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fix(pvforecast): normalize Forecast.Solar power before resampling (#1487)
Forecast.Solar supplies instantaneous power with irregular sunrise/sunset timestamps. GENETIC interprets stored values as interval averages and infers cadence from the shortest gap, so these samples create artificial coverage gaps. The prediction list API also averages away the sunset zero before interpolating, producing positive nighttime PV. Integrate each roof plane's piecewise-linear power curve into UTC-contiguous 15-minute interval-average W before summing planes. Both hourly and quarter-hour GENETIC slots then consume the same normalized data as the prediction API, with exactly one downstream W-to-Wh conversion. Explicit sunrise/sunset zero boundaries establish the adjacent night within each forecast day. Daylight gaps longer than an hour, invalid power, absent planes and unavailable forecast days remain missing; the strict GENETIC coverage check stays in place. Refreshes replace the current forecast and remove stale tails. Because cadence detection also sees historical records, the first refresh clears legacy forecasts containing non-aligned timestamps, including their history. Later refreshes retain normalized history. This upgrade behavior is documented. Forecast validation errors now identify the first invalid interval and the number of invalid slots.
This commit is contained in:
Normann
@@ -224,8 +224,15 @@ class ConfigOptimizationRequest(
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("prices", prices),
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("feed-in tariff", tariffs),
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):
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if not all(math.isfinite(value) for value in values[first:last]):
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raise ValueError(f"Missing or invalid {key} within the control horizon.")
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missing = [index for index in range(first, last) if not math.isfinite(values[index])]
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if missing:
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left = origin.add(seconds=missing[0] * settings.interval_sec)
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right = left.add(seconds=settings.interval_sec)
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raise ValueError(
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f"Missing or invalid {key} within the control horizon. "
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f"First invalid interval: [{left.isoformat()}, {right.isoformat()}); "
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f"{len(missing)} invalid interval(s)."
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)
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if any(value < 0 for values in (pv, load) for value in values[first:last]):
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raise ValueError("PV and load energy must be nonnegative.")
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previous = ems.genetic_solution()
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@@ -6,10 +6,10 @@ request covers a single plane:
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GET https://api.forecast.solar[/{api_key}]/estimate/{lat}/{lon}/{dec}/{az}/{kwp}
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``result.watts`` is the instantaneous AC power per timestamp — exactly what the
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optimizer consumes as ``pvforecast_ac_power``. EOS plants with several roof
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planes issue one request per plane and the instantaneous powers are summed per
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timestamp.
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``result.watts`` contains instantaneous AC power, including non-aligned sunrise
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and sunset samples. Integrate each plane's piecewise-linear power curve into
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15-minute interval averages before summing planes. This is the interval-start
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power representation consumed by EOS; conversion to slot Wh happens downstream.
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Note on conventions:
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- Forecast.Solar azimuth is -180=N, -90=E, 0=S, 90=W, whereas EOS
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@@ -19,6 +19,7 @@ Note on conventions:
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to resolve them to absolute instants before EOS resamples them.
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"""
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import math
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import re
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from typing import Any, Optional
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@@ -30,11 +31,12 @@ from pydantic import Field
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from akkudoktoreos.config.configabc import SettingsBaseModel
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from akkudoktoreos.core.cache import cache_in_file
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from akkudoktoreos.prediction.pvforecastabc import PVForecastProvider
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from akkudoktoreos.utils.datetimeutil import to_datetime
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from akkudoktoreos.utils.datetimeutil import DateTime, to_datetime
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FORECAST_SOLAR_BASE = "https://api.forecast.solar"
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_TZ_SUFFIX = re.compile(r"([zZ]|[+-]\d\d:?\d\d)$")
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_INTERVAL_SECONDS = 900
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class PVForecastForecastSolarCommonSettings(SettingsBaseModel):
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@@ -65,7 +67,7 @@ class PVForecastForecastSolar(PVForecastProvider):
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settings = self.config.pvforecast.forecastsolar
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return settings.api_key if settings is not None else None
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def _to_utc_datetime(self, local_ts: Any, iana_tz: Optional[str]) -> Any:
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def _to_utc_datetime(self, local_ts: Any, iana_tz: Optional[str]) -> DateTime:
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"""Resolve a Forecast.Solar wall-clock timestamp to a timezone-aware datetime."""
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s = str(local_ts).strip()
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if _TZ_SUFFIX.search(s):
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@@ -76,6 +78,63 @@ class PVForecastForecastSolar(PVForecastProvider):
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raise ValueError(f"Expected a datetime, got {local_ts!r}")
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return to_datetime(dt.isoformat())
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def _interval_power(self, watts: dict[str, Any], timezone: Optional[str]) -> dict[int, float]:
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"""Integrate instantaneous samples without bridging missing daylight data.
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Forecast.Solar supplies at most hourly daylight samples. A larger gap,
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invalid power, or an unobserved day remains missing. Only an explicit
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zero at the first/last sample of a day establishes the adjacent night,
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up to that local day's midnight. Never extend into an unforecast day.
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UTC seconds preserve energy and both occurrences of a DST fall-back hour.
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"""
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tz = timezone or str(self.config.general.timezone)
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days: dict[DateTime, dict[int, float]] = {}
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for ts, power in watts.items():
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try:
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date = self._to_utc_datetime(ts, timezone).in_timezone(tz)
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except (ValueError, TypeError) as exc:
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logger.warning(f"Forecast.Solar: skipping unparseable timestamp {ts!r}: {exc}")
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continue
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try:
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value = float(power)
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except (ValueError, TypeError):
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value = math.nan
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if not math.isfinite(value) or value < 0:
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value = math.nan
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days.setdefault(date.start_of("day"), {})[int(date.timestamp())] = value
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averages: dict[int, float] = {}
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for day, samples in sorted(days.items()):
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start = int(day.timestamp())
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end = int(day.add(days=1).timestamp())
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points = sorted(samples.items())
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segments: list[tuple[int, int, float, float]] = []
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if len(points) >= 2:
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if points[0][1] == 0:
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segments.append((start, points[0][0], 0.0, 0.0))
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for (left, p_left), (right, p_right) in zip(points, points[1:]):
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if right - left <= 3600 and math.isfinite(p_left) and math.isfinite(p_right):
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segments.append((left, right, p_left, p_right))
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if points[-1][1] == 0:
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segments.append((points[-1][0], end, 0.0, 0.0))
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for slot in range(start, end, _INTERVAL_SECONDS):
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energy = 0.0
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covered = 0
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for left, right, p_left, p_right in segments:
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a = max(slot, left)
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b = min(slot + _INTERVAL_SECONDS, right)
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if b <= a:
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continue
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slope = (p_right - p_left) / (right - left)
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# Exact trapezoidal integral of this linear segment, in W s.
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energy += (p_left + slope * ((a + b) / 2 - left)) * (b - a)
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covered += b - a
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averages[slot] = (
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energy / _INTERVAL_SECONDS if covered == _INTERVAL_SECONDS else math.nan
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)
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return averages
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def _plane_url(self, plane: Any) -> str:
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"""Build the single-plane estimate URL for the given plane configuration."""
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latitude = self.config.general.latitude
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@@ -105,8 +164,7 @@ class PVForecastForecastSolar(PVForecastProvider):
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if not planes:
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raise ValueError("PVForecastForecastSolar needs at least one pvforecast.planes entry")
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summed: dict[str, float] = {}
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timezone: Optional[str] = None
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plane_power: list[dict[int, float]] = []
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for plane in planes:
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url = self._plane_url(plane)
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logger.debug(f"Requesting Forecast.Solar estimate: {url}")
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@@ -117,17 +175,22 @@ class PVForecastForecastSolar(PVForecastProvider):
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logger.error(f"Failed to fetch pvforecast from Forecast.Solar: {e}")
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raise RuntimeError("Failed to fetch pvforecast from Forecast.Solar API") from e
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data = response.json()
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if timezone is None:
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timezone = (data.get("message", {}).get("info", {}) or {}).get("timezone")
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timezone = (data.get("message", {}).get("info", {}) or {}).get("timezone")
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watts = (data.get("result", {}) or {}).get("watts", {}) or {}
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for ts, power in watts.items():
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try:
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summed[ts] = summed.get(ts, 0.0) + float(power)
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except (TypeError, ValueError):
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continue
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plane_power.append(self._interval_power(watts, timezone))
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# Align before adding: planes can have different sunrise/sunset timestamps.
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# Missing coverage on any plane must not become a valid partial plant forecast.
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stamps = sorted({stamp for power in plane_power for stamp in power})
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summed = {
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to_datetime(stamp, in_timezone="UTC").isoformat(): sum(
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power.get(stamp, math.nan) for power in plane_power
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)
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for stamp in stamps
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}
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self.update_datetime = to_datetime(in_timezone=self.config.general.timezone)
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return {"timezone": timezone, "watts": summed}
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return {"timezone": "UTC", "watts": summed}
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async def _update_data(self, force_update: Optional[bool] = False) -> None:
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"""Update forecast data in the PVForecastDataRecord format."""
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@@ -139,6 +202,16 @@ class PVForecastForecastSolar(PVForecastProvider):
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timezone = body.get("timezone")
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watts = body.get("watts", {})
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if watts:
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# Replace the refreshed forecast, including old irregular timestamps
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# and stale tails. Cadence inference also sees historical rows, so
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# invalidate legacy non-aligned forecasts once on upgrade. Subsequent
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# refreshes preserve the normalized forecast history.
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stored = await self.key_to_raw_series("pvforecast_ac_power", dropna=False)
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legacy = any(stamp.timestamp() % _INTERVAL_SECONDS for stamp in stored.index)
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start = min(self._to_utc_datetime(ts, timezone) for ts in watts)
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await self.delete_by_datetime(start_datetime=None if legacy else start)
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count = 0
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for ts, power_w in sorted(watts.items()):
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try:
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@@ -146,7 +219,7 @@ class PVForecastForecastSolar(PVForecastProvider):
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except Exception as e: # noqa: BLE001 - skip unparseable rows
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logger.warning(f"Forecast.Solar: skipping unparseable timestamp {ts!r}: {e}")
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continue
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value = round(float(power_w), 1)
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value = float(power_w)
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await self.update_value(
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date,
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{"pvforecast_ac_power": value, "pvforecast_dc_power": value},
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