A single 503 from Open-Meteo answered /v1/prediction/update with 400 and left
every provider after PVForecastAkkudoktorLocal unrun: the provider raised on the
first failed request, and PredictionContainer.update_data re-raises whatever an
enabled provider raises. Open-Meteo returns 503 while it rotates its model runs
and 429 when the free tier is briefly saturated; both clear within seconds.
Retryable responses (429, 500, 502, 503, 504) and connection errors are now
retried three times with a growing pause, matching what the SMARD provider
already does. If the fetch still fails and the stored forecast reaches past the
run start, that forecast is kept for one more run rather than failing the update
- a forecast one run old beats no forecast at all. A cold start with nothing
stored still fails, because then there really is no PV forecast.
A battery or inverter failure limits PV to local demand for days. The
calibration read that as the plant's true output and learned it as a permanent
model loss, so one outage degraded the forecast long after the hardware was
fixed.
Calibration now estimates the healthy plant ratio over
`calibration_reference_days`, excludes days below `calibration_outage_threshold`
of it, and falls back to the most recent `calibration_min_healthy_days` when the
normal window is contaminated. `calibration_outage_filter_enabled` turns this
off for plants where measured curtailment, not available potential, is the
prediction target.
The fit also uses native 15-minute meter readings when every configured PV meter
supplies them - never interpolating hourly counters into an invented
quarter-hour profile - interpolates azimuth factors smoothly between bin centres
instead of stepping the EMS input curve, and normalizes the shape per forecast
day so it redistributes energy without changing that day's kWh correction. The
default azimuth bin widens from 15 to 45 degrees, which is what a typical
window actually supports.
Fixes the calibration window itself: it was derived from the measurement store
as a whole rather than from the configured PV production meters. A load meter
reaching further than the PV meter placed the window where no PV reading exists,
so calibration silently fell back to hourly fitting or skipped itself.
Also fixes `Measurement.load()`, which discarded every stored record. It
validated the file into a "temporary" Measurement, but Measurement is a
singleton, so that instance was the live one and the parsed records were
dropped.
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