"""Retrieves and processes electricity price forecast data from Akkudoktor. This module provides classes and mappings to manage electricity price data obtained from the Akkudoktor API, including support for various electricity price attributes such as temperature, humidity, cloud cover, and solar irradiance. The data is mapped to the `ElecPriceDataRecord` format, enabling consistent access to forecasted and historical electricity price attributes. """ from typing import Any, List, Optional, Union import numpy as np import requests from pydantic import ValidationError from statsmodels.tsa.holtwinters import ExponentialSmoothing from akkudoktoreos.core.logging import get_logger from akkudoktoreos.core.pydantic import PydanticBaseModel from akkudoktoreos.prediction.elecpriceabc import ElecPriceDataRecord, ElecPriceProvider from akkudoktoreos.utils.cacheutil import cache_in_file from akkudoktoreos.utils.datetimeutil import to_datetime, to_duration logger = get_logger(__name__) class AkkudoktorElecPriceMeta(PydanticBaseModel): start_timestamp: str end_timestamp: str start: str end: str class AkkudoktorElecPriceValue(PydanticBaseModel): start_timestamp: int end_timestamp: int start: str end: str marketprice: float unit: str marketpriceEurocentPerKWh: float class AkkudoktorElecPrice(PydanticBaseModel): meta: AkkudoktorElecPriceMeta values: List[AkkudoktorElecPriceValue] class ElecPriceAkkudoktor(ElecPriceProvider): """Fetch and process electricity price forecast data from Akkudoktor. ElecPriceAkkudoktor is a singleton-based class that retrieves electricity price forecast data from the Akkudoktor API and maps it to `ElecPriceDataRecord` fields, applying any necessary scaling or unit corrections. It manages the forecast over a range of hours into the future and retains historical data. Attributes: prediction_hours (int, optional): Number of hours in the future for the forecast. prediction_historic_hours (int, optional): Number of past hours for retaining data. start_datetime (datetime, optional): Start datetime for forecasts, defaults to the current datetime. end_datetime (datetime, computed): The forecast's end datetime, computed based on `start_datetime` and `prediction_hours`. keep_datetime (datetime, computed): The datetime to retain historical data, computed from `start_datetime` and `prediction_historic_hours`. Methods: provider_id(): Returns a unique identifier for the provider. _request_forecast(): Fetches the forecast from the Akkudoktor API. _update_data(): Processes and updates forecast data from Akkudoktor in ElecPriceDataRecord format. """ @classmethod def provider_id(cls) -> str: """Return the unique identifier for the Akkudoktor provider.""" return "ElecPriceAkkudoktor" @classmethod def _validate_data(cls, json_str: Union[bytes, Any]) -> AkkudoktorElecPrice: """Validate Akkudoktor Electricity Price forecast data.""" try: akkudoktor_data = AkkudoktorElecPrice.model_validate_json(json_str) except ValidationError as e: error_msg = "" for error in e.errors(): field = " -> ".join(str(x) for x in error["loc"]) message = error["msg"] error_type = error["type"] error_msg += f"Field: {field}\nError: {message}\nType: {error_type}\n" logger.error(f"Akkudoktor schema change: {error_msg}") raise ValueError(error_msg) return akkudoktor_data @cache_in_file(with_ttl="1 hour") def _request_forecast(self) -> AkkudoktorElecPrice: """Fetch electricity price forecast data from Akkudoktor API. This method sends a request to Akkudoktor's API to retrieve forecast data for a specified date range. The response data is parsed and returned as JSON for further processing. Returns: dict: The parsed JSON response from Akkudoktor API containing forecast data. Raises: ValueError: If the API response does not include expected `electricity price` data. """ source = "https://api.akkudoktor.net" assert self.start_datetime # mypy fix # Try to take data from 5 weeks back for prediction date = to_datetime(self.start_datetime - to_duration("35 days"), as_string="YYYY-MM-DD") last_date = to_datetime(self.end_datetime, as_string="YYYY-MM-DD") url = f"{source}/prices?start={date}&end={last_date}&tz={self.config.timezone}" response = requests.get(url) logger.debug(f"Response from {url}: {response}") response.raise_for_status() # Raise an error for bad responses akkudoktor_data = self._validate_data(response.content) # We are working on fresh data (no cache), report update time self.update_datetime = to_datetime(in_timezone=self.config.timezone) return akkudoktor_data def _cap_outliers(self, data: np.ndarray, sigma: int = 2) -> np.ndarray: mean = data.mean() std = data.std() lower_bound = mean - sigma * std upper_bound = mean + sigma * std capped_data = data.clip(min=lower_bound, max=upper_bound) return capped_data def _predict_ets( self, history: np.ndarray, seasonal_periods: int, prediction_hours: int ) -> np.ndarray: clean_history = self._cap_outliers(history) model = ExponentialSmoothing( clean_history, seasonal="add", seasonal_periods=seasonal_periods ).fit() return model.forecast(prediction_hours) def _predict_median(self, history: np.ndarray, prediction_hours: int) -> np.ndarray: clean_history = self._cap_outliers(history) return np.full(prediction_hours, np.median(clean_history)) def _update_data(self, force_update: Optional[bool] = False) -> None: """Update forecast data in the ElecPriceDataRecord format. Retrieves data from Akkudoktor, maps each Akkudoktor field to the corresponding `ElecPriceDataRecord` and applies any necessary scaling. The final mapped and processed data is inserted into the sequence as `ElecPriceDataRecord`. """ # Get Akkudoktor electricity price data akkudoktor_data = self._request_forecast(force_update=force_update) # type: ignore # Assumption that all lists are the same length and are ordered chronologically # in ascending order and have the same timestamps. # Get elecprice_charges_kwh_kwh charges_wh = ( self.config.elecprice_charges_kwh / 1000 if self.config.elecprice_charges_kwh else 0.0 ) assert self.start_datetime # mypy fix for akkudoktor_value in akkudoktor_data.values: orig_datetime = to_datetime(akkudoktor_value.start, in_timezone=self.config.timezone) price_wh = akkudoktor_value.marketpriceEurocentPerKWh / (100 * 1000) + charges_wh record = ElecPriceDataRecord( date_time=orig_datetime, elecprice_marketprice_wh=price_wh, ) try: self.insert(0, record) except: pass # self.update_value(record) # now we check if we have data newer than the last from the api. if so thats old prediction. we delete them all. amount_datasets = len(self.records) if amount_datasets > 800: pass elif amount_datasets >= 168: pass elif amount_datasets < 168 and amount_datasets > 0: pass else: pass # now we count how many data points we have. # if its > 800 (5 weeks) we will use EST # elif > idk maybe 168 (1 week) we use EST without season # elif < 168 we use a simple median # #elif == 0 we need some static value from the config # depending on the result we check prediction_hours and predict that many hours. # we get the result and iterate over it to put it into ElecPriceDataRecord