diff --git a/src/akkudoktoreos/prediction/elecpricetibber.py b/src/akkudoktoreos/prediction/elecpricetibber.py
index b2b810b..aade5f5 100644
--- a/src/akkudoktoreos/prediction/elecpricetibber.py
+++ b/src/akkudoktoreos/prediction/elecpricetibber.py
@@ -16,6 +16,8 @@ from akkudoktoreos.prediction.elecpriceabc import ElecPriceProvider
 from akkudoktoreos.utils.datetimeutil import to_datetime, to_duration
 
 TIBBER_GRAPHQL_URL = "https://api.tibber.com/v1-beta/gql"
+TIBBER_DAILY_SEASONAL_HOURS = 24 * 7
+TIBBER_WEEKLY_SEASONAL_HOURS = 24 * 35
 TIBBER_PRICE_QUERY = """
 query TibberPriceInfo {
   viewer {
@@ -159,6 +161,23 @@ class ElecPriceTibber(ElecPriceProvider):
             raise ValueError(error_msg)
         return tibber_data
 
+    def _api_price_counts(self, response: TibberGraphQLResponse) -> tuple[int, int, int]:
+        """Return Tibber API price counts for history, today, and tomorrow."""
+        home = self._select_home(response)
+        subscription = home.currentSubscription
+        if subscription is None:
+            raise ValueError("Tibber home has no current subscription")
+
+        history_count = 0
+        today_count = 0
+        tomorrow_count = 0
+        if subscription.priceInfoRange is not None:
+            history_count = len(subscription.priceInfoRange.nodes)
+        if subscription.priceInfo is not None:
+            today_count = len(subscription.priceInfo.today)
+            tomorrow_count = len(subscription.priceInfo.tomorrow)
+        return history_count, today_count, tomorrow_count
+
     @cache_in_file(with_ttl="1 hour")
     def _request_forecast(self) -> TibberGraphQLResponse:
         """Fetch electricity price data from the Tibber GraphQL API."""
@@ -253,12 +272,44 @@ class ElecPriceTibber(ElecPriceProvider):
         clean_history = self._cap_outliers(history)
         return np.full(hours, np.median(clean_history))
 
+    def _predict_missing_prices(self, history: np.ndarray, hours: int) -> np.ndarray:
+        """Forecast missing future prices from the available hourly history."""
+        numeric_history = np.asarray(history, dtype=float)
+        numeric_history = numeric_history[np.isfinite(numeric_history)]
+        history_hours = len(numeric_history)
+
+        if history_hours > TIBBER_WEEKLY_SEASONAL_HOURS:
+            logger.info(
+                "Using weekly seasonal ETS forecast for Tibber electricity prices "
+                "with {} historical hourly values.",
+                history_hours,
+            )
+            return self._predict_ets(numeric_history, seasonal_periods=168, hours=hours)
+        if history_hours > TIBBER_DAILY_SEASONAL_HOURS:
+            logger.info(
+                "Using daily seasonal ETS forecast for Tibber electricity prices "
+                "with {} historical hourly values.",
+                history_hours,
+            )
+            return self._predict_ets(numeric_history, seasonal_periods=24, hours=hours)
+        if history_hours > 0:
+            logger.warning(
+                "Using median fallback for Tibber electricity prices because only {} "
+                "historical hourly values are available.",
+                history_hours,
+            )
+            return self._predict_median(numeric_history, hours=hours)
+
+        logger.error("No data available for prediction")
+        raise ValueError("No data available")
+
     def _update_data(self, force_update: Optional[bool] = False) -> None:
         """Update Tibber price data and extrapolate missing future prices."""
         tibber_data = self._request_forecast(force_update=force_update)  # type: ignore
         if not self.ems_start_datetime:
             raise ValueError(f"Start DateTime not set: {self.ems_start_datetime}")
 
+        api_history_count, api_today_count, api_tomorrow_count = self._api_price_counts(tibber_data)
         series_data = self._hourly_series(self._parse_data(tibber_data))
         if series_data.empty:
             raise ValueError("Tibber response contains no usable hourly price points")
@@ -272,7 +323,6 @@ class ElecPriceTibber(ElecPriceProvider):
             fill_method="linear",
         )
 
-        amount_datasets = len(self.records)
         if not highest_orig_datetime:
             error_msg = f"Highest original datetime not available: {highest_orig_datetime}"
             logger.error(error_msg)
@@ -293,15 +343,16 @@ class ElecPriceTibber(ElecPriceProvider):
             )
             return
 
-        if amount_datasets > 800:
-            prediction = self._predict_ets(history, seasonal_periods=168, hours=needed_hours)
-        elif amount_datasets > 168:
-            prediction = self._predict_ets(history, seasonal_periods=24, hours=needed_hours)
-        elif amount_datasets > 0:
-            prediction = self._predict_median(history, hours=needed_hours)
-        else:
-            logger.error("No data available for prediction")
-            raise ValueError("No data available")
+        logger.info(
+            "Tibber electricity price input: api_history_hours={}, api_today_hours={}, "
+            "api_tomorrow_hours={}, combined_history_hours={}, needed_forecast_hours={}.",
+            api_history_count,
+            api_today_count,
+            api_tomorrow_count,
+            len(history),
+            needed_hours,
+        )
+        prediction = self._predict_missing_prices(history, hours=needed_hours)
 
         prediction_series = pd.Series(
             data=prediction,
diff --git a/tests/test_elecpricetibber.py b/tests/test_elecpricetibber.py
index 556d985..5fc2353 100644
--- a/tests/test_elecpricetibber.py
+++ b/tests/test_elecpricetibber.py
@@ -30,6 +30,7 @@ def _tibber_payload(
     *,
     home_id: str = "home-1",
     include_other_home: bool = False,
+    include_history_range: bool = True,
 ) -> dict[str, object]:
     homes: list[dict[str, object]] = []
     if include_other_home:
@@ -42,15 +43,11 @@ def _tibber_payload(
             }
         )
 
-    homes.append(
-        {
-            "id": home_id,
-            "currentSubscription": {
-                "priceInfo": {"today": prices[:2], "tomorrow": prices[2:]},
-                "priceInfoRange": {"nodes": prices},
-            },
-        }
-    )
+    subscription: dict[str, object] = {"priceInfo": {"today": prices[:2], "tomorrow": prices[2:]}}
+    if include_history_range:
+        subscription["priceInfoRange"] = {"nodes": prices}
+
+    homes.append({"id": home_id, "currentSubscription": subscription})
     return {"data": {"viewer": {"homes": homes}}}
 
 
@@ -266,3 +263,39 @@ def test_tibber_update_extrapolates_missing_hours_with_seasonal_history(
     )
 
     assert prices.tolist() == pytest.approx([0.0003, 0.00042, 0.00036, 0.0005, 0.0005, 0.0005])
+
+
+def test_tibber_update_uses_eos_storage_history_when_api_history_is_missing(
+    tibber_provider, monkeypatch
+):
+    """Stored EOS price history can provide enough data for weekly seasonal ETS."""
+    data = TibberGraphQLResponse.model_validate(
+        _tibber_payload(
+            [
+                _price("2026-07-09T00:00:00+00:00", 0.30),
+                _price("2026-07-09T01:00:00+00:00", 0.42),
+                _price("2026-07-09T02:00:00+00:00", 0.36),
+            ],
+            include_history_range=False,
+        )
+    )
+    monkeypatch.setattr(tibber_provider, "_request_forecast", lambda **_: data)
+    forecast_call = {}
+
+    def fake_predict_ets(history, seasonal_periods, hours):
+        forecast_call["seasonal_periods"] = seasonal_periods
+        forecast_call["history_hours"] = len(history)
+        return np.full(hours, 0.0007)
+
+    monkeypatch.setattr(tibber_provider, "_predict_ets", fake_predict_ets)
+
+    stored_history = pd.Series(
+        data=np.linspace(0.0002, 0.0004, 900),
+        index=pd.date_range("2026-06-01T00:00:00+00:00", periods=900, freq="1h"),
+    )
+    tibber_provider.key_from_series("elecprice_marketprice_wh", stored_history)
+
+    tibber_provider._update_data(force_update=True)
+
+    assert forecast_call["seasonal_periods"] == 168
+    assert forecast_call["history_hours"] > 840