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.
Andreas
Bobby NoelteandGitHub