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day end
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@ -10,8 +10,7 @@ from typing import Any, List, Optional, Union
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import numpy as np
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import requests
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from numpydantic import NDArray, Shape
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from pydantic import Field, ValidationError
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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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@ -66,14 +65,6 @@ class ElecPriceAkkudoktor(ElecPriceProvider):
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_update_data(): Processes and updates forecast data from Akkudoktor in ElecPriceDataRecord format.
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"""
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elecprice_8days_weights_day_of_week: NDArray[Shape["7, 8"], float] = Field(
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default=np.full((7, 8), np.nan),
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description="Daily electricity price weights for the last 7 days and today. "
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"A NumPy array of 7 elements (Monday..Sunday), each representing "
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"the daily price weights of the last 7 days (index 0..6, Monday..Sunday) "
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"and today (index 7).",
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)
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@classmethod
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def provider_id(cls) -> str:
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"""Return the unique identifier for the Akkudoktor provider."""
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@ -172,12 +163,22 @@ class ElecPriceAkkudoktor(ElecPriceProvider):
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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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self.insert(
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0, record
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) # idk what happens if the date is already there. try except update?
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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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# 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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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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