. Two database backends
are integrated and can be configured, LMDB and SQLight3.

New prediction providers allow to access OpenMeteo weather data and to define fixed
electricity prices for configurable time windows.

An anoying bug in the genetic algorithm that created unfeasable battery charge and
discharge amounts is now hopefully fixed.

In addition, bugs were fixed and new features were added.

### Feat

- add inverter AC/DC efficiency and break-even penalty
- add database support for measurements and historic prediction data.
  The prediction and measurement data can now be backed by a database. The database allows
  to keep historic prediction data and measurement data for long time without keeping
  it in memory. Two database backends are integrated and can be configured, LMDB and SQLight3.
- add adapters for integrations
  Adapters for Home Assistant and NodeRED integration are added.
  Akkudoktor-EOS can now be run as Home Assistant add-on and standalone.
  As Home Assistant add-on EOS uses ingress to fully integrate the EOSdash dashboard
  in Home Assistant.
- add make repeated task function
  make_repeated_task allows to wrap a function to be repeated cyclically.
- allow eos to be started with root permissions and drop priviledges
  Home assistant starts all add-ons with root permissions. Eos now drops
  root permissions if an applicable user is defined by paramter --run_as_user.
  The docker image defines the user eos to be used.
- make home assistant add-on run optimization by default
  When running as Home Assistant add-on the only viable usage is running with
  cyclic optimization. Make this the default to als propvide a better experience
  for first time users. The optimization will start with demo data, which also
  helps to configure Akkudoktor-EOS to the personal usage.
- make eos supervise and monitor EOSdash
  Eos now not only starts EOSdash but also monitors EOSdash during runtime
  and restarts EOSdash on fault. EOSdash logging is captured by EOS
  and forwarded to the EOS log to provide better visibility.
- add openmeteo weather provider
- add fixed electricity prediction with time window support
- add duration to string conversion
  Make to_duration to also return the duration as string on request.

### Fixed

- genetic optimizer charge rates and soc accuracy
- energy charts bidding zone in request
- prevent exception when load prediction data is missing
- eosdash startup
  Ensure that EOSdash is only started after EOS configuration is available.
- config eos test setup
  Make the config_eos fixture generate a new instance of the config_eos singleton.
  Use correct env names to setup data folder path.
- startup with no config
  Make cache and measurements complain about missing data path configuration but
  do not bail out.
- soc data preparation and usage for genetic optimization.
  Search for soc measurments 48 hours around the optimization start time.
  Only clamp soc to maximum in battery device simulation.
- dashboard bailout on zero value solution display
  Do not use zero values to calculate the chart values adjustment for display.
- openapi generation script
  Make the script also replace data_folder_path and data_output_path to hide
  real (test) environment pathes.
- development version scheme
  The development versioning scheme is adaptet to fit to docker and
  home assistant expectations. The new scheme is x.y.z and x.y.z.dev'date''hash'.
  Hash is only digits as expected by home assistant. Development version
  is appended by .dev as expected by docker.
- use mean value in interval on resampling for array
  When downsampling data use the mean value of all values within the new
  sampling interval.
- default battery ev soc and appliance wh
  Make the genetic simulation return default values for the
  battery SoC, electric vehicle SoC and appliance load if these
  assets are not used.
- import json string
  Strip outer quotes from JSON strings on import to be compliant to json.loads()
  expectation.
- default interval definition for import data
  Default interval must be defined in lowercase human definition to
  be accepted by pendulum.
- clearoutside schema change

### Chore

- removed index based data sequence access
  Index based data sequence access does not make sense as the sequence can be backed
  by the database. The sequence is now purely time series data.
- refactor eos startup to avoid module import startup
  Avoid module import initialisation expecially of the EOS configuration.
  Config mutation, singleton initialization, logging setup, argparse parsing,
  background task definitions depending on config and environment-dependent behavior
  is now done at function startup.
- introduce retention manager
  A single long-running background task that owns the scheduling of all periodic
  server-maintenance jobs (cache cleanup, DB autosave, …)
- guard against visualization errors in genetic optimization
- improve provider update error handling and add VRM provider settings validation
- canonicalize timezone name for UTC
  Timezone names that are semantically identical to UTC are canonicalized to UTC.
- extend config file migration for default value handling
- extend datetime util test cases
- make version test check for untracked files
  Check for files that are not tracked by git. Version calculation will be
  wrong if these files will not be commited.
- bump pandas to 3.0.0
  Pandas 3.0 now performs inference on the appropriate resolution (a.k.a. unit)
  for the output dtype which may become datetime64[us] (before it was ns). Also
  numeric dtype detection is now more strict which needs a different detection for
  numerics.
- bump pydantic-settings to 2.12.0
  pydantic-settings 2.12.0 under pytest creates a different behaviour. The tests
  were adapted and a workaround was introduced. Also ConfigEOS was adapted
  to allow for fine grain initialization control to be able to switch
  off certain settings such as file settings during test.
- remove sci learn kit from dependencies
  The sci learn kit is not strictly necessary as long as we have scipy.
- add documentation mode guarding for sphinx autosummary
  Sphinx autosummary excecutes functions. Prevent exceptions in case of pure doc
  mode.
- adapt docker-build CI workflow to stricter GitHub handling
- add CodeQL analysis workflow to CI
- Use info logging to report missing optimization parameters
  In parameter preparation for automatic optimization an error was logged for missing paramters.
  Log is now down using the info level.
- make EOSdash use the EOS data directory for file import/ export
  EOSdash use the EOS data directory for file import/ export by default.
  This allows to use the configuration import/ export function also
  within docker images.
- improve EOSdash config tab display
  Improve display of JSON code and add more forms for config value update.
- make docker image file system layout similar to home assistant
  Only use /data directory for persistent data. This is handled as a
  docker volume. The /data volume is mapped to ~/.local/share/net.akkudoktor.eos
  if using docker compose.
- add home assistant add-on development environment
  Add VSCode devcontainer and task definition for home assistant add-on
  development.
- Use uv to manage the virtual environment for development.
  This enormously increases dependency updates.
- improve documentation

## 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.