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https://github.com/Akkudoktor-EOS/EOS.git
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103 lines
4.3 KiB
Python
103 lines
4.3 KiB
Python
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"""Abstract and base classes for load predictions.
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Notes:
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- Ensure appropriate API keys or configurations are set up if required by external data sources.
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"""
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from abc import abstractmethod
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from typing import List, Optional
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from pydantic import Field, computed_field
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from akkudoktoreos.prediction.predictionabc import PredictionProvider, PredictionRecord
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from akkudoktoreos.utils.logutil import get_logger
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logger = get_logger(__name__)
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class LoadDataRecord(PredictionRecord):
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"""Represents a load data record containing various load attributes at a specific datetime."""
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load0_mean: Optional[float] = Field(default=None, description="Load 0 mean value (W)")
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load0_std: Optional[float] = Field(default=None, description="Load 0 standard deviation (W)")
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load1_mean: Optional[float] = Field(default=None, description="Load 1 mean value (W)")
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load1_std: Optional[float] = Field(default=None, description="Load 1 standard deviation (W)")
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load2_mean: Optional[float] = Field(default=None, description="Load 2 mean value (W)")
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load2_std: Optional[float] = Field(default=None, description="Load 2 standard deviation (W)")
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load3_mean: Optional[float] = Field(default=None, description="Load 3 mean value (W)")
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load3_std: Optional[float] = Field(default=None, description="Load 3 standard deviation (W)")
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load4_mean: Optional[float] = Field(default=None, description="Load 4 mean value (W)")
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load4_std: Optional[float] = Field(default=None, description="Load 4 standard deviation (W)")
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# Computed fields
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@computed_field # type: ignore[prop-decorator]
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@property
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def load_total_mean(self) -> float:
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"""Total load mean value (W)."""
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total_mean = 0.0
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for i in range(5):
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load_mean_attr = f"load{i}_mean"
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value = getattr(self, load_mean_attr)
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if value:
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total_mean += value
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return total_mean
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@computed_field # type: ignore[prop-decorator]
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@property
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def load_total_std(self) -> float:
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"""Total load standard deviation (W)."""
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total_std = 0.0
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for i in range(5):
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load_std_attr = f"load{i}_std"
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value = getattr(self, load_std_attr)
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if value:
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total_std += value
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return total_std
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class LoadProvider(PredictionProvider):
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"""Abstract base class for load providers.
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LoadProvider is a thread-safe singleton, ensuring only one instance of this class is created.
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Configuration variables:
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load_provider (str): Prediction provider for load.
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Attributes:
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prediction_hours (int, optional): The number of hours into the future for which predictions are generated.
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prediction_historic_hours (int, optional): The number of past hours for which historical data is retained.
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latitude (float, optional): The latitude in degrees, must be within -90 to 90.
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longitude (float, optional): The longitude in degrees, must be within -180 to 180.
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start_datetime (datetime, optional): The starting datetime for predictions, defaults to the current datetime if unspecified.
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end_datetime (datetime, computed): The datetime representing the end of the prediction range,
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calculated based on `start_datetime` and `prediction_hours`.
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keep_datetime (datetime, computed): The earliest datetime for retaining historical data, calculated
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based on `start_datetime` and `prediction_historic_hours`.
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"""
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# overload
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records: List[LoadDataRecord] = Field(
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default_factory=list, description="List of LoadDataRecord records"
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)
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@classmethod
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@abstractmethod
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def provider_id(cls) -> str:
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return "LoadProvider"
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def enabled(self) -> bool:
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logger.debug(
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f"LoadProvider ID {self.provider_id()} vs. config {self.config.load_providers}"
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)
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return self.provider_id() == self.config.load_providers
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def loads(self) -> List[str]:
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"""Returns a list of key prefixes of the loads managed by this provider."""
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loads_prefix = []
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for i in range(self.config.load_count):
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load_provider_attr = f"load{i}_provider"
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value = getattr(self.config, load_provider_attr)
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if value == self.provider_id():
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loads_prefix.append(f"load{i}")
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return loads_prefix
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