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EOS/src/akkudoktoreos/prediction/loadabc.py

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