Introduce thermal zones as config subentries

Each temperature sensor used to be its own "room", so a room with two
sensors counted twice and comfort importance was tied to sensors.

zones.py adds Zone: a name, one or more sensors with sensor weights, a
comfort weight, an optional target offset and adjacent zones (made
symmetric). Sensor weights only combine readings; comfort weights only
steer the MPC; neither touches the learned thermal coefficients.

Zones are config subentries (HA's native "Add zone" / edit / delete on
the integration page), with validation that a sensor belongs to one
zone and is not an outdoor source, and a sensor-weight step for
multi-sensor zones. Initial setup still asks for room sensors and
weights and creates one zone per sensor. Configure no longer asks for
rooms or weights.

Config entry version 3. Migrating from 2 turns each room sensor into a
one-sensor zone with its comfort weight and the sensor's entity ID as
the zone key, so learned models, forecast entities (now linked to their
zone, so deleting a zone removes them) and accuracy statistics carry
over. Older versions cannot load a version 3 entry; rolling back needs
the HA backup.

A zone learns only across polls that used the same sensors (a sensor
dropping out shifts the mean by its offset); the new poll outcome
"sensors_changed" reports this. Status renames: input_quality.zones
(per-sensor detail nested), excluded_zones, retired_zone_models; adds
zone_names. Invalid stored comfort weights now fall back to 1.0, so the
invalid_weights status is gone.

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

tests/ha/test_lifecycle.py

@@ -10,6 +10,21 @@ from homeassistant.helpers import entity_registry as er
 from homeassistant.helpers.storage import Store
 
 from custom_components.heating_mpc.const import DOMAIN
+from homeassistant.config_entries import ConfigSubentry
+from types import MappingProxyType
+
+
+def zone_subentry(entry, key):
+    return next(sub for sub in entry.subentries.values() if sub.unique_id == key)
+
+
+def add_zone(hass, entry, key, sensors, name=None):
+    """What the Add zone flow stores, added directly."""
+    hass.config_entries.async_add_subentry(entry, ConfigSubentry(
+        data=MappingProxyType({"key": key, "name": name or key, "sensors": sensors,
+                               "sensor_weights": {s: 1.0 for s in sensors}, "comfort_weight": 1.0,
+                               "target_offset": 0.0, "neighbors": []}),
+        subentry_type="zone", title=name or key, unique_id=key))
 
 
 async def setup(hass, entry):
@@ -28,7 +43,8 @@ async def test_setup_reload_unload(hass, entry, inputs):
     ids = {e.unique_id: e.entity_id for e in entities}
     assert coordinator.data["status"] == "learning"
     status = hass.states.get(ids[f"{entry.entry_id}_status"])
-    assert status.attributes["input_quality"]["rooms"]["sensor.room"]["max_age_seconds_24h"] == 0
+    zone_quality = status.attributes["input_quality"]["zones"]["sensor.room"]
+    assert zone_quality["sensors"]["sensor.room"]["max_age_seconds_24h"] == 0
     # Volatile diagnostics stay visible but are not written to the Recorder.
     assert {"input_quality", "weather_comparison"} <= status.state_info["unrecorded_attributes"]
     assert "models" not in status.state_info["unrecorded_attributes"]
@@ -45,16 +61,19 @@ async def test_setup_reload_unload(hass, entry, inputs):
     assert coordinator._unsub_refresh is None
 
 
-async def test_options_reload_removes_only_old_room(hass, entry, inputs):
+async def test_replacing_a_zone_removes_only_its_forecast(hass, entry, inputs):
     await setup(hass, entry)
     registry = er.async_get(hass)
     old = registry.async_get_entity_id("sensor", DOMAIN, f"{entry.entry_id}_sensor.room_predicted_60min")
     hass.states.async_set("sensor.new", "21", {"unit_of_measurement": "°C"})
-    hass.config_entries.async_update_entry(entry, options={"room_entities": ["sensor.new"]})
+    add_zone(hass, entry, "zone_new", ["sensor.new"], "New")
     await hass.async_block_till_done()
-    assert hass.data[DOMAIN][entry.entry_id].rooms == ["sensor.new"]
+    assert hass.config_entries.async_remove_subentry(entry, zone_subentry(entry, "sensor.room").subentry_id)
+    await hass.async_block_till_done()
+    assert hass.data[DOMAIN][entry.entry_id].rooms == ["zone_new"]
     assert registry.async_get(old) is None
-    assert registry.async_get_entity_id("sensor", DOMAIN, f"{entry.entry_id}_sensor.new_predicted_60min")
+    new = registry.async_get_entity_id("sensor", DOMAIN, f"{entry.entry_id}_zone_new_predicted_60min")
+    assert hass.states.get(new).attributes["friendly_name"] == "MPC New 60min Prediction"
     assert len(er.async_entries_for_config_entry(registry, entry.entry_id)) == 11
 
 
@@ -203,7 +222,9 @@ async def test_removed_room_model_survives_options_round_trip(hass, entry, input
         "learners": {"sensor.room": Learner(count=250, on_count=125, off_count=125).as_dict()}}}
     hass.states.async_set("sensor.new", "21", {"unit_of_measurement": "°C"})
     await setup(hass, entry)
-    hass.config_entries.async_update_entry(entry, options={"room_entities": ["sensor.new"]})
+    add_zone(hass, entry, "sensor.new", ["sensor.new"])
+    await hass.async_block_till_done()
+    assert hass.config_entries.async_remove_subentry(entry, zone_subentry(entry, "sensor.room").subentry_id)
     await hass.async_block_till_done()
     future = dt_util.utcnow() + timedelta(minutes=5, seconds=1)
     freezer.move_to(future)
@@ -212,8 +233,7 @@ async def test_removed_room_model_survives_options_round_trip(hass, entry, input
     saved = hass_storage[key]["data"]
     assert set(saved["learners"]) == {"sensor.new"}
     assert saved["retired_learners"]["sensor.room"]["count"] == 250
-    hass.config_entries.async_update_entry(
-        entry, options={"room_entities": ["sensor.room", "sensor.new"]})
+    add_zone(hass, entry, "sensor.room", ["sensor.room"])
     await hass.async_block_till_done()
     assert hass.data[DOMAIN][entry.entry_id].learners["sensor.room"].count == 250
 
@@ -234,9 +254,9 @@ async def test_forecast_error_sensors_score_scheduled_polls(hass, entry, inputs,
     assert coordinator.data["forecast_accuracy"][15]["samples"] == 1
     assert float(state.state) == coordinator.data["forecast_accuracy"][15]["mae"] > 0
     assert state.attributes["samples"] == 1
-    assert state.attributes["rooms"]["sensor.room"]["samples"] == 1
+    assert state.attributes["zones"]["room"]["samples"] == 1  # by zone name
     assert state.attributes["unit_of_measurement"] == "°C"
-    assert "rooms" in state.state_info["unrecorded_attributes"]
+    assert "zones" in state.state_info["unrecorded_attributes"]
     assert hass.states.get(ids[30]).state == "unknown"