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.
61 lines
2.3 KiB
Python
61 lines
2.3 KiB
Python
from __future__ import annotations
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from dataclasses import dataclass
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from os import environ
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@dataclass(frozen=True)
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class EnergyForecastConfig:
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horizon_hours: int = 24
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oracle_step_minutes: int = 15
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fallback_solar_peak_w: float = 10000
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solar_peak_headroom: float = 1.05
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solar_scale: float = 1.0
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solar_training_days: int = 30
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solar_min_training_samples: int = 24
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solar_ridge_lambda: float = 0.1
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load_lookback_minutes: int = 30
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load_profile_days: int = 30
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load_profile_bucket_minutes: int = 15
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load_profile_min_samples: int = 5
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load_recent_blend: float = 0.35
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local_timezone: str = "Europe/Stockholm"
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@classmethod
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def from_env(cls) -> "EnergyForecastConfig":
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return cls(
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horizon_hours=int(environ.get("ASTRAPE_ENERGY_FORECAST_HOURS", "24")),
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oracle_step_minutes=int(environ.get("ASTRAPE_ORACLE_STEP_MINUTES", "15")),
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fallback_solar_peak_w=float(
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environ.get("ASTRAPE_SOLAR_PEAK_W", "10000")
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),
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solar_peak_headroom=float(
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environ.get("ASTRAPE_SOLAR_PEAK_HEADROOM", "1.05")
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),
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solar_scale=float(environ.get("ASTRAPE_SOLAR_FORECAST_SCALE", "1.0")),
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solar_training_days=int(
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environ.get("ASTRAPE_SOLAR_TRAINING_DAYS", "30")
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),
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solar_min_training_samples=int(
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environ.get("ASTRAPE_SOLAR_MIN_TRAINING_SAMPLES", "24")
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),
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solar_ridge_lambda=float(
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environ.get("ASTRAPE_SOLAR_RIDGE_LAMBDA", "0.1")
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),
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load_lookback_minutes=int(
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environ.get("ASTRAPE_LOAD_LOOKBACK_MINUTES", "30")
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),
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load_profile_days=int(environ.get("ASTRAPE_LOAD_PROFILE_DAYS", "30")),
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load_profile_bucket_minutes=int(
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environ.get("ASTRAPE_LOAD_PROFILE_BUCKET_MINUTES", "15")
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),
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load_profile_min_samples=int(
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environ.get("ASTRAPE_LOAD_PROFILE_MIN_SAMPLES", "5")
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),
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load_recent_blend=float(environ.get("ASTRAPE_LOAD_RECENT_BLEND", "0.35")),
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local_timezone=environ.get(
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"ASTRAPE_LOCAL_TIMEZONE",
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environ.get("TZ", "Europe/Stockholm"),
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),
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)
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