"""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 pandas as pd 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 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: hours (int, optional): Number of hours in the future for the forecast. 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 `hours`. keep_datetime (datetime, computed): The datetime to retain historical data, computed from `start_datetime` and `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. Todo: - add the file cache again. """ 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.prediction.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.prediction.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, hours: int) -> np.ndarray: clean_history = self._cap_outliers(history) model = ExponentialSmoothing( clean_history, seasonal="add", seasonal_periods=seasonal_periods ).fit() return model.forecast(hours) def _predict_median(self, history: np.ndarray, hours: int) -> np.ndarray: clean_history = self._cap_outliers(history) return np.full(hours, np.median(clean_history)) def _update_data( self, force_update: Optional[bool] = False ) -> None: # tuple[np.ndarray, np.ndarray, np.ndarray]: """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 assert self.start_datetime # mypy fix # Assumption that all lists are the same length and are ordered chronologically # in ascending order and have the same timestamps. # Get charges_kwh in wh charges_wh = (self.config.elecprice.charges_kwh or 0) / 1000 highest_orig_datetime = None # newest datetime from the api after that we want to update. series_data = pd.Series(dtype=float) # Initialize an empty series for value in akkudoktor_data.values: orig_datetime = to_datetime(value.start, in_timezone=self.config.prediction.timezone) if highest_orig_datetime is None or orig_datetime > highest_orig_datetime: highest_orig_datetime = orig_datetime price_wh = value.marketpriceEurocentPerKWh / (100 * 1000) + charges_wh # Collect all values into the Pandas Series series_data.at[orig_datetime] = price_wh # Update values using key_from_series self.key_from_series("elecprice_marketprice_wh", series_data) # Generate history array for prediction history = self.key_to_array( key="elecprice_marketprice_wh", end_datetime=highest_orig_datetime, fill_method="linear" ) amount_datasets = len(self.records) assert highest_orig_datetime # mypy fix # some of our data is already in the future, so we need to predict less. If we got less data we increase the prediction hours needed_hours = int( self.config.prediction.hours - ((highest_orig_datetime - self.start_datetime).total_seconds() // 3600) ) if needed_hours <= 0: logger.warning( f"No prediction needed. needed_hours={needed_hours}, hours={self.config.prediction.hours},highest_orig_datetime {highest_orig_datetime}, start_datetime {self.start_datetime}" ) # this might keep data longer than self.start_datetime + self.config.prediction.hours in the records return if amount_datasets > 800: # we do the full ets with seasons of 1 week prediction = self._predict_ets(history, seasonal_periods=168, hours=needed_hours) elif amount_datasets > 168: # not enough data to do seasons of 1 week, but enough for 1 day prediction = self._predict_ets(history, seasonal_periods=24, hours=needed_hours) elif amount_datasets > 0: # not enough data for ets, do median prediction = self._predict_median(history, hours=needed_hours) else: logger.error("No data available for prediction") raise ValueError("No data available") # write predictions into the records, update if exist. prediction_series = pd.Series( data=prediction, index=[ highest_orig_datetime + to_duration(f"{i + 1} hours") for i in range(len(prediction)) ], ) self.key_from_series("elecprice_marketprice_wh", prediction_series) # history2 = self.key_to_array(key="elecprice_marketprice_wh", fill_method="linear") + 0.0002 # return history, history2, prediction # for debug main """ def visualize_predictions( history: np.ndarray[Any, Any], history2: np.ndarray[Any, Any], predictions: np.ndarray[Any, Any], ) -> None: import matplotlib.pyplot as plt plt.figure(figsize=(28, 14)) plt.plot(range(len(history)), history, label="History", color="green") plt.plot(range(len(history2)), history2, label="History_new", color="blue") plt.plot( range(len(history), len(history) + len(predictions)), predictions, label="Predictions", color="red", ) plt.title("Predictions ets") plt.xlabel("Time") plt.ylabel("Price") plt.legend() plt.savefig("predictions_vs_true.png") plt.close() def main() -> None: # Initialize ElecPriceAkkudoktor with required parameters elec_price_akkudoktor = ElecPriceAkkudoktor() history, history2, predictions = elec_price_akkudoktor._update_data() visualize_predictions(history, history2, predictions) # print(history, history2, predictions) if __name__ == "__main__": main() """