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.

gibil/classes/models.py

@@ -22,6 +22,11 @@ class PowerStage(str, Enum):
     CONSERVE = "conserve"
 
 
+class ForecastKind(str, Enum):
+    SOLAR = "solar"
+    LOAD = "load"
+
+
 @dataclass(frozen=True)
 class Observation:
     source: str
@@ -80,3 +85,75 @@ class WeatherResolvedTruth:
     temperature_c: float | None
     shortwave_radiation_w_m2: float | None
     source: str
+    cloud_cover_pct: float | None = None
+
+
+@dataclass(frozen=True)
+class SigenPlantSnapshot:
+    observed_at: datetime
+    received_at: datetime
+    source: str = "sigen_modbus"
+    plant_epoch_seconds: int | None = None
+    plant_ems_work_mode: int | None = None
+    plant_running_state: int | None = None
+    grid_sensor_status: int | None = None
+    solar_power_w: float | None = None
+    battery_soc_pct: float | None = None
+    battery_soh_pct: float | None = None
+    battery_power_w: float | None = None
+    grid_power_w: float | None = None
+    grid_import_w: float | None = None
+    grid_export_w: float | None = None
+    load_power_w: float | None = None
+    plant_active_power_w: float | None = None
+    accumulated_pv_energy_kwh: float | None = None
+    daily_consumed_energy_kwh: float | None = None
+    accumulated_consumed_energy_kwh: float | None = None
+    raw_values: dict[str, int | float | str | bool | None] = field(default_factory=dict)
+
+
+@dataclass(frozen=True)
+class PowerForecastPoint:
+    target_at: datetime
+    horizon_minutes: int
+    expected_power_w: float
+    p10_power_w: float
+    p50_power_w: float
+    p90_power_w: float
+    confidence: float
+    source: str
+    model_version: str
+    metadata: dict[str, Any] = field(default_factory=dict)
+
+
+@dataclass(frozen=True)
+class PowerForecastRun:
+    issued_at: datetime
+    kind: ForecastKind
+    source: str
+    model_version: str
+    points: list[PowerForecastPoint]
+
+
+@dataclass(frozen=True)
+class NetPowerForecastPoint:
+    target_at: datetime
+    horizon_minutes: int
+    expected_net_power_w: float
+    safe_net_power_w: float
+    p10_net_power_w: float
+    p50_net_power_w: float
+    p90_net_power_w: float
+    solar_p50_power_w: float
+    load_p50_power_w: float
+    solar_p10_power_w: float
+    solar_p90_power_w: float
+    load_p10_power_w: float
+    load_p90_power_w: float
+
+
+@dataclass(frozen=True)
+class NetPowerForecastRun:
+    issued_at: datetime
+    source: str
+    points: list[NetPowerForecastPoint]