Discard predictions only when their inputs change

A finished plan was discarded whenever any watched State object changed
during the save or calculation, including diagnostic-only weather
sensors and the thermostat's current_temperature/hvac_action. The
proposed-heat entity then went unavailable for five minutes without
cause. Compare only the values the prediction uses: room and outdoor
temperature states and units, thermostat mode and target, and relay
state and last change.

Also pass config_entry to the coordinator explicitly instead of relying
on HA's ContextVar fallback, which HA reports as deprecated usage.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

custom_components/heating_mpc/coordinator.py

@@ -32,7 +32,8 @@ class HeatingMPCCoordinator(DataUpdateCoordinator[dict[str, Any]]):
     """The existing generic thermostat stays the only actuator owner."""
 
     def __init__(self, hass: HomeAssistant, entry: ConfigEntry) -> None:
-        super().__init__(hass, _LOGGER, name=DOMAIN, update_interval=timedelta(minutes=5))
+        super().__init__(hass, _LOGGER, config_entry=entry, name=DOMAIN,
+                         update_interval=timedelta(minutes=5))
         self.entry = entry
         self.config = {**DEFAULTS, **entry.data, **entry.options}
         self.rooms: list[str] = self.config[CONF_ROOMS]
@@ -106,6 +107,23 @@ class HeatingMPCCoordinator(DataUpdateCoordinator[dict[str, Any]]):
         self._lag = {"time": now, "heater_on": heater_on, "lag": lag, "uncertainty": uncertainty}
         return steady
 
+    def _prediction_inputs(self) -> tuple:
+        """Values the prediction depends on, used to detect changes during calculation."""
+        heater = self.config[CONF_HEATER]
+        entities = sorted(set(self.rooms) | set(sources_for(self.config, "temperature")) |
+                          {self.config[CONF_CLIMATE], heater})
+        values = []
+        for entity_id in entities:
+            state = self.hass.states.get(entity_id)
+            if state is None:
+                values.append((entity_id, None))
+                continue
+            attrs = state.attributes
+            values.append((entity_id, state.state, attrs.get("unit_of_measurement"),
+                           attrs.get("temperature"), attrs.get("temperature_unit"),
+                           state.last_changed if entity_id == heater else None))
+        return tuple(values)
+
     async def _async_update_data(self) -> dict[str, Any]:
         now = datetime.now(timezone.utc)
         heater = self.hass.states.get(self.config[CONF_HEATER])
@@ -116,9 +134,9 @@ class HeatingMPCCoordinator(DataUpdateCoordinator[dict[str, Any]]):
             entity_id for metric in WEATHER_SOURCES
             for entity_id in sources_for(self.config, metric)
         }
-        watched = set(self.rooms) | weather_entity_ids | {
-            self.config[CONF_CLIMATE], self.config[CONF_HEATER]}
-        snapshot = {entity: self.hass.states.get(entity) for entity in watched}
+        snapshot = {entity: self.hass.states.get(entity)
+                    for entity in set(self.rooms) | weather_entity_ids}
+        inputs_before = self._prediction_inputs()
         room_quality = {}
         temps = {}
         for room in self.rooms:
@@ -273,10 +291,11 @@ class HeatingMPCCoordinator(DataUpdateCoordinator[dict[str, Any]]):
                 heating_cost=float(self.config[CONF_HEAT_COST]),
                 comfort_band=float(self.config[CONF_BAND]),
             )
-            # HA State objects are immutable snapshots. Discard a result if any
-            # input changed while the worker was calculating the hypothetical plan.
+            # Discard a result if a value used by the prediction changed while
+            # saving or while the worker calculated the hypothetical plan. Optional
+            # diagnostic weather sensors are not prediction inputs.
             output = await self.hass.async_add_executor_job(job)
-            if any(self.hass.states.get(entity) is not state for entity, state in snapshot.items()):
+            if self._prediction_inputs() != inputs_before:
                 status["status"] = "inputs_changed"
                 return status
         except (ValueError, RuntimeError, OverflowError) as err: