Fix seasonal SMARD retail price forecast

src/akkudoktoreos/prediction/elecpriceenergycharts.py

@@ -91,6 +91,10 @@ class ElecPriceEnergyCharts(ElecPriceProvider):
 
     highest_orig_datetime: Optional[datetime] = None
 
+    def historic_hours_min(self) -> int:
+        """Keep enough market-price history for weekly seasonal extrapolation."""
+        return 24 * 35
+
     @classmethod
     def provider_id(cls) -> str:
         """Return the unique identifier for the Energy-Charts provider."""
@@ -246,25 +250,41 @@ class ElecPriceEnergyCharts(ElecPriceProvider):
         if not self.ems_start_datetime:
             raise ValueError(f"Start DateTime not set: {self.ems_start_datetime}")
 
-        # Determine if update is needed and how many days
+        # Determine if an update or history repair is needed and how many days to request.
         past_days = 35
+        needs_history_refresh = False
         if self.highest_orig_datetime:
-            history_series = self.key_to_series(
-                key="elecprice_marketprice_wh", start_datetime=self.ems_start_datetime
+            raw_history = self.key_to_series(
+                key="elecprice_marketprice_wh",
+                end_datetime=to_datetime(self.highest_orig_datetime).add(seconds=1),
             )
-            # If history lower, then start_datetime
-            if history_series.index.min() <= self.ems_start_datetime:
+
+            # Do not mistake the current forecast window for sufficient ETS history. This also
+            # repairs installations that retained only the previous 48-hour default history.
+            if not raw_history.empty:
+                resolution_seconds = self._resolution_seconds(raw_history)
+                slots_per_hour = 3600 // resolution_seconds
+                needs_history_refresh = len(raw_history) <= 800 * slots_per_hour
+            else:
+                needs_history_refresh = True
+
+            if not needs_history_refresh and not force_update:
                 past_days = 0
 
-            needs_update = end > self.highest_orig_datetime
+            needs_update = (
+                bool(force_update) or end > self.highest_orig_datetime or needs_history_refresh
+            )
         else:
             needs_update = True
 
         if needs_update:
             logger.info(
-                "Update {} is needed, last in history: {}",
+                "Update {} is needed, last in history: {}, "
+                "force_update={}, history_refresh={}",
                 self.provider_id(),
                 self.highest_orig_datetime,
+                bool(force_update),
+                needs_history_refresh,
             )
             # Set start_date try to take data from 5 weeks back for prediction
             start_date = to_datetime(

tests/test_elecpriceenergycharts.py

@@ -3,6 +3,7 @@ from pathlib import Path
 from unittest.mock import Mock, patch
 
 import numpy as np
+import pandas as pd
 import pytest
 import requests
 from loguru import logger
@@ -63,6 +64,11 @@ def test_singleton_instance(provider):
     assert provider is another_instance
 
 
+def test_keeps_weekly_price_history(provider):
+    """Retain enough native-resolution values for the weekly ETS forecast."""
+    assert provider.historic_hours_min() == 24 * 35
+
+
 def test_invalid_provider(provider, monkeypatch):
     """Test requesting an unsupported provider."""
     monkeypatch.setenv("EOS_ELECPRICE__ELECPRICE_PROVIDER", "<invalid>")
@@ -159,6 +165,53 @@ def test_update_data_keeps_quarter_hour_resolution(provider):
     assert result.index.to_series().diff().dropna().dt.total_seconds().unique().tolist() == [900.0]
 
 
+def test_update_data_repairs_short_quarter_hour_history(provider):
+    """A previously retained 48-hour series is replaced with the full ETS history."""
+    start = to_datetime("2026-08-01 00:00:00", in_timezone="Europe/Berlin")
+    get_ems().set_start_datetime(start)
+    provider.highest_orig_datetime = start.add(hours=24)
+    short_history = pd.Series(
+        0.0001,
+        index=pd.date_range(start=start.subtract(hours=48), periods=192, freq="15min"),
+    )
+    weekly_history = pd.Series(
+        0.0001,
+        index=pd.date_range(start=start.subtract(days=35), periods=3204, freq="15min"),
+    )
+    refreshed_data = EnergyChartsElecPrice(
+        license_info="",
+        unix_seconds=[int(provider.highest_orig_datetime.timestamp())],
+        price=[100.0],
+        unit="EUR/MWh",
+        deprecated=False,
+    )
+    predicted_slots = provider.config.prediction.hours * 4 - 97
+
+    with (
+        patch.object(
+            ElecPriceEnergyCharts,
+            "key_to_series",
+            side_effect=[short_history, weekly_history],
+        ),
+        patch.object(
+            ElecPriceEnergyCharts, "key_to_array", return_value=weekly_history.to_numpy()
+        ),
+        patch.object(ElecPriceEnergyCharts, "key_from_series"),
+        patch.object(
+            ElecPriceEnergyCharts, "_request_forecast", return_value=refreshed_data
+        ) as request,
+        patch.object(
+            ElecPriceEnergyCharts,
+            "_predict_ets",
+            return_value=np.full(predicted_slots, 0.0001),
+        ) as predict,
+    ):
+        provider._update_data()
+
+    assert request.call_args.kwargs["start_date"] == "2026-06-27"
+    assert predict.call_args.kwargs["seasonal_periods"] == 168 * 4
+
+
 def test_parse_data_adds_constant_charges_variable_network_fees_and_vat(provider):
     """Build the gross retail price from market price and the matching Module 3 fee."""
     provider.config.elecprice.charges_kwh = None