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review improvements
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Normann
Andreas
parent
ed8c6580ee
commit
8c41d291bd
@ -6,16 +6,17 @@ humidity, cloud cover, and solar irradiance. The data is mapped to the `ElecPric
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format, enabling consistent access to forecasted and historical electricity price attributes.
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"""
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from typing import Any, List, Optional, Tuple, Union
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from typing import Any, List, Optional, Union
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import numpy as np
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import pandas as pd
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import requests
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from pydantic import ValidationError
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from statsmodels.tsa.holtwinters import ExponentialSmoothing
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from akkudoktoreos.core.logging import get_logger
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from akkudoktoreos.core.pydantic import PydanticBaseModel
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from akkudoktoreos.prediction.elecpriceabc import ElecPriceDataRecord, ElecPriceProvider
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from akkudoktoreos.prediction.elecpriceabc import ElecPriceProvider
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from akkudoktoreos.utils.cacheutil import cache_in_file
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from akkudoktoreos.utils.datetimeutil import to_datetime, to_duration
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@ -100,7 +101,6 @@ class ElecPriceAkkudoktor(ElecPriceProvider):
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ValueError: If the API response does not include expected `electricity price` data.
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Todo:
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- maybe some data cleanup/checking. we might have a problem if a single day has none values or is missing at all in the api or the data.
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- add the file cache again.
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"""
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source = "https://api.akkudoktor.net"
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@ -141,7 +141,7 @@ class ElecPriceAkkudoktor(ElecPriceProvider):
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def _update_data(
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self, force_update: Optional[bool] = False
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) -> Tuple[np.ndarray, np.ndarray, np.ndarray]: # TODO: remove return, only for debug
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) -> None: # Tuple[np.ndarray, np.ndarray, np.ndarray]: # for debug main
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"""Update forecast data in the ElecPriceDataRecord format.
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Retrieves data from Akkudoktor, maps each Akkudoktor field to the corresponding
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@ -152,6 +152,7 @@ class ElecPriceAkkudoktor(ElecPriceProvider):
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# Get Akkudoktor electricity price data
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akkudoktor_data = self._request_forecast(force_update=force_update) # type: ignore
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assert self.start_datetime # mypy fix
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# Assumption that all lists are the same length and are ordered chronologically
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# in ascending order and have the same timestamps.
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@ -159,6 +160,7 @@ class ElecPriceAkkudoktor(ElecPriceProvider):
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charges_wh = (self.config.elecprice_charges_kwh or 0) / 1000
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highest_orig_datetime = None # newest datetime from the api after that we want to update.
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series_data = pd.Series(dtype=float) # Initialize an empty series
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for value in akkudoktor_data.values:
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orig_datetime = to_datetime(value.start, in_timezone=self.config.timezone)
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@ -167,77 +169,51 @@ class ElecPriceAkkudoktor(ElecPriceProvider):
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price_wh = value.marketpriceEurocentPerKWh / (100 * 1000) + charges_wh
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existing_record = next((r for r in self.records if r.date_time == orig_datetime), None)
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if existing_record:
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# Update existing record
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existing_record.elecprice_marketprice_wh = price_wh
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else:
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self.insert(
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0,
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ElecPriceDataRecord(date_time=orig_datetime, elecprice_marketprice_wh=price_wh),
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)
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# Collect all values into the Pandas Series
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series_data.at[orig_datetime] = price_wh
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# Update values using key_from_series
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self.key_from_series("elecprice_marketprice_wh", series_data)
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# Generate history array for prediction
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history = np.array(
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[
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record.elecprice_marketprice_wh
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for record in sorted(self.records, key=lambda r: r.date_time)
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if record.elecprice_marketprice_wh is not None
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and record.date_time
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< highest_orig_datetime # make sure we only real data for the prediction, so cant be newer then data from the api.
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]
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history = self.key_to_array(
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key="elecprice_marketprice_wh", end_datetime=highest_orig_datetime, fill_method="linear"
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)
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amount_datasets = len(self.records)
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assert highest_orig_datetime # mypy fix
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if amount_datasets > 800:
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if amount_datasets > 800: # we do the full ets with seasons of 1 week
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prediction = self._predict_ets(
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history, seasonal_periods=168, prediction_hours=7 * 24
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) # todo: add config values for prediction_hours
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elif amount_datasets > 168:
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history, seasonal_periods=168, prediction_hours=self.config.prediction_hours
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)
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elif amount_datasets > 168: # not enough data to do seasons of 1 week, but enough for 1 day
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prediction = self._predict_ets(
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history, seasonal_periods=24, prediction_hours=7 * 24
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) # todo: add config values for prediction_hours
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elif (
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amount_datasets > 0
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): # TODO might be a problem if amount_datasets is really low and we do the _cap_outliers.
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prediction = self._predict_median(history, prediction_hours=7 * 24)
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history, seasonal_periods=24, prediction_hours=self.config.prediction_hours
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)
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elif amount_datasets > 0: # not enough data for ets, do median
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prediction = self._predict_median(
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history, prediction_hours=self.config.prediction_hours
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)
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else:
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assert False, "No data available"
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logger.error("No data available for prediction")
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raise ValueError("No data available")
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# write predictions into the records, update if exist.
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for i, price in enumerate(prediction):
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pred_datetime = highest_orig_datetime + to_duration(f"{i + 1} hours")
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existing_record = next((r for r in self.records if r.date_time == pred_datetime), None)
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if existing_record:
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# Update existing record
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existing_record.elecprice_marketprice_wh = price
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else:
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assert pred_datetime # mypy fix, why do we need that we already made sure highest_orig_datetime is not None
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self.insert(
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0,
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ElecPriceDataRecord(date_time=pred_datetime, elecprice_marketprice_wh=price),
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)
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history2 = np.array( # TODO: remove return, only for debug, offset to see the difference
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[
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record.elecprice_marketprice_wh + 0.0002
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for record in sorted(self.records, key=lambda r: r.date_time)
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if record.elecprice_marketprice_wh is not None
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]
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prediction_series = pd.Series(
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data=prediction,
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index=[
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highest_orig_datetime + to_duration(f"{i + 1} hours")
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for i in range(len(prediction))
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],
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)
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self.key_from_series("elecprice_marketprice_wh", prediction_series)
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return history, history2, prediction # TODO: remove return, only for debug
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def main() -> None:
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elec_price_akkudoktor = ElecPriceAkkudoktor()
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history, history2, predictions = elec_price_akkudoktor._update_data()
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visualize_predictions(history, history2, predictions)
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# print(history, history2, predictions)
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# history2 = self.key_to_array(key="elecprice_marketprice_wh", fill_method="linear") + 0.0002
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# return history, history2, prediction # for debug main
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"""
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def visualize_predictions(
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history: np.ndarray[Any, Any],
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history2: np.ndarray[Any, Any],
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@ -262,5 +238,14 @@ def visualize_predictions(
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plt.close()
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def main() -> None:
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elec_price_akkudoktor = ElecPriceAkkudoktor()
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history, history2, predictions = elec_price_akkudoktor._update_data()
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visualize_predictions(history, history2, predictions)
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# print(history, history2, predictions)
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if __name__ == "__main__":
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main()
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"""
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