rpotter6298
c8e3016fd6
- 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.
33 lines
1.1 KiB
Python
33 lines
1.1 KiB
Python
from __future__ import annotations
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from datetime import datetime
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from gibil.classes.models import PowerForecastRun
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from gibil.classes.oracle.config import EnergyForecastConfig
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from gibil.classes.predictors.usage_daily import DailyUsageOracle
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from gibil.classes.sigen.store import SigenStore
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class SequenceUsageOracle:
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"""Forecasts load from recent sequence state when a trained model exists."""
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model_version = "sequence_usage_tcn_v1"
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def __init__(
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self,
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sigen_store: SigenStore,
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config: EnergyForecastConfig,
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) -> None:
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self.sigen_store = sigen_store
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self.config = config
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self.fallback = DailyUsageOracle(sigen_store=sigen_store, config=config)
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def forecast(
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self,
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target_times: list[datetime],
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issued_at: datetime | None = None,
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) -> PowerForecastRun:
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# The sequence model scaffold is present, but production should remain
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# deterministic until we have a trained artifact and evaluation history.
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return self.fallback.forecast(target_times=target_times, issued_at=issued_at)
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