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_hybrid_architecture.py

@@ -0,0 +1,35 @@
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+from gibil.classes.predictors.usage_sequence_dataset import (
+    UsageSequenceDatasetBuilder,
+    UsageSequenceScaleConfig,
+)
+
+
+@dataclass(frozen=True)
+class UsageHybridModelShape:
+    """Describes the fixed-plus-token sequence model input contract."""
+
+    past_scales: tuple[UsageSequenceScaleConfig, ...]
+    past_fixed_features: tuple[str, ...]
+    future_fixed_features: tuple[str, ...]
+    future_steps: int
+    quantiles: tuple[float, ...] = (0.10, 0.50, 0.90)
+
+    @classmethod
+    def from_dataset_builder(
+        cls,
+        builder: UsageSequenceDatasetBuilder,
+    ) -> "UsageHybridModelShape":
+        return cls(
+            past_scales=builder.config.past_scales,
+            past_fixed_features=tuple(builder.past_feature_names),
+            future_fixed_features=tuple(builder.future_feature_names),
+            future_steps=builder.future_steps,
+        )
+
+    @property
+    def output_width(self) -> int:
+        return self.future_steps * len(self.quantiles)