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.
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@@ -200,6 +200,40 @@ Notes:
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- future prediction modules can join this to `weather_forecast_points`
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- make this a hypertable on `resolved_at`
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### `sigen_plant_snapshots`
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High-resolution Sigenergy plant state from Modbus TCP.
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Core fields:
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- `observed_at`
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- `received_at`
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- `source`
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- `solar_power_w`
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- `battery_soc_pct`
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- `battery_soh_pct`
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- `battery_power_w`
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- `grid_power_w`
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- `grid_import_w`
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- `grid_export_w`
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- `load_power_w`
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- `plant_active_power_w`
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- `accumulated_pv_energy_kwh`
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- `daily_consumed_energy_kwh`
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- `accumulated_consumed_energy_kwh`
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- status fields for EMS, running state, and grid sensor state
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- `raw_values`
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Notes:
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- raw polling target is `SIGEN_POLL_SECONDS=5`
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- make this a hypertable on `observed_at`
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- keep raw JSON during integration so unsupported or surprising registers can be debugged
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- rollup views should preserve averages, min/max spikes, and sample counts so short-duration usage signatures are not erased completely
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Initial rollups:
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- `sigen_plant_snapshots_1m`
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- `sigen_plant_snapshots_15m`
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- `sigen_plant_snapshots_1h`
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### `system_events`
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Operational events from collectors, storage, Gibil, and publishers.
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