needed_prediction_hours fixed

src/akkudoktoreos/prediction/elecpriceakkudoktor.py

@@ -140,7 +140,7 @@ class ElecPriceAkkudoktor(ElecPriceProvider):
 
     def _update_data(
         self, force_update: Optional[bool] = False
-    ) -> None:  # Tuple[np.ndarray, np.ndarray, np.ndarray]:  # for debug main
+    ) -> None:  # tuple[np.ndarray, np.ndarray, np.ndarray]:
         """Update forecast data in the ElecPriceDataRecord format.
 
         Retrieves data from Akkudoktor, maps each Akkudoktor field to the corresponding
@@ -182,18 +182,21 @@ class ElecPriceAkkudoktor(ElecPriceProvider):
         amount_datasets = len(self.records)
         assert highest_orig_datetime  # mypy fix
 
+        # some of our data is already in the future, so we need to predict less. If we got less data we increase the prediction hours
+        needed_prediction_hours = int(
+            self.config.prediction_hours
+            - ((highest_orig_datetime - self.start_datetime).total_seconds() // 3600)
+        )
         if amount_datasets > 800:  # we do the full ets with seasons of 1 week
             prediction = self._predict_ets(
-                history, seasonal_periods=168, prediction_hours=self.config.prediction_hours
+                history, seasonal_periods=168, prediction_hours=needed_prediction_hours
             )
         elif amount_datasets > 168:  # not enough data to do seasons of 1 week, but enough for 1 day
             prediction = self._predict_ets(
-                history, seasonal_periods=24, prediction_hours=self.config.prediction_hours
+                history, seasonal_periods=24, prediction_hours=needed_prediction_hours
             )
         elif amount_datasets > 0:  # not enough data for ets, do median
-            prediction = self._predict_median(
-                history, prediction_hours=self.config.prediction_hours
-            )
+            prediction = self._predict_median(history, prediction_hours=needed_prediction_hours)
         else:
             logger.error("No data available for prediction")
             raise ValueError("No data available")
@@ -229,7 +232,7 @@ def visualize_predictions(
         label="Predictions",
         color="red",
     )
-    plt.title("Predictions vs True Values for ets")
+    plt.title("Predictions ets")
     plt.xlabel("Time")
     plt.ylabel("Price")
     plt.legend()
@@ -238,6 +241,7 @@ def visualize_predictions(
 
 
 def main() -> None:
+    # Initialize ElecPriceAkkudoktor with required parameters
     elec_price_akkudoktor = ElecPriceAkkudoktor()
     history, history2, predictions = elec_price_akkudoktor._update_data()
 

tests/test_elecpriceakkudoktor.py

@@ -120,7 +120,9 @@ def test_update_data(mock_get, elecprice_provider, sample_akkudoktor_1_json, cac
 
     # Assert: Verify the result is as expected
     mock_get.assert_called_once()
-    assert len(elecprice_provider) == 96
+    assert (
+        len(elecprice_provider) == 73
+    )  # we have 48 datasets in the api response, we want to know 48h into the future. The data we get has already 23h into the future so we need only 25h more. 48+25=73
 
     # Assert we get prediction_hours prioce values by resampling
     np_price_array = elecprice_provider.key_to_array(