initial commit

src/akkudoktoreos/prediction/elecpriceakkudoktor.py

@@ -16,8 +16,9 @@ from pydantic import Field, ValidationError
 from akkudoktoreos.core.logging import get_logger
 from akkudoktoreos.core.pydantic import PydanticBaseModel
 from akkudoktoreos.prediction.elecpriceabc import ElecPriceDataRecord, ElecPriceProvider
-from akkudoktoreos.utils.cacheutil import CacheFileStore, cache_in_file
+from akkudoktoreos.utils.cacheutil import cache_in_file
-from akkudoktoreos.utils.datetimeutil import compare_datetimes, to_datetime, to_duration
+from akkudoktoreos.utils.datetimeutil import to_datetime, to_duration
+from statsmodels.tsa.holtwinters import ExponentialSmoothing
 
 logger = get_logger(__name__)
 
@@ -65,12 +66,6 @@ class ElecPriceAkkudoktor(ElecPriceProvider):
         _update_data(): Processes and updates forecast data from Akkudoktor in ElecPriceDataRecord format.
     """
 
-    elecprice_8days: NDArray[Shape["24, 8"], float] = Field(
-        default=np.full((24, 8), np.nan),
-        description="Hourly electricity prices for the last 7 days and today (€/KWh). "
-        "A NumPy array of 24 elements, each representing the hourly prices "
-        "of the last 7 days (index 0..6, Monday..Sunday) and today (index 7).",
-    )
     elecprice_8days_weights_day_of_week: NDArray[Shape["7, 8"], float] = Field(
         default=np.full((7, 8), np.nan),
         description="Daily electricity price weights for the last 7 days and today. "
@@ -100,54 +95,6 @@ class ElecPriceAkkudoktor(ElecPriceProvider):
             raise ValueError(error_msg)
         return akkudoktor_data
 
-    def historic_hours_min(self) -> int:
-        """Return the minimum historic prediction hours for Akkudoktor electricity price data."""
-        return 5 * 7 * 24  # 5 weeks a 7 days a 24 hours
-
-    def _calculate_weighted_mean(self, day_of_week: int, hour: int) -> float:
-        """Calculate the weighted mean price for given day_of_week and hour.
-
-        Args:
-            day_of_week (int). The day of week to calculate the mean for (0=Monday..6).
-            hour (int): The hour  week to calculate the mean for (0..23).
-
-        Returns:
-            price_weihgted_mead (float): Weighted mean price for given day_of:week and hour.
-        """
-        if np.isnan(self.elecprice_8days_weights_day_of_week[0][0]):
-            # Weights not initialized - do now
-
-            # Priority of day: 1=most .. 7=least
-            priority_of_day = np.array(
-                #    Available Prediction days /
-                #    M,Tu,We,Th,Fr,Sa,Su,Today/ Forecast day_of_week
-                [
-                    [1, 2, 3, 4, 5, 6, 7, 1],  # Monday
-                    [3, 1, 2, 4, 5, 6, 7, 1],  # Tuesday
-                    [4, 2, 1, 3, 5, 6, 7, 1],  # Wednesday
-                    [5, 4, 2, 1, 3, 6, 7, 1],  # Thursday
-                    [5, 4, 3, 2, 1, 6, 7, 1],  # Friday
-                    [7, 6, 5, 4, 2, 1, 3, 1],  # Saturday
-                    [7, 6, 5, 4, 3, 2, 1, 1],  # Sunday
-                ]
-            )
-            # Take priorities above to decrease relevance in 2s exponential
-            self.elecprice_8days_weights_day_of_week = 2 / (2**priority_of_day)
-
-        # Compute the weighted mean for day_of_week and hour
-        prices_of_hour = self.elecprice_8days[hour]
-        if np.isnan(prices_of_hour).all():
-            # No prediction prices available for this hour - use mean value of all prices
-            price_weighted_mean = np.nanmean(self.elecprice_marketprice_wh_8day)
-        else:
-            weights = self.elecprice_8days_weights_day_of_week[day_of_week]
-            prices_of_hour_masked: NDArray[Shape["24"]] = np.ma.MaskedArray(
-                prices_of_hour, mask=np.isnan(prices_of_hour)
-            )
-            price_weighted_mean = np.ma.average(prices_of_hour_masked, weights=weights)
-
-        return float(price_weighted_mean)
-
     @cache_in_file(with_ttl="1 hour")
     def _request_forecast(self) -> AkkudoktorElecPrice:
         """Fetch electricity price forecast data from Akkudoktor API.
@@ -162,8 +109,9 @@ class ElecPriceAkkudoktor(ElecPriceProvider):
             ValueError: If the API response does not include expected `electricity price` data.
         """
         source = "https://api.akkudoktor.net"
-        # Try to take data from 7 days back for prediction - usually only some hours back are available
+        assert self.start_datetime  # mypy fix
-        date = to_datetime(self.start_datetime - to_duration("7 days"), as_string="YYYY-MM-DD")
+        # 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.timezone}"
         response = requests.get(url)
@@ -174,6 +122,23 @@ class ElecPriceAkkudoktor(ElecPriceProvider):
         self.update_datetime = to_datetime(in_timezone=self.config.timezone)
         return akkudoktor_data
 
+        def cap_outliers(data, sigma=2):
+            mean = data.mean()
+            std = data.std()
+            lower_bound = mean - sigma * std
+            upper_bound = mean + sigma * std
+            capped_data = data.clip(lower=lower_bound, upper=upper_bound)
+            return capped_data
+
+        def predict_ets(
+            history,
+            seasonal_periods,
+        ):
+            model = ExponentialSmoothing(
+                history, seasonal="add", seasonal_periods=seasonal_periods
+            ).fit()
+            return model.forecast(7 * 24)
+
     def _update_data(self, force_update: Optional[bool] = False) -> None:
         """Update forecast data in the ElecPriceDataRecord format.
 
@@ -187,77 +152,34 @@ class ElecPriceAkkudoktor(ElecPriceProvider):
 
         # Assumption that all lists are the same length and are ordered chronologically
         # in ascending order and have the same timestamps.
-        values_len = len(akkudoktor_data.values)
-        if values_len < 1:
-            # Expect one value set per prediction hour
-            raise ValueError(
-                f"The forecast must have at least one dataset, "
-                f"but only {values_len} data sets are given in forecast data."
-            )
-
-        # Get cached 8day values
-        elecprice_cache_file = CacheFileStore().get(key="ElecPriceAkkudoktor8dayCache")
-        if elecprice_cache_file is None:
-            # Cache does not exist - create it
-            elecprice_cache_file = CacheFileStore().create(
-                key="ElecPriceAkkudoktor8dayCache",
-                until_datetime=to_datetime("infinity"),
-                suffix=".npy",
-            )
-            np.save(elecprice_cache_file, self.elecprice_8days)
-        elecprice_cache_file.seek(0)
-        self.elecprice_8days = np.load(elecprice_cache_file)
 
         # Get elecprice_charges_kwh_kwh
-        charges_kwh = (
+        charges_wh = (
-            self.config.elecprice_charges_kwh if self.config.elecprice_charges_kwh else 0.0
+            self.config.elecprice_charges_kwh / 1000 if self.config.elecprice_charges_kwh else 0.0
         )
+        assert self.start_datetime  # mypy fix
 
-        for i in range(values_len):
+        for akkudoktor_value in akkudoktor_data.values:
-            original_datetime = akkudoktor_data.values[i].start
+            orig_datetime = to_datetime(akkudoktor_value.start, in_timezone=self.config.timezone)
-            dt = to_datetime(original_datetime, in_timezone=self.config.timezone)
 
-            akkudoktor_value = akkudoktor_data.values[i]
+            price_wh = akkudoktor_value.marketpriceEurocentPerKWh / (100 * 1000) + charges_wh
-            price_wh = (
-                akkudoktor_value.marketpriceEurocentPerKWh / (100 * 1000) + charges_kwh / 1000
-            )
-
-            # We provide prediction starting at start of day, to be compatible to old system.
-            if compare_datetimes(dt, self.start_datetime.start_of("day")).lt:
-                # forecast data is too old - older than start_datetime with time set to 00:00:00
-                self.elecprice_8days[dt.hour, dt.day_of_week] = price_wh
-                continue
-            self.elecprice_8days[dt.hour, 7] = price_wh
-
-            self.update_value(dt, "elecprice_marketprice_wh", price_wh)
-
-        # Update 8day cache
-        elecprice_cache_file.seek(0)
-        np.save(elecprice_cache_file, self.elecprice_8days)
-
-        # Check for new/ valid forecast data
-        if len(self) == 0:
-            # Got no valid forecast data
-            return
-
-        # Assure price starts at start_time
-        while compare_datetimes(self[0].date_time, self.start_datetime).gt:
-            # Repeat the mean on the 8 day array to cover the missing hours
-            dt = self[0].date_time.subtract(hours=1)  # type: ignore
-            value = self._calculate_weighted_mean(dt.day_of_week, dt.hour)
 
             record = ElecPriceDataRecord(
-                date_time=dt,
+                date_time=orig_datetime,
-                elecprice_marketprice_wh=value,
+                elecprice_marketprice_wh=price_wh,
             )
-            self.insert(0, record)
+            self.insert(
-        # Assure price ends at end_time
+                0, record
-        while compare_datetimes(self[-1].date_time, self.end_datetime).lt:
+            )  # idk what happens if the date is already there. try except update?
-            # Repeat the mean on the 8 day array to cover the missing hours
+
-            dt = self[-1].date_time.add(hours=1)  # type: ignore
+        # now we check if we have data newer than the last from the api. if so thats old prediction. we delete them all.
-            value = self._calculate_weighted_mean(dt.day_of_week, dt.hour)
+
-            record = ElecPriceDataRecord(
+        # now we count how many data points we have.
-                date_time=dt,
+        # if its > 800 (5 weeks) we will use EST
-                elecprice_marketprice_wh=value,
+        # elif > idk maybe 168 (1 week) we use EST without season
-            )
+        # elif < 168 we use a simple median
-            self.append(record)
+        # #elif == 0 we need some static value from the config
+
+        # depending on the result we check prediction_hours and predict that many hours.
+
+        # we get the result and iterate over it to put it into ElecPriceDataRecord