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initially working
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@ -144,47 +144,79 @@ class ElecPriceAkkudoktor(ElecPriceProvider):
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"""
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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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# Get elecprice_charges_kwh_kwh
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charges_wh = (
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self.config.elecprice_charges_kwh / 1000 if self.config.elecprice_charges_kwh else 0.0
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)
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assert self.start_datetime # mypy fix
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# Get elecprice_charges_kwh in wh
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charges_wh = (self.config.elecprice_charges_kwh or 0) / 1000
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for akkudoktor_value in akkudoktor_data.values:
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orig_datetime = to_datetime(akkudoktor_value.start, in_timezone=self.config.timezone)
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highest_orig_datetime = None # newest datetime from the api after that we want to update.
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price_wh = akkudoktor_value.marketpriceEurocentPerKWh / (100 * 1000) + charges_wh
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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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if highest_orig_datetime is None or orig_datetime > highest_orig_datetime:
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highest_orig_datetime = orig_datetime
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record = ElecPriceDataRecord(
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date_time=orig_datetime,
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elecprice_marketprice_wh=price_wh,
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)
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try:
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self.insert(0, record)
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except:
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pass
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# self.update_value(record)
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price_wh = value.marketpriceEurocentPerKWh / (100 * 1000) + charges_wh
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# now we check if we have data newer than the last from the api. if so thats old prediction. we delete them all.
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amount_datasets = len(self.records)
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if amount_datasets > 800:
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pass
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elif amount_datasets >= 168:
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pass
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elif amount_datasets < 168 and amount_datasets > 0:
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pass
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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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pass
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# now we count how many data points we have.
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# if its > 800 (5 weeks) we will use EST
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# elif > idk maybe 168 (1 week) we use EST without season
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# elif < 168 we use a simple median
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# #elif == 0 we need some static value from the config
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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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# depending on the result we check prediction_hours and predict that many hours.
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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 self.records
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if record.elecprice_marketprice_wh is not None
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]
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)
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# we get the result and iterate over it to put it into ElecPriceDataRecord
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amount_datasets = len(self.records)
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# Insert prediction into ElecPriceDataRecord
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if amount_datasets > 800:
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assert highest_orig_datetime # mypy fix
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prediction = self._predict_ets(
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history, seasonal_periods=24 * 7, prediction_hours=24 * 7
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)
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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(
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(r for r in self.records if r.date_time == pred_datetime), None
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)
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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
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self.insert(
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0,
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ElecPriceDataRecord(
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date_time=pred_datetime, elecprice_marketprice_wh=price
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),
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)
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history2 = np.array(
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[
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[record.elecprice_marketprice_wh, record.date_time]
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for record in self.records
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if record.elecprice_marketprice_wh is not None
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]
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)
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print(len(history2), len(history))
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def main() -> None:
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elec_price_akkudoktor = ElecPriceAkkudoktor()
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elec_price_akkudoktor._update_data()
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if __name__ == "__main__":
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main()
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