Planes sharing one inverter_paco feed a single physical inverter, but their AC output was summed without any cap, so a 15 kW inverter could report 21-25 kW. This PR groups the per-plane AC output by inverter_paco and clips each group's sum to its rating; planes without a rating are left alone. It also applies the configured system loss (PVGIS-style percent, same semantics as PVForecastAkkudoktorLocal) to DC and AC output instead of warning that it is unsupported.
PV arrays with different orientations and curtailment need separate plant calibration and weather correction. This opt-in provider publishes normal EOS AC/DC forecast keys at 15-minute resolution.
- Preserve daily per-plane energy/shape calibration and validated short-term local/trend selection. Curtailment-sensitive arrays require a fresh release flag or an explicitly configured learning guard.
- Add switchable empirical solar-angle response relative to separate eligible reference arrays, removing their common weather error. This learns angular residuals, not physical angles or a measured geometric horizon.
- Archive actually received weather model inputs, first local receipt and frozen per-member PV candidates. Returned past weather is excluded; unknown source issuance remains explicit. Learn model weights and forecast-regime bias separately at 2–6, 6–24 and 24–48 hours.
- New long-horizon candidates remain in shadow until their genuinely issued outputs improve equal-day MAE by the configured margin, win on at least 60% of validation days and pass a matched-support energy-bias guard. Insufficient evidence retains calibration. Daily yields still require complete local days, including DST.
- Expose fitted parameters, independent training/validation day counts, errors, selected methods and persistence in the existing status API, with additional normal prediction keys for Grafana. Retain physical DC through angular/weather corrections an enforce individual inverter AC limits.
Co-authored-by: Normann <github@koldrack.com>
Co-authored-by: Christin <chrissi@von-perbandt.de>
Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com>
Co-authored-by: YMT <yn276364@dal.ca>
Co-authored-by: sarankumar1325 <120080287+sarankumar1325@users.noreply.github.com>
Regression tests were scattered across feature- and bug-specific files, and some suites repeated the same checks. Group tests by responsibility, isolate feature fixtures in test classes, and remove duplicate or obsolete cases.
The review covered all 127 collected test modules on main. This reduces them and removes or combines definitions:.
No application code changes.
A GENETIC server with external (import) providers and EOSdash disabled
loads several large packages at start that it never uses, which costs
a noticeable part of the memory on 512 MB boards:
- matplotlib via the GENETIC/GENETIC0 PDF report modules (server/eos.py)
- statsmodels via the Holt-Winters fallback in prediction/priceabc.py
- pvlib via the weather base class and the pvlib-based providers
- bs4/lxml via the ClearOutside provider
- scipy via the self-consumption interpolator singletons, which were
loaded at import time
- the GENETIC0 optimizer, imported by core/ems.py
- tzfpy, whose polygon data the computed general.timezone loads
Import these where they are used. The interpolator tables are loaded
on first use (thread-safe). Nothing changes in what is computed.
general.timezone is computed from latitude/longitude and cannot be set.
Add general.timezone_override (IANA name, validated with pendulum):
when set, general.timezone returns it and the lookup is skipped. When
unset (default) the lookup is unchanged.
Tests that patched the moved names now patch them at their source.
A new test starts the server module in a subprocess and checks that
none of these modules is loaded.
Co-authored-by: Christin <christin@BigBrother.bikini.bottom.zone>
Co-authored-by: Claude Opus 5.5 <noreply@anthropic.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>
ChenYi-NnergyandGitHub
Bobby NoelteandGitHub