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/predictors/usage_sequence.py

@@ -0,0 +1,32 @@
+from __future__ import annotations
+
+from datetime import datetime
+
+from gibil.classes.models import PowerForecastRun
+from gibil.classes.oracle.config import EnergyForecastConfig
+from gibil.classes.predictors.usage_daily import DailyUsageOracle
+from gibil.classes.sigen.store import SigenStore
+
+
+class SequenceUsageOracle:
+    """Forecasts load from recent sequence state when a trained model exists."""
+
+    model_version = "sequence_usage_tcn_v1"
+
+    def __init__(
+        self,
+        sigen_store: SigenStore,
+        config: EnergyForecastConfig,
+    ) -> None:
+        self.sigen_store = sigen_store
+        self.config = config
+        self.fallback = DailyUsageOracle(sigen_store=sigen_store, config=config)
+
+    def forecast(
+        self,
+        target_times: list[datetime],
+        issued_at: datetime | None = None,
+    ) -> PowerForecastRun:
+        # The sequence model scaffold is present, but production should remain
+        # deterministic until we have a trained artifact and evaluation history.
+        return self.fallback.forecast(target_times=target_times, issued_at=issued_at)