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Andreas
f976335122
Add PVForecastAkkudoktorLocal, which runs the modelling chain inside EOS on raw Open-Meteo irradiance instead of calling a forecast service: solar position, horizon shading, plane transposition, incidence-angle modifier, cell temperature, PVWatts DC and inverter AC. It needs no API key and serves up to 16 days at 15-minute resolution from a single hourly request, which is what keeps `optimization.tail_horizon_hours` fed - services wrapping Open-Meteo cut the horizon much shorter. Several Open-Meteo models can be listed in `weather_models` and are averaged per variable at no extra request cost. With `calibration_enabled` the provider fits itself against `measurement.pv_production_emr_keys` over the past `calibration_days`: a global scale factor plus optional per-solar-azimuth factors, each weighted by modelled energy, shrunk toward the global factor by `calibration_prior_kwh` and clamped to `[calibration_min_factor, calibration_max_factor]`. The comparison runs on past intervals, where Open-Meteo serves analysed rather than forecast weather, so it corrects the error of the PV model and not that of the weather forecast. Calibration is a scale factor on the output and never touches `userhorizon`, `surface_tilt`, `surface_azimuth` or `peakpower`. The docs say so, and say why a short window and a plant fault inside it are the two ways to end up with a misleading factor. Also add `Measurement.pv_production_total_kwh()` alongside the existing load total, and `scripts/pvforecast_backtest.py`, which scores configuration variants against the stored meter readings without waiting for new forecasts to come true.