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.
2026-04-28 08:14:00 +02:00
parent ff0c65a794
commit c8e3016fd6
55 changed files with 6385 additions and 633 deletions
@@ -0,0 +1,94 @@
+from __future__ import annotations
+
+from datetime import datetime, timedelta, timezone
+
+from gibil.classes.models import ForecastKind, PowerForecastPoint, PowerForecastRun, WeatherForecastPoint
+from gibil.classes.oracle.config import EnergyForecastConfig
+from gibil.classes.sigen.store import SigenStore
+from gibil.classes.weather.store import WeatherStore
+
+
+class BaselineSolarProductionOracle:
+    """Forecasts solar production from shortwave radiation and recent plant peak."""
+
+    model_version = "baseline_solar_radiation_v1"
+
+    def __init__(
+        self,
+        weather_store: WeatherStore,
+        sigen_store: SigenStore,
+        config: EnergyForecastConfig,
+    ) -> None:
+        self.weather_store = weather_store
+        self.sigen_store = sigen_store
+        self.config = config
+
+    def forecast(self, issued_at: datetime | None = None) -> PowerForecastRun:
+        if issued_at is None:
+            issued_at = datetime.now(timezone.utc)
+
+        weather_points = self.weather_store.load_latest_forecast_points(
+            start_at=issued_at,
+            end_at=issued_at + timedelta(hours=self.config.horizon_hours),
+        )
+        peak_w = self._solar_peak_w()
+        points = [
+            self._forecast_point(
+                weather_point=point,
+                issued_at=issued_at,
+                peak_w=peak_w,
+            )
+            for point in weather_points
+        ]
+
+        return PowerForecastRun(
+            issued_at=issued_at,
+            kind=ForecastKind.SOLAR,
+            source="baseline_solar_oracle",
+            model_version=self.model_version,
+            points=points,
+        )
+
+    def _forecast_point(
+        self,
+        weather_point: WeatherForecastPoint,
+        issued_at: datetime,
+        peak_w: float,
+    ) -> PowerForecastPoint:
+        radiation = max(weather_point.shortwave_radiation_w_m2 or 0.0, 0.0)
+        expected = min(peak_w, peak_w * (radiation / 1000.0) * self.config.solar_scale)
+        cloud_cover = weather_point.cloud_cover_pct
+        cloud_uncertainty = 1.0
+        if cloud_cover is not None:
+            cloud_uncertainty += min(max(cloud_cover, 0.0), 100.0) / 200.0
+
+        p10 = max(0.0, expected * (0.75 / cloud_uncertainty))
+        p90 = min(peak_w, expected * (1.15 * cloud_uncertainty))
+
+        return PowerForecastPoint(
+            target_at=weather_point.target_at,
+            horizon_minutes=self._horizon_minutes(issued_at, weather_point.target_at),
+            expected_power_w=expected,
+            p10_power_w=p10,
+            p50_power_w=expected,
+            p90_power_w=p90,
+            confidence=0.25,
+            source="open_meteo_shortwave",
+            model_version=self.model_version,
+            metadata={
+                "shortwave_radiation_w_m2": weather_point.shortwave_radiation_w_m2,
+                "cloud_cover_pct": weather_point.cloud_cover_pct,
+                "temperature_c": weather_point.temperature_c,
+                "solar_peak_w": peak_w,
+                "fallback_reason": "not_enough_solar_training_samples",
+            },
+        )
+
+    def _solar_peak_w(self) -> float:
+        recent_peak = self.sigen_store.load_recent_solar_peak_w()
+        if recent_peak is None or recent_peak <= 0:
+            return self.config.fallback_solar_peak_w
+        return recent_peak * max(self.config.solar_peak_headroom, 1.0)
+
+    def _horizon_minutes(self, issued_at: datetime, target_at: datetime) -> int:
+        return max(0, round((target_at - issued_at).total_seconds() / 60))