Recalibrate only the model parts whose configuration changed

Confirmed: learned models were matched only by zone key, so replacing a
zone's sensors, changing sensor weights, moving or replacing a heat
source or turning a window kept coefficients learned for a different
configuration, silently.

compatibility.py fingerprints exactly what each learned component
depends on: the zone measurement (sensors and relative weights; equal
scaling changes nothing), each zone's heat sources, the neighbour's
measurement, the zone's windows, window orientation (direction term
only), the recovery time (recovery only) and other zones' openings
(cross term only). Comfort weight, target offset and names are not
included. Heat-source baselines have their own fingerprint (sensor and
included sources).

On restore, a changed measurement recalibrates the zone: base and
extended loss/heat/bias keep their values as a warm start, but their
uncertainty, counts and readiness start over, other terms return to the
prior, and the zone's forecast statistics are dropped. Otherwise only
the changed terms are reset; everything else, including other zones,
is kept. Each recalibration is logged (Status model_changes, info log)
and saved at once. Data from before fingerprints (0.4.0) is assumed to
match its current configuration and is fingerprinted at the first
start, so the next edit is detected even before a learned sample.

Tests cover comfort changes, sensor replacement and weights, window
orientation and size, moved and replaced heat sources, re-added zones,
0.4.0 data and a second restart, plus an HA Core test through real
subentry updates.

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

custom_components/heating_mpc/evaluation.py

@@ -174,6 +174,14 @@ class ForecastEvaluator:
                                "rooms": {room: _stats(values) for room, values in per_room.items()}}
         return result
 
+    def forget(self, room: str) -> None:
+        """Drop a zone's statistics, for example when its temperature is measured differently."""
+        for by_horizon in self._days.values():
+            for by_room in by_horizon.values():
+                by_room.pop(room, None)
+        for poll in self._history:
+            poll["temps"].pop(room, None)
+
     def as_dict(self) -> dict[str, Any]:
         # Rounded copy: shorter JSON on every save; in-memory sums stay exact.
         return {"days": {day: {horizon: {room: [v[0], *(round(x, 6) for x in v[1:])]