Add new daemons and debug scripts for Sigenergy and Oracle functionalities
- Implement `sigen_daemon.py` to poll Sigenergy plant metrics and store snapshots. - Create `web_daemon.py` for serving a web interface with various endpoints. - Add debug scripts: - `debug_duplicates.py` to find duplicate target times in forecast data. - `debug_energy_forecast.py` to print baseline energy forecast curves. - `debug_oracle_evaluations.py` to run the oracle evaluator. - `debug_sigen.py` to inspect stored Sigenergy plant snapshots. - `debug_weather.py` to trace resolved truth data. - `modbus_test.py` for exploring Sigenergy plants or inverters over Modbus TCP. - Introduce `oracle_evaluator.py` for evaluating stored oracle predictions against actuals. - Add TCN training scripts in `tcn` directory for training usage sequence models.
This commit is contained in:
rpotter6298
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from __future__ import annotations
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from datetime import datetime, timedelta, timezone
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from zoneinfo import ZoneInfo, ZoneInfoNotFoundError
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from gibil.classes.models import ForecastKind, PowerForecastPoint, PowerForecastRun
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from gibil.classes.oracle.config import EnergyForecastConfig
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from gibil.classes.sigen.store import SigenStore
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class HistoricalUsageOracle:
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"""Forecasts load from time-of-week history blended with recent load."""
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model_version = "historical_usage_profile_v1"
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def __init__(
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self,
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sigen_store: SigenStore,
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config: EnergyForecastConfig,
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) -> None:
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self.sigen_store = sigen_store
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self.config = config
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def forecast(
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self,
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target_times: list[datetime],
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issued_at: datetime | None = None,
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) -> PowerForecastRun:
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if issued_at is None:
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issued_at = datetime.now(timezone.utc)
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recent_summary = self.sigen_store.load_recent_power_summary(
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lookback=timedelta(minutes=self.config.load_lookback_minutes)
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)
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profile = self.sigen_store.load_load_profile(
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lookback=timedelta(days=self.config.load_profile_days),
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bucket_minutes=self.config.load_profile_bucket_minutes,
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min_samples=self.config.load_profile_min_samples,
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timezone_name=self._local_timezone_name(),
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)
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latest = self.sigen_store.load_latest_snapshot()
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fallback_load_w = latest.load_power_w if latest else 0.0
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recent_p50 = self._number(recent_summary.get("load_p50_w"), fallback_load_w)
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recent_p10 = self._number(recent_summary.get("load_p10_w"), recent_p50 * 0.7)
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recent_p90 = self._number(recent_summary.get("load_p90_w"), recent_p50 * 1.5)
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blend = min(max(self.config.load_recent_blend, 0.0), 1.0)
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points = [
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self._forecast_point(
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target_at=target_at,
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issued_at=issued_at,
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profile=profile,
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recent_p10=recent_p10,
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recent_p50=recent_p50,
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recent_p90=recent_p90,
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blend=blend,
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)
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for target_at in target_times
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]
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return PowerForecastRun(
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issued_at=issued_at,
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kind=ForecastKind.LOAD,
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source="historical_usage_oracle",
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model_version=self.model_version,
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points=points,
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)
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def _forecast_point(
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self,
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target_at: datetime,
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issued_at: datetime,
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profile: dict[tuple[int, int], dict[str, float | int]],
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recent_p10: float,
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recent_p50: float,
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recent_p90: float,
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blend: float,
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) -> PowerForecastPoint:
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profile_key = self._profile_key(target_at)
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profile_values = profile.get(profile_key)
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if profile_values is None:
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p10 = max(0.0, recent_p10)
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p50 = max(0.0, recent_p50)
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p90 = max(p50 * 1.25, recent_p90)
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confidence = 0.25
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sample_count = 0
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else:
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p10 = self._blend(float(profile_values["p10"]), recent_p10, blend)
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p50 = self._blend(float(profile_values["p50"]), recent_p50, blend)
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p90 = self._blend(float(profile_values["p90"]), recent_p90, blend)
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p10 = max(0.0, min(p10, p50))
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p90 = max(p90, p50 * 1.15)
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confidence = min(0.65, 0.35 + float(profile_values["sample_count"]) / 500.0)
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sample_count = int(profile_values["sample_count"])
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return PowerForecastPoint(
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target_at=target_at,
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horizon_minutes=max(
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0, round((target_at - issued_at).total_seconds() / 60)
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),
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expected_power_w=p50,
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p10_power_w=p10,
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p50_power_w=p50,
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p90_power_w=p90,
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confidence=confidence,
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source="time_of_week_load_profile",
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model_version=self.model_version,
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metadata={
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"profile_key": profile_key,
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"profile_sample_count": sample_count,
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"recent_blend": blend,
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"lookback_days": self.config.load_profile_days,
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"bucket_minutes": self.config.load_profile_bucket_minutes,
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},
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)
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def _profile_key(self, target_at: datetime) -> tuple[int, int]:
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local = target_at.astimezone(self._local_timezone())
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minute_of_day = local.hour * 60 + local.minute
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bucket = (
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minute_of_day // self.config.load_profile_bucket_minutes
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) * self.config.load_profile_bucket_minutes
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return local.isoweekday(), bucket
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def _local_timezone(self) -> ZoneInfo:
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return ZoneInfo(self._local_timezone_name())
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def _local_timezone_name(self) -> str:
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try:
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ZoneInfo(self.config.local_timezone)
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except ZoneInfoNotFoundError:
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return "UTC"
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return self.config.local_timezone
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def _blend(self, profile_value: float, recent_value: float, blend: float) -> float:
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return profile_value * (1.0 - blend) + recent_value * blend
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def _number(self, value: object, fallback: float) -> float:
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if value is None:
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return float(fallback)
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return float(value)
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