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initial commit
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Normann
Andreas
parent
d2f6e9866d
commit
4c0afb6a2a
@ -16,8 +16,9 @@ from pydantic import Field, ValidationError
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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.utils.cacheutil import CacheFileStore, cache_in_file
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from akkudoktoreos.utils.datetimeutil import compare_datetimes, to_datetime, to_duration
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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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from statsmodels.tsa.holtwinters import ExponentialSmoothing
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logger = get_logger(__name__)
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@ -65,12 +66,6 @@ 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: NDArray[Shape["24, 8"], float] = Field(
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default=np.full((24, 8), np.nan),
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description="Hourly electricity prices for the last 7 days and today (€/KWh). "
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"A NumPy array of 24 elements, each representing the hourly prices "
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"of the last 7 days (index 0..6, Monday..Sunday) and today (index 7).",
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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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@ -100,54 +95,6 @@ class ElecPriceAkkudoktor(ElecPriceProvider):
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raise ValueError(error_msg)
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return akkudoktor_data
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def historic_hours_min(self) -> int:
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"""Return the minimum historic prediction hours for Akkudoktor electricity price data."""
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return 5 * 7 * 24 # 5 weeks a 7 days a 24 hours
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def _calculate_weighted_mean(self, day_of_week: int, hour: int) -> float:
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"""Calculate the weighted mean price for given day_of_week and hour.
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Args:
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day_of_week (int). The day of week to calculate the mean for (0=Monday..6).
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hour (int): The hour week to calculate the mean for (0..23).
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Returns:
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price_weihgted_mead (float): Weighted mean price for given day_of:week and hour.
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"""
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if np.isnan(self.elecprice_8days_weights_day_of_week[0][0]):
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# Weights not initialized - do now
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# Priority of day: 1=most .. 7=least
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priority_of_day = np.array(
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# Available Prediction days /
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# M,Tu,We,Th,Fr,Sa,Su,Today/ Forecast day_of_week
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[
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[1, 2, 3, 4, 5, 6, 7, 1], # Monday
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[3, 1, 2, 4, 5, 6, 7, 1], # Tuesday
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[4, 2, 1, 3, 5, 6, 7, 1], # Wednesday
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[5, 4, 2, 1, 3, 6, 7, 1], # Thursday
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[5, 4, 3, 2, 1, 6, 7, 1], # Friday
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[7, 6, 5, 4, 2, 1, 3, 1], # Saturday
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[7, 6, 5, 4, 3, 2, 1, 1], # Sunday
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]
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)
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# Take priorities above to decrease relevance in 2s exponential
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self.elecprice_8days_weights_day_of_week = 2 / (2**priority_of_day)
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# Compute the weighted mean for day_of_week and hour
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prices_of_hour = self.elecprice_8days[hour]
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if np.isnan(prices_of_hour).all():
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# No prediction prices available for this hour - use mean value of all prices
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price_weighted_mean = np.nanmean(self.elecprice_marketprice_wh_8day)
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else:
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weights = self.elecprice_8days_weights_day_of_week[day_of_week]
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prices_of_hour_masked: NDArray[Shape["24"]] = np.ma.MaskedArray(
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prices_of_hour, mask=np.isnan(prices_of_hour)
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)
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price_weighted_mean = np.ma.average(prices_of_hour_masked, weights=weights)
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return float(price_weighted_mean)
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@cache_in_file(with_ttl="1 hour")
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def _request_forecast(self) -> AkkudoktorElecPrice:
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"""Fetch electricity price forecast data from Akkudoktor API.
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@ -162,8 +109,9 @@ class ElecPriceAkkudoktor(ElecPriceProvider):
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ValueError: If the API response does not include expected `electricity price` data.
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"""
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source = "https://api.akkudoktor.net"
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# Try to take data from 7 days back for prediction - usually only some hours back are available
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date = to_datetime(self.start_datetime - to_duration("7 days"), as_string="YYYY-MM-DD")
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assert self.start_datetime # mypy fix
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# Try to take data from 5 weeks back for prediction
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date = to_datetime(self.start_datetime - to_duration("35 days"), as_string="YYYY-MM-DD")
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last_date = to_datetime(self.end_datetime, as_string="YYYY-MM-DD")
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url = f"{source}/prices?start={date}&end={last_date}&tz={self.config.timezone}"
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response = requests.get(url)
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@ -174,6 +122,23 @@ class ElecPriceAkkudoktor(ElecPriceProvider):
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self.update_datetime = to_datetime(in_timezone=self.config.timezone)
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return akkudoktor_data
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def cap_outliers(data, sigma=2):
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mean = data.mean()
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std = data.std()
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lower_bound = mean - sigma * std
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upper_bound = mean + sigma * std
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capped_data = data.clip(lower=lower_bound, upper=upper_bound)
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return capped_data
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def predict_ets(
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history,
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seasonal_periods,
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):
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model = ExponentialSmoothing(
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history, seasonal="add", seasonal_periods=seasonal_periods
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).fit()
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return model.forecast(7 * 24)
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def _update_data(self, force_update: Optional[bool] = False) -> None:
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"""Update forecast data in the ElecPriceDataRecord format.
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@ -187,77 +152,34 @@ class ElecPriceAkkudoktor(ElecPriceProvider):
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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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values_len = len(akkudoktor_data.values)
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if values_len < 1:
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# Expect one value set per prediction hour
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raise ValueError(
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f"The forecast must have at least one dataset, "
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f"but only {values_len} data sets are given in forecast data."
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)
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# Get cached 8day values
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elecprice_cache_file = CacheFileStore().get(key="ElecPriceAkkudoktor8dayCache")
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if elecprice_cache_file is None:
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# Cache does not exist - create it
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elecprice_cache_file = CacheFileStore().create(
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key="ElecPriceAkkudoktor8dayCache",
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until_datetime=to_datetime("infinity"),
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suffix=".npy",
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)
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np.save(elecprice_cache_file, self.elecprice_8days)
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elecprice_cache_file.seek(0)
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self.elecprice_8days = np.load(elecprice_cache_file)
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# Get elecprice_charges_kwh_kwh
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charges_kwh = (
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self.config.elecprice_charges_kwh if self.config.elecprice_charges_kwh else 0.0
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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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for i in range(values_len):
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original_datetime = akkudoktor_data.values[i].start
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dt = to_datetime(original_datetime, in_timezone=self.config.timezone)
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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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akkudoktor_value = akkudoktor_data.values[i]
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price_wh = (
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akkudoktor_value.marketpriceEurocentPerKWh / (100 * 1000) + charges_kwh / 1000
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)
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# We provide prediction starting at start of day, to be compatible to old system.
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if compare_datetimes(dt, self.start_datetime.start_of("day")).lt:
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# forecast data is too old - older than start_datetime with time set to 00:00:00
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self.elecprice_8days[dt.hour, dt.day_of_week] = price_wh
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continue
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self.elecprice_8days[dt.hour, 7] = price_wh
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self.update_value(dt, "elecprice_marketprice_wh", price_wh)
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# Update 8day cache
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elecprice_cache_file.seek(0)
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np.save(elecprice_cache_file, self.elecprice_8days)
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# Check for new/ valid forecast data
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if len(self) == 0:
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# Got no valid forecast data
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return
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# Assure price starts at start_time
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while compare_datetimes(self[0].date_time, self.start_datetime).gt:
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# Repeat the mean on the 8 day array to cover the missing hours
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dt = self[0].date_time.subtract(hours=1) # type: ignore
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value = self._calculate_weighted_mean(dt.day_of_week, dt.hour)
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price_wh = akkudoktor_value.marketpriceEurocentPerKWh / (100 * 1000) + charges_wh
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record = ElecPriceDataRecord(
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date_time=dt,
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elecprice_marketprice_wh=value,
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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(0, record)
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# Assure price ends at end_time
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while compare_datetimes(self[-1].date_time, self.end_datetime).lt:
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# Repeat the mean on the 8 day array to cover the missing hours
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dt = self[-1].date_time.add(hours=1) # type: ignore
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value = self._calculate_weighted_mean(dt.day_of_week, dt.hour)
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record = ElecPriceDataRecord(
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date_time=dt,
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elecprice_marketprice_wh=value,
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)
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self.append(record)
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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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# 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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# 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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# depending on the result we check prediction_hours and predict that many hours.
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# we get the result and iterate over it to put it into ElecPriceDataRecord
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