Add new provider class for electricity fees providers.
Add the generic providers:
- ElecFeeFixed
- ElecFeeImport
The providers provide predictions for:
- elecfee_consumption_amt_wh:
Total fixed fee for consumed energy per Wh [amount/Wh]. This is the accumulation of all
fixed per-Wh fees payable on "consumed energy - such as network charge, concession fee,
and electricity charge - into a single amount.
- elecfee_consumption_percent_amt:
Total fixed surcharge on consumed energy, given as a percentage of the monetary amount
already charged for that energy [%]. This is the accumulation of all percentage-based
surcharges payable on top of the consumed-energy fee - such as VAT - into a single
percentage. This is a percentage of the fee amount, not a per-Wh rate.
- elecfee_feedin_amt_wh:
Total fixed deduction from feed-in energy per Wh [amount/Wh]. This is the accumulation of
all fixed per-Wh charges deducted from feed-in energy - such as metering fees or
grid-operator handling "charges - into a single amount. Applied after the percentage-based
deduction, i.e. it reduces the price by a flat amount per Wh rather than by a share of the
raw price.
- elecfee_feedin_percent_amt:
Total percentage deducted from the raw feed-in price (spot price) [%]. This is the
accumulation of all percentage-based deductions payable on the feed-in tariff - such as a
marketing or balancing fee retained by the aggregator - into a single percentage. It is
applied as `raw_price * (100 - percent) / 100`, i.e. it scales down the raw price rather
than adding a surcharge to it.
A new _apply_fee() method is added to the base class for ElecPrice and FeedInTariff to be used to
add the fees in a consistent way. Fees are taken from the active ElecFee provider and applied
to the raw prices given to the _apply_fee() method.
The optional application of fees is added to:
- ElecPriceAkkudoktor
- ElecPriceFixed
- ElecPriceEnergyCharts
- ElecPriceSMARD
- FeedInTariffEnergyCharts
- FeedInTariffFixed
- FeedInTariffSMARD
The import providers ElecPriceImport and FeedInTariffImport do not apply fees by intentention.
The following providers currently do not handle fees defined by ElecFee:
- ElecPriceTibber
- FeedInTariffAkkudoktor
- FeedInTariffDvhubOnline
- FeedInTariffTibber
The tests for this feature are either added or existing tests are extended.
The documentation was extended for the electricity fee provider settings.
Besides this feature further improvements are added:
* feat: add SMARD quarter-hour electricty price and feed-in tariff provider
* feat: to_series method for TimeWindows and ValueTimeWindows
Additional to to_array the time window sequence can now also produce a pandas series.
Test have been extended to cover the series generation.
* feat: use time windows in fixed feedin tariff provider
Feedin tariff can now be configured by time windows - not a single value.
* feat: EOSdash select for PVLib inverters and modules
Provide PVLib inverter and module names in config selection.
* feat: EOSdash lazy select for big option sets
Add a new form for lazy selection of big option sets. Filtering and
generation of the option set is done server-side.
* fix: use raw data for ETS/ median prediction
Use to raw time series data for ETS/ median prediction to avoid interference
by e.g. dynamic grid charges.
* fix: EOSdash config drops by type only on details resolve
Drop configuration by type and path. Prevents dropping of configuration items
with same type and level but different path.
* fix: EOSdash configuration section closes on update
Open section if searching or if last update touched this category — including
updates on deeply nested sub-fields.
* chore: make elecfeefixed, elecpricefixed and feedintarifffixed warn about no windows and default to 0
Missining configuration creates default 0 value and a warning instead of an exception.
* fix: test setup for providers
Reset db state on each test run.
* chore: improve config option naming for elecpricefixed.
* chore: adapt elecpricefixed test to changed time_windows naming
* chore: factorized common price provider helpers to priceabc.py
Factorized common price provider helpers to priceabc.py. Add tests for these helpers.
Reduce/ change testing of elecpriceabc.py and feedintariffabc.py to cover
only specifics. Rest of testing is already covered by test_priceabc.py.
* chore: update version
Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
### Significant changes / EOS impact
The dependency bumps are mostly patch/tooling updates, but a few are worth explicit review:
- **`pydantic-settings` 2.14.2 → 2.15.0 — highest runtime impact.** EOS directly subclasses `pydantic_settings.BaseSettings` for `SettingsEOS` / `ConfigEOS`, with `case_sensitive` left at its default. In 2.15.0, `case_sensitive` now also applies to init kwargs and config-file sources, so top-level setting keys are now matched case-insensitively by default where those sources previously did not behave that way. This can change how differently-cased config keys are accepted/resolved. The release also adds warnings for unresolved forward references and changes strict non-JSON env-value failures to `ValidationError`. **Recommended:** exercise JSON config loading, init/update paths, env overrides, and config round-trips.
- **`tzfpy` 1.3.2 → 1.3.3 — runtime correctness change.** EOS uses `tzfpy.get_tz()` in `to_timezone()` and exposes the result through `GeneralSettings.timezone`. Queries exactly on timezone polygon borders now resolve instead of returning an empty result, and the `America/Argentina/Ushuaia` boundary is corrected. This can intentionally change timezone output for users on/near affected boundaries.
- **`cachebox` 6.2.2 → 6.2.5 — runtime cache correctness/safety.** EOS uses `cachebox.LRUCache` and `cachebox.cached` for the energy-management cache. The update fixes iterator lifetime safety, LRU iterator invalidation when reads promote entries, and a potential `setdefault_with` locking issue. EOS does not appear to rely on those edge cases directly, so this is expected to be low-risk and mostly corrective; existing cache tests are the relevant regression coverage.
- **`GitPython` 3.1.58 → 3.1.59 — dev/tooling security hardening.** This release blocks file-reading Git options, separate git-directory use during clone, and hardens config parsing. It appears to be a dev/docs dependency rather than EOS runtime code, but CI/tooling that intentionally passes unusual Git options could be affected.
- **`pre-commit` 4.6.1 → 4.6.2 — dev-only bug fix.** Fixes a regression in Node-language hooks using npm 11.x build scripts.
- **`mypy` 2.3.1, `commitizen` 4.17.1, and `types-PyYaml` stub update** are tooling/type-checking changes with no expected EOS runtime behavior change.
Overall, the main compatibility focus should be **configuration handling (`pydantic-settings`)**, followed by **timezone edge cases (`tzfpy`)**. The remaining updates are primarily correctness, security, or developer-tooling fixes.
* initialize the temporary Git repository in test_workflow_git with --initial-branch=main
* avoid Git's default-branch advisory in pytest logs without changing global Git configuration or suppressing stderr
* upload generated optimization result artifacts only when the test job fails
* use the actual nested tests/testdata/**/new_optimize_result* paths
* ignore the no-files case so unrelated test failures do not produce an artifact warning
Consolidates the currently applicable dependency updates into one PR, including the closed Dependabot backlog such as #1241, plus dependency surfaces that were not covered by the repository's previous pip-only Dependabot configuration.
Cleanup of test warnings.
Most importent:
* GitPython + pypdf security hardening.
* Uvicorn WebSocket close/backpressure/header fixes for server/dashboard reliability.
* FastAPI dependency-memory/OpenAPI improvements for the API process.
* Bokeh WebSocket/resource-leak/prefix fixes for EOSdash and proxied deployments.
* cachebox cancellation/lock cleanup fixes for long-running/concurrent work.
* pandas 3.0.5 avoiding the yanked 3.0.4 datetime/segfault build.
* Ruff security-lint and pydocstyle correctness fixes, plus faster release builds via PGO.
* platformdirs malformed-XDG and duplicate-directory fixes for deployment portability.
* CI action modernization, regenerated uv.lock, and expanded Dependabot coverage.
Runtime dependencies
cachebox: 6.1.2 → 6.2.2
fastapi: 0.139.2 → 0.141.1
python-fasthtml: 0.14.9 → 0.14.11
MonsterUI: 1.0.46 → 1.0.47
bokeh: 3.9.1 → 3.9.2
uvicorn: 0.51.0 → 0.52.4 (build(deps): bump uvicorn from 0.51.0 to 0.52.3 #1241, refreshed to latest patch)
pandas: 3.0.3 → 3.0.5
platformdirs: 4.11.0 → 4.11.3
Development/test dependencies
pandas-stubs: 3.0.3.260530 → 3.0.5.260730
types-PyYAML: 6.0.12.20260518 → 6.0.12.20260724
GitPython: 3.1.53 → 3.1.58 (security/fix releases)
coverage: 7.15.2 → 7.15.4
pypdf: 6.14.2 → 6.16.1 (includes security fixes)
Pre-commit/tooling
ruff-pre-commit: v0.15.21 → v0.16.3
synchronize pandas-stubs, types-docutils, and types-PyYAML pins with pyproject.toml
CI / repository dependencies
Python 3.13.9 → 3.13.15 in CI, Docker, .env, and local Docker Make targets
actions/checkout → v7 in pytest, pre-commit, CodeQL, and release workflows
actions/setup-python → v7 in pytest, pre-commit, and release workflows
actions/upload-artifact → v7 in pytest workflow
actions/stale: v9.1.0 → v11.0.0 (SHA-pinned)
regenerate uv.lock from the final dependency pins so locked/frozen installs match pyproject.toml
Future update coverage
Expand Dependabot from pip-only to also monitor:
GitHub Actions
Docker
The existing open docutils 0.23 update (#1085) is intentionally excluded because it has separate compatibility/ignore handling and should remain isolated.
docker-build.yml was audited and is already using the newer action generations, so no changes were needed there.
---------
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
Change configuration source priorities to:
- cli
- environment vars
- dotenv settings
- config file settings
- init settings
By this the environment vars supersede any configuration var
provided by the configuration file or by the initialisation
with pydantic.
The test_config.py::test_computed_path was fixed to to not
use the defaul env var overwrite defined by conftest.py.
This seemed to indicate non working env vars, but in fact
was a test setupt fault.
Besides this fix there are other fixes and changes added:
* fix: exclude computed fields when merging settings
Pydantic may overwrite settings by values given for computed
fields and use these values instead of re-computing the field.
Avoid computed fields in merging settings.
* chore: improve Windows compatability of development setup
Improve scripts to better run also on Windows. When doing path
checks keep compatibility also to Windows pathes. A lot of
changes to avoid the famous Windows CRLF handling and keep
line endings to LF.
* chore: add development hint for Windows
Windows developers should set core.autocrlf to false.
* chore: update version
Signed-off-by: b0661 <b0661n0e17e@gmail.com>
Catch exceptions from prediction update and log as error message. Keep
executing the energy management run.
Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
Add a PV forecast provider that calculates the forecast using a PVLib system model
and weather forecast from the EOS weather forecast provider.
Additional module and inverter models can be easily added as the database
is build from PVLib and SAM databases and a bundled csv file.
The module model and inververt model names are provided by new endpoints
to be used in configuration.
The provider is based on the fantastic work of EMHASS. See
https://github.com/davidusb-geek/emhass/blob/master/src/emhass/forecast.py
A short description of the provider is added to the documentation.
Besides the new features there are the fixes and improvements:
* feat: improve EOSdash config page
* fix: kex_to_series for start_datetime
Make key_to_series always start the series at start_datetime.
* fix: default provider for GENETIC and GENETIC0 optimization
To make the default less dependent on internet servers (with API changes and
availability issues) the default for PVForecast is set to PVForecastPVLib
and for ElecPrice to ElecPriceFixed. The default weather provider is changed
to OpenMeteo.
* fix: EOSdash display resampled prediction values
Make EOSdash display resampled prediction values where resampling fits to
the prediction value type. Use bar width that fits to 15 minutes value samples.
* chore: add a UI hints system to EOSdash
The UI hints system eases the definition of forms for configuration items.
There are also forms for items in maps and lists. These forms allow to add and delete
items to/ from maps and lists. The forms ensure that all required fields of newly
added items are filled.
* chore: Create an enum for valid optimization algorithms
* chore. Make config also provide the available energy management modes.
Used for configuration hints.
* chore: Randomize default device id in configuration
Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
The dropna filter compared values against float("nan") using ==, which is
always False (NaN != NaN). As a result NaN values were never dropped when
dropna=True, letting them leak into key_to_series/key_to_array and downstream
resampling.
Use pd.isna() to detect NaN, matching the rest of the module. Add regression
tests that fail before and pass after the fix.
Co-authored-by: Cornelius Mund <cornim@users.noreply.github.com>
Co-authored-by: Normann <github@koldrack.com>
* fix(database): open backend so records actually persist and reload
Measurement and prediction records were never persisted to the
configured database backend (e.g. LMDB). They only survived while the
process was running; every restart lost the accumulated history. For
LoadAkkudoktorAdjusted this silently zeroed the measurement-based
adjustment (the adjusted load forecast collapsed onto the raw mean).
Root cause: `db_enabled` is defined as `database.is_open`, and every
database code path (initialization, load, save, insert) is guarded behind
`db_enabled`. Nothing ever opened the backend first -- the only lazy open
(via `_run_db`) was unreachable because those calls sit behind the same
guard. The backend therefore stayed closed, `db_enabled` stayed False,
and all writes fell through to the JSON file fallback, which only holds
the current in-memory snapshot and is not reloaded into the record store
on startup.
Fix: explicitly open the configured backend in `_db_ensure_initialized`
before the `db_enabled` gate, wrapped in try/except so a failure degrades
gracefully to file storage.
Verified via an A/B test (identical persisted config, push -> save ->
restart): without the fix the backend reports enabled=false and data is
lost on restart; with the fix the backend is enabled, records persist to
LMDB, and reload correctly after restart.
* fix(database): skip disabled providers and avoid open retry loop
Address review feedback:
- Skip opening the backend for disabled providers (None/"NoDB"), whose
is_open is always False and would otherwise be reopened on every call.
- Add a one-shot _db_open_attempted flag so a closed/failed backend is not
retried (and re-logged) on every record operation.
- Add tests covering that NoDB never calls open() and that an unavailable
backend does not raise per operation and is opened at most once.
* fix(database): drop file-storage-fallback wording from open failure log
---------
Co-authored-by: Cornelius Mund <cornim@users.noreply.github.com>
Change PDF visualization to be created on demand and per optimization algorithm. The PDF
for the GENETIC0 optimization is provided by the /visualization_results.pdf endpoint.
There is no change in the interface.
By this the optimization algorithm is offloaded from the PDF generation which spares some
time.
To cope with several users may call the /visualization_results.pdf endpoint at the same
time the PDF is generated on the fly without any intermediate file taking the stored
GENETIC0 solution as an input. SVG picture generation is removed as this would again
create intermediate files. Chart pictures can easily be taken from the PDF.
To allow on demand creation of the optimization results visualization the optimisation
solution stored is extended by several new attributes. To keep the deprecated
/optimize endpoint compatible the optimization solution is stripped to the legacy
content before returned. Due to the extension of the solution the optimization tests were
adapted to cover the extended content.
The optimization tests are adapted to test the generated visualization report by
the pypdf reader. Pypdf is added to the development dependencies.
Besides the adaptation several fixes and improvements are added:
* feat: extend /v1/prediction/series endpoint by resampling and filling
Add parameters for resampling and filling. Add the processing parameter
to control wether raw data or resampled data shall be returned.
* feat: extend /v1/measurement/series endpoint by resampling and filling
Add parameters for resampling and filling: Add the processing parameter
to control wether raw data or resampled data shall be returned.
* feat: standardize and improve API error response
Use FASTApi exception handlers to provide a standardized API exception handling.
All exceptions are logged.
Exception traces are only returned if the new logging configuration parameter
logging.api_logging_level is set to "DEBUG" or "TRACE". Avoids unwanted leackage
of server internals on exceptions.
* fix: align to intervall when resampling
Ensure resampling is aligned to interval also when the buckets are shifted due to the
align_to_intervall parameter is set.
* chore: make dropna mandatory and default to True
* chore: refactor key_to_xxx data management methods
Make key_to_series the central method for data resampling and fill.
Add a new key_to_raw_series to retrieve the data as it is stored
(without resampling and filling).
Users of key_to_series were mostly moved to key_to_raw_series as this resembles
the former interface. Especially in predictions and tests this was done.
* chore: create test data sub-directory for each optimization algorithm
To prevent cluttering the test data directory and ease test data management for
optimization algorithms each algorithm got it's own sub-directory. The current
test data was moved to these sub-directories.
* chore: update version
Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
Ensure the deprecated endpoints get /strompreis, post /gesamtlast, get /gesamtlast_simple,
get /pvforecast to work on 1-hour intervalls even if the prediction provides 15-minutes
intervall data. This keeps the interface compliant to the legacy functionality.
Besides this adaptations several other improvements and fixes are included in this PR.
* feat: extend data management method key_to array by resample_method
One can now define the resample method on how to aggregate the values in an interval
for resampling. Three methods are provided:
- "first": Use the first value in each interval.
- "mean": Compute the arithmetic mean of all samples in each interval.
- "interval_mean": Compute the time-weighted mean assuming each value remains valid
until the next timestamp (piecewise-constant signal).
* feat: extend the get /v1/prediction/dataframe endpoint with resampling parameters
Make all parameters for resampling available at the endpoint.
* feat: extend the get /v1/prediction/list endpoint with resampling parameters
Make all parameters for resampling available at the endpoint.
* feat: add new delete /v1/prediction/range endpoint
The endpoint allows to delete prediction values for a given time span.
* fix: adapt for PVForecastAkkudoktor server side cache handling
/api.akkudoktor/forecast does it's own caching on requests. Call it with slightly
randomized request values to avoid getting cached values in the case we need fresh
data. The requests are anyway rate limited to one request per hour on our side.
* chore: add core.types
This module centralizes reusable type definitions shared across multiple
packages. Defining common types here avoids duplication of complex type
annotations (such as Literal aliases), ensures consistent typing across the
code base, and helps prevent circular import dependencies between modules.
* chore: extend cache testing
* chore: add system test for deprecated /strompreis endpoint
* chore: add unit test module for server endpoints
Add a new test module to do unit tests on server endpoints. First test added
for deprecated get /strompreis endpoint.
Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
Fetches GET /api/prices?start&end&zone (15-min slots, EUR/MWh) and stores
the raw market price as feed_in_tariff_wh (EUR/Wh) — no import charges/VAT.
Slots beyond the day-ahead horizon are left to the consumer's forward-fill
(FeedInTariffImport behaviour).
5 unit tests + opt-in live smoke (EOS_DVHUB_ONLINE_LIVE=1, passing).
Signed-off-by: Christin <info@bikinibottom.capital>
Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
Skip root privileges drop on non unix systems. It is
just not supported.
Signed-off-by: Andreas Schmitz <akkudoktor.net>
Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
Andreas will update the genetic algorithm for 15-minutes optimization
intervals.
Copy the current GENETIC optimization algorithm to GENETIC0 to enable
to keep the algorithm with the current functionality. Also copy resources
like the load interpolator to the GENETIC0 algorithm to keep them despite
possible later changes to the interpolator.
Make the deprecated legacy /optimize endpoint use the GENETIC0 optimization
algorithm to in-fact behave the same way even if there will later be changes
to the GENETIC algorithm by Andreas. Add a new REST endpoint to provide
the unprocessed optimisation results of the GENETIC and GENETIC0 algorithm
in case one wants to use them as done with the deprecated /optimize endpoint.
Adapt the optimization configuration to have distinct configurations for the
GENETIC and the GENETIC0 algorithm.
Create a copy of the current tests for the GENETIC algorithm to be used
for the GENETIC0 algorithm. This avoids the tests for the GENETIC0
algorithm to be influenced by later changes by Andreas.
Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
The `FeedInTariffTibber` provider requests `priceInfo` and `priceInfoRange` with
`resolution: QUARTER_HOURLY` and preserves the native 15-minute timestamps. It uses Tibber's
`energy` spot-price component without the `tax` part or EOS electricity-price charges. The
end-customer `total` component is deliberately ignored.
The provider deliberately rejects hourly API responses instead of silently repeating them. It
reuses `elecprice.tibber.access_token` and `elecprice.tibber.home_id`, so no duplicate credentials
are needed.
Signed-off-by: Andreas Schmitz <akkudoktor.net>
Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
The `FeedInTariffAkkudoktor` provider uses raw day-ahead market prices from
`https://api.akkudoktor.net/prices` as `feed_in_tariff_wh`. It does not add electricity import
charges or VAT. Published prices are extended to the configured prediction horizon with the same
seasonal ETS or median fallback used by the Akkudoktor electricity-price provider.
The Akkudoktor endpoint currently forwards hourly market prices from aWATTar. With a 15-minute
optimization interval, EOS holds each hourly price constant for its four quarter-hour slots. This
keeps the slot grid consistent but does not create genuine quarter-hour market prices.
Signed-off-by: Andreas Schmitz <akkudoktor.net>
Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
The `FeedInTariffEnergyCharts` provider uses the raw Energy-Charts day-ahead market price as the
feed-in tariff. It stores prices in `feed_in_tariff_wh` without adding electricity import charges
or VAT. The data is loaded from the Energy-Charts `/price` endpoint for the configured bidding
zone. The native Energy-Charts resolution, including quarter-hour data, is retained.
Energy-Charts usually supplies prices only for the published day-ahead period. If that data does
not cover the complete configured prediction horizon, the provider extends it as follows:
- With more than 800 hours of history, an ETS (Holt-Winters exponential smoothing) forecast with
weekly seasonality is used.
- With more than 168 hours of history, an ETS forecast with daily seasonality is used.
- With less history, the median of the available values is used as a constant fallback.
The seasonal periods are adjusted to the source resolution. For example, quarter-hour data uses
four values per hour. Values already supplied by Energy-Charts are kept unchanged; only missing
future slots after the last published price are forecast. Consequently, a 15-minute optimization
uses four forecast values per hour without converting them to hourly averages.
Signed-off-by: Andreas Schmitz <akkudoktor.net>
Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
When running as a Home Assistant add-on the ports shall not be changed
as this would break config.yaml that is used by Home Assistant. Prevent
the change and return an error message.
Extra fixes:
* fix: EOS configuration initialisation by cli under EOSdash.
Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
Fix test warnings left over from data management conversion to async design.
Mostly tagging async tests.
Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
Complete the English API translation started in #675:
- rename input fields pv_akku to pv_battery and eauto to ev, and the
solution fields eautocharge_hours_float to ev_charge_hours_float and
eauto_obj to ev_obj; the German names are still accepted on input
via validation aliases and re-emitted in responses as deprecated
computed fields, same pattern as #675
- mark the legacy endpoints /strompreis, /gesamtlast and
/gesamtlast_simple as deprecated in the OpenAPI schema
- use amount instead of a euro reference in the battery LCOS log
message
Co-authored-by: Tobias Welz <tobias.wizneteu@gmail.com>
The akkudoktor api for PV forecast seems to be changed and does not support to
concatenate several planes into one request anymore.
Make a request for every plane and add up the power results. Do not use the
new 15 minutes slots as the returned data does not include the hourly values
for windspeed and temperature as does the hourly slots.
Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
Configuration for some providers was given in the sub-field `provider_settings`
combining the settings of several providers. Pydantic does not understand
this very well and the configuration became cumbersome, especially in EOSdash.
All provider settings from the `provider-settings` sub-field are now lifted
to the top level field of the configuration.
The changes are automatically migrated in the configuration.
Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
These fixes were taken from https://github.com/arneman/EOS/tree/refactor/economic-objective.
1. Fix compaction job registration bug in eos.py:
- compact_eos_database was registered with save_eos_database function
- Now correctly calls compact_eos_database()
- Root cause: compaction/vacuum never ran, allowing records to grow unbounded
2. Optimize db_iterate_records() from O(n) to O(log n):
- Previous: linear scan from index 0 for every key_to_array() call
- Now: uses bisect_left on _db_sorted_timestamps to skip to start position
- Critical for large datasets: 9400 measurement records → 94s/call overhead → 5 calls/optimization
3. Reduce compaction_interval_sec default from 604800s (7 days) to 3600s (1 hour):
- With 7-day interval: 65000 records accumulate before first cleanup
- Bridge pushes ~1 record/9s (grid_export_emr)
- Hourly compaction + 2h data window → stable ~950 records
Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
GeneticOptimizationParameters.prepare() calls .to_list() on the result of
prediction.key_to_array(), which returns a numpy ndarray. Numpy arrays only
provide .tolist(), so every call raised AttributeError, was swallowed by the
bare except and logged as a misleading 'No ... data available' message. As a
consequence every automatic energy management run was canceled with 'Could
not prepare optimisation parameters'.
Regression introduced in a0febef (#1155).
Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
Co-authored-by: Bobby Noelte <b0661n0e17e@gmail.com>
Bare `except:` catches SystemExit and KeyboardInterrupt, which can:
- Mask Ctrl+C handling during long optimization runs
- Hide critical system signals
- Make debugging harder
Replacement by `except Exception:` preserves the same error handling while respecting
system-level exceptions.
Co-authored-by: Milo @KeloYuan
Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
Rename the German field names of the genetic optimization API to
English with full backward compatibility:
- English names are canonical and documented in the OpenAPI schema;
the German names are still accepted on input via validation aliases
and re-emitted in responses as deprecated computed fields
- fix visualization receiving the raw German-keyed simulation dict
(KeyError: load_wh_per_hour)
- rename internal German identifiers (optimize_ems, total_balance,
battery_residual_value, self_consumption, extra_data keys)
- use amount instead of currency/euro in chart labels and schema
descriptions; document currency handling (whole currency units,
never cents) in the API description
- regenerate openapi.json and generated docs
Co-authored-by: Tobias Welz <tobias.wizneteu@gmail.com>
Use forward fill to interpolate time series data that represents prices:
- elecprice_marketprice_wh
- feed_in_tariff_wh
Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
This PR adds three native PV power forecast providers, giving operators more
cloud forecast sources to choose from via pvforecast.provider, alongside the
existing PVForecastAkkudoktor, PVForecastVrm and PVForecastImport:
PVForecastPVNode — native 15-minute forecasts from the pvnode.com
V2 API. Saved-site mode (GET /v2/forecast/{site_id}) where the operator enters their API key +
site id, or inline mode (POST /v2/forecast/inline) using the configured planes.
PVForecastForecastSolar — the free Forecast.Solar API (no key
required for the public endpoint). Multi-plane systems issue one request per plane and the
instantaneous powers are summed per timestamp.
PVForecastSolcast — the Solcast rooftop-site API (API key + resource id).
Implementation notes
All three populate the existing pvforecast_ac_power (and mirror pvforecast_dc_power)
prediction keys, so they slot into the optimizer unchanged.
Timezone handling: pvnode and Forecast.Solar return site-local wall-clock timestamps with an
IANA timezone field, which are resolved to absolute instants before resampling; Solcast returns
UTC period_end and is normalised to the period start.
Each provider follows the existing provider pattern (provider_id(), _request_forecast() with
@cache_in_file, _update_data()), is registered in pvforecast.py and prediction.py, and is
upstream-neutral.
Validation
27 unit tests (timezone resolution, null/zero handling, kW→W and period-start conversion,
azimuth conversion, multi-plane summation, request URL/auth, HTTP-error handling).
ruff check (F/D/S/bandit) and ruff format clean.
Each provider was additionally validated against its live API with a real plant, confirming
the response shapes (pvnode: 288 native 15-min slots; Forecast.Solar: instantaneous watts;
Solcast: kW estimates with period_end/PT30M).
Documentation
Provider descriptions and configuration examples added to docs/akkudoktoreos/prediction.md.
CHANGELOG.md entry under Unreleased.
Regenerated docs/_generated/configpvforecast.md and configexample.md.
Notes for reviewers
Forecast.Solar's free endpoint is rate-limited (12 req/hour) and Solcast's free tier limits daily
calls; both providers rely on the standard 1-hour cache_in_file TTL to stay within budget.
Authors:
The code is created by Christin. Only minor adaptions by Bobby.
Signed-off-by: Christin <info@bikinibottom.capital>
Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
Co-authored-by: Christin <info@bikinibottom.capital>
Bobby NoelteandGitHub
NormannandGitHub
