Leave out unusable rooms instead of pausing; add keep-last-value option

A single stale, unavailable, missing or invalid room sensor paused
learning and forecasts for every room. That is not acceptable once the
MPC may control heating.

Now such a room is left out: the weighted temperature, learning and the
optimizer use the remaining rooms with their weights re-normalized, and
a room learns only across poll pairs where it was usable at both ends.
Readiness considers only the rooms used. Everything pauses only when no
room is usable or a shared input (relay, thermostat, outdoor
temperature) is missing.

A new setup/Configure checkbox, room_stale_use_last_value (default off),
keeps using a stale room sensor's last value instead, for sensors that
report only on change. It never applies to unavailable or invalid
sensors, or to a sensor silent for more than 24 hours.

Status adds excluded_rooms, weight_coverage and a per-room "used" flag.
English and Romanian labels and help text added.

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

README.md

@@ -74,7 +74,10 @@ is available only while it is `ready`. Useful attributes:
   `invalid_timestamp`, `future_timestamp` or `invalid_value_or_unit`),
   `age_seconds` (time since the sensor last reported to HA) and
   `max_age_seconds_24h` (largest age seen at polls in the last 24 hours; resets on
-  reload and can read up to one poll, 5 minutes, low).
+  reload and can read up to one poll, 5 minutes, low). Rooms also have `used`:
+  whether that room's reading was used in this poll.
+- `excluded_rooms` and `weight_coverage` — rooms left out of this poll, and the
+  share of the total comfort weight still covered by the rooms used (1.0 = all).
 - `heat_lag` and `heat_lag_uncertainty` — the radiator-lag estimate (0 cold, 1 fully
   warm) and its error bound. Learning waits while the bound is above 0.1, for
   example for about 30 minutes after an HA restart.
@@ -91,18 +94,31 @@ written to the Recorder; the other attributes are.
 ### Choosing freshness limits
 
 Room and weather report-age limits are set during setup and in **Configure**
-(5–1440 minutes; defaults 30 for rooms and 120 for weather). A room sensor older
-than its limit pauses learning and forecasts for **all** rooms until it reports
-again. A stale outdoor source falls back to the next one in its list; optional
-weather metrics never block. Recovery starts a new sampling interval, so nothing is
-learned across an outage.
+(5–1440 minutes; defaults 30 for rooms and 120 for weather).
 
-Within the limit, the last reading counts as current, so a too-generous limit lets
-a frozen value be learned for a while. A too-tight limit pauses the integration
-whenever a sensor is quietly between reports. Freshness uses HA's `last_reported`:
-the time a sensor last reported to HA, even with an unchanged value. It is not the
-age of the physical measurement, so a provider re-publishing cached data looks
-fresh, and sensors that report only on change can look stale in a steady room.
+A room sensor that is older than its limit, unavailable, missing or invalid is
+**left out**: the weighted temperature, learning and forecasts continue with the
+other rooms, whose weights are re-normalized. Status shows it in `excluded_rooms`.
+Learning and forecasts pause only when no room is usable, or when the relay,
+thermostat or outdoor temperature is missing. A room that reports again learns
+from its second poll onwards, so nothing is learned across its gap.
+
+The checkbox **Keep using the last value of a room sensor that stopped reporting**
+changes this for stale sensors: their last value is still used, for up to 24 hours
+without a report. Use it for sensors that report only when the value changes.
+Unavailable or invalid sensors are always left out, and so is any sensor silent
+for more than 24 hours, since it is most likely dead.
+
+A stale outdoor source falls back to the next one in its list; optional weather
+metrics never block.
+
+Within the limit (or with the checkbox on), the last reading counts as current,
+so a frozen value can be learned for a while. A too-tight limit (with the checkbox
+off) drops a room whenever its sensor is quietly between reports. Freshness uses
+HA's `last_reported`: the time a sensor last reported to HA, even with an unchanged
+value. It is not the age of the physical measurement, so a provider re-publishing
+cached data looks fresh, and sensors that report only on change can look stale in
+a steady room.
 
 After a day of running, set each limit comfortably above (about 1.5–2×) the largest
 `max_age_seconds_24h` among the sensors it covers. The relay and thermostat have no

custom_components/heating_mpc/config_flow.py

@@ -9,7 +9,7 @@ from homeassistant.core import callback
 from homeassistant.helpers import selector
 
 from .const import (
-    CONF_ROOM_MAX_AGE, CONF_WEATHER_MAX_AGE,
+    CONF_ROOM_MAX_AGE, CONF_ROOM_STALE_KEEP, CONF_WEATHER_MAX_AGE,
     DOMAIN, CONF_CLIMATE, CONF_HEATER, CONF_OUTSIDE, CONF_ROOMS,
     CONF_WEIGHTS, CONF_HORIZON, CONF_MIN_ON, CONF_MIN_OFF,
     CONF_SWITCH_COST, CONF_HEAT_COST, CONF_BAND, DEFAULTS,
@@ -41,6 +41,8 @@ def _main_schema(config: dict[str, Any] | None = None, detailed: bool = False):
     for key in (CONF_ROOM_MAX_AGE, CONF_WEATHER_MAX_AGE):
         schema[vol.Required(key, default=data.get(key, DEFAULTS[key]))] = vol.All(
             vol.Coerce(int), vol.Range(min=5, max=1440))
+    schema[vol.Required(CONF_ROOM_STALE_KEEP, default=bool(
+        data.get(CONF_ROOM_STALE_KEEP, DEFAULTS[CONF_ROOM_STALE_KEEP])))] = selector.BooleanSelector()
     if detailed:
         for key, allowed in {
             CONF_HORIZON: vol.In([60, 90, 120, 150, 180]),

custom_components/heating_mpc/const.py

@@ -12,6 +12,9 @@ CONF_SWITCH_COST = "switching_cost"
 CONF_HEAT_COST = "heating_cost"
 CONF_ROOM_MAX_AGE = "room_max_age_minutes"
 CONF_WEATHER_MAX_AGE = "weather_max_age_minutes"
+# Stale room sensors: False leaves them out (re-weighting the other rooms),
+# True keeps using their last value for sensors that report only on change.
+CONF_ROOM_STALE_KEEP = "room_stale_use_last_value"
 CONF_BAND = "comfort_band"
 
 # Each source list is ordered: first valid sensor wins; remaining are fallbacks.
@@ -32,6 +35,7 @@ WEATHER_EXTRA = ("humidity", "feels_like", "pressure", "visibility", "rain", "sn
 DEFAULTS = {
     CONF_ROOM_MAX_AGE: 30,
     CONF_WEATHER_MAX_AGE: 120,
+    CONF_ROOM_STALE_KEEP: False,
     CONF_HORIZON: 120,
     CONF_MIN_ON: 15,
     CONF_MIN_OFF: 10,
@@ -39,5 +43,7 @@ DEFAULTS = {
     CONF_HEAT_COST: 0.025,
     CONF_BAND: 0.15,
 }
+# A room sensor silent this long is treated as dead even when keeping last values.
+ROOM_STALE_KEEP_MAX_MINUTES = 24 * 60
 SAMPLE_MINUTES = 5
 BLOCK_MINUTES = 15

custom_components/heating_mpc/coordinator.py

@@ -14,7 +14,7 @@ from homeassistant.helpers.storage import Store
 from homeassistant.helpers.update_coordinator import DataUpdateCoordinator
 
 from .const import (
-    CONF_ROOM_MAX_AGE, CONF_WEATHER_MAX_AGE,
+    CONF_ROOM_MAX_AGE, CONF_ROOM_STALE_KEEP, CONF_WEATHER_MAX_AGE, ROOM_STALE_KEEP_MAX_MINUTES,
     DOMAIN, CONF_CLIMATE, CONF_HEATER, CONF_ROOMS, CONF_WEIGHTS, WEATHER_SOURCES,
     CONF_HORIZON, CONF_MIN_ON, CONF_MIN_OFF, CONF_SWITCH_COST, CONF_HEAT_COST,
     CONF_BAND, DEFAULTS,
@@ -186,15 +186,21 @@ class HeatingMPCCoordinator(DataUpdateCoordinator[dict[str, Any]]):
         inputs_before = self._prediction_inputs()
         room_quality = {}
         temps = {}
+        keep_stale = self.config.get(CONF_ROOM_STALE_KEEP) is True
         for room in self.rooms:
             quality = self._sensor_quality(room, now, self.config[CONF_ROOM_MAX_AGE])
             value = None
-            if quality["status"] == "valid":
+            # Optionally trust sensors that report only on change, but never a
+            # sensor silent for a day: it is most likely dead.
+            if quality["status"] == "valid" or (
+                    keep_stale and quality["status"] == "stale"
+                    and quality["age_seconds"] <= ROOM_STALE_KEEP_MAX_MINUTES * 60):
                 state = snapshot[room]
                 value = normalize("temperature", state.state, state.attributes.get("unit_of_measurement"))
                 if value is None or not 0 <= value <= 45:
                     quality["status"] = "invalid_value_or_unit"
                     value = None
+            quality["used"] = value is not None
             room_quality[room] = quality
             temps[room] = value
         weather_states = {}
@@ -229,6 +235,7 @@ class HeatingMPCCoordinator(DataUpdateCoordinator[dict[str, Any]]):
             "weather_comparison": {k: v for k, v in weather_comparisons.items() if v},
             "weather_model_inputs": ["temperature"],
             "retired_room_models": sorted(self._retired),
+            "excluded_rooms": [room for room in self.rooms if temps[room] is None],
         }
         heater_valid = heater is not None and heater.state in ("on", "off")
         heater_on = heater_valid and heater.state == "on"
@@ -241,14 +248,16 @@ class HeatingMPCCoordinator(DataUpdateCoordinator[dict[str, Any]]):
             self._lag = None
             self._relay_events.clear()
             self._relay_events_dropped = False
-        if (not heater_valid or climate is None or outside is None or
-                any(temp is None for temp in temps.values())):
+        # Rooms without a usable reading are left out and the others carry on;
+        # only the shared inputs, or losing every room, pause the integration.
+        vals: dict[str, float] = {room: float(t) for room, t in temps.items() if t is not None}
+        if not heater_valid or climate is None or outside is None or not vals:
             self._previous = None
             return status
 
-        # No casting surprises: all rooms are valid here.
-        vals: dict[str, float] = {room: float(temps[room]) for room in self.rooms}
-        if self._previous is None:
+        # A room learns only across a poll pair where it was usable at both ends.
+        pairs = [room for room in vals if self._previous and room in self._previous["temps"]]
+        if not pairs:
             status["sample_result"] = "first_sample"
         else:
             dt = (now - self._previous["time"]).total_seconds()
@@ -267,7 +276,7 @@ class HeatingMPCCoordinator(DataUpdateCoordinator[dict[str, Any]]):
                     self.learners[room].learn(
                         self._previous["temps"][room], vals[room],
                         self._previous["outside"], self._lag["lag"], dt, heater_on)
-                    for room in self.rooms
+                    for room in pairs
                 ]
                 status["sample_result"] = "learned" if all(accepted) else "partly_rejected"
                 self._update_counts()
@@ -294,9 +303,12 @@ class HeatingMPCCoordinator(DataUpdateCoordinator[dict[str, Any]]):
         except (AttributeError, TypeError, ValueError, OverflowError):
             status["status"] = "invalid_weights"
             return status
-        weighted_temp = sum(weights[r] * vals[r] for r in self.rooms) / sum(weights.values())
+        # Left-out rooms take their weight with them; the rest are re-normalized.
+        used_weights = {room: weights[room] for room in vals}
+        weighted_temp = sum(used_weights[r] * vals[r] for r in vals) / sum(used_weights.values())
         status.update({
             "weighted_temperature": round(weighted_temp, 3),
+            "weight_coverage": round(sum(used_weights.values()) / sum(weights.values()), 3),
             "outside_temperature": outside,
             "reference_heat": heater_on,
             "on_samples": self._on_samples,
@@ -325,8 +337,11 @@ class HeatingMPCCoordinator(DataUpdateCoordinator[dict[str, Any]]):
             status["status"] = "waiting_for_target"
             return status
         status["target"] = target
-        # Do not act on a cold model: need ~17 hours of samples plus both conditions.
-        if status["samples"] < 200 or self._on_samples < 30 or self._off_samples < 30:
+        # Do not act on a cold model: every room used needs ~17 hours of samples
+        # plus both relay conditions.
+        used_models = [self.learners[room] for room in vals]
+        if (min(m.count for m in used_models) < 200 or min(m.on_count for m in used_models) < 30
+                or min(m.off_count for m in used_models) < 30):
             status["status"] = "learning"
             return status
         try:
@@ -334,7 +349,8 @@ class HeatingMPCCoordinator(DataUpdateCoordinator[dict[str, Any]]):
             # never block Home Assistant's event loop with CPU work.
             job = partial(
                 optimize,
-                vals, outside, self._lag["lag"], target, weights, self.learners,
+                vals, outside, self._lag["lag"], target, used_weights,
+                {room: self.learners[room] for room in vals},
                 heater_on, (now - heater.last_changed).total_seconds(),
                 horizon_minutes=int(self.config[CONF_HORIZON]),
                 minimum_on_minutes=int(self.config[CONF_MIN_ON]),

custom_components/heating_mpc/strings.json

@@ -10,7 +10,11 @@
           "heater_entity": "Physical heating switch (read-only)",
           "room_entities": "Individual room temperature sensors",
           "room_max_age_minutes": "Maximum room sensor report age (minutes)",
-          "weather_max_age_minutes": "Maximum weather sensor report age (minutes)"
+          "weather_max_age_minutes": "Maximum weather sensor report age (minutes)",
+          "room_stale_use_last_value": "Keep using the last value of a room sensor that stopped reporting"
+        },
+        "data_description": {
+          "room_stale_use_last_value": "Off: a room sensor older than its report-age limit is left out and the other rooms are re-weighted. On: its last value is still used, for up to 24 hours without a report; for sensors that report only when the value changes. Unavailable or invalid room sensors are always left out. Nothing pauses unless no room is usable."
         }
       },
       "weather": {
@@ -63,7 +67,11 @@
           "heating_cost": "Heating runtime cost",
           "comfort_band": "Comfort tolerance (°C)",
           "room_max_age_minutes": "Maximum room sensor report age (minutes)",
-          "weather_max_age_minutes": "Maximum weather sensor report age (minutes)"
+          "weather_max_age_minutes": "Maximum weather sensor report age (minutes)",
+          "room_stale_use_last_value": "Keep using the last value of a room sensor that stopped reporting"
+        },
+        "data_description": {
+          "room_stale_use_last_value": "Off: a room sensor older than its report-age limit is left out and the other rooms are re-weighted. On: its last value is still used, for up to 24 hours without a report; for sensors that report only when the value changes. Unavailable or invalid room sensors are always left out. Nothing pauses unless no room is usable."
         }
       },
       "weather": {

custom_components/heating_mpc/translations/en.json

@@ -10,7 +10,11 @@
           "heater_entity": "Physical heating switch (read-only)",
           "room_entities": "Individual room temperature sensors",
           "room_max_age_minutes": "Maximum room sensor report age (minutes)",
-          "weather_max_age_minutes": "Maximum weather sensor report age (minutes)"
+          "weather_max_age_minutes": "Maximum weather sensor report age (minutes)",
+          "room_stale_use_last_value": "Keep using the last value of a room sensor that stopped reporting"
+        },
+        "data_description": {
+          "room_stale_use_last_value": "Off: a room sensor older than its report-age limit is left out and the other rooms are re-weighted. On: its last value is still used, for up to 24 hours without a report; for sensors that report only when the value changes. Unavailable or invalid room sensors are always left out. Nothing pauses unless no room is usable."
         }
       },
       "weather": {
@@ -63,7 +67,11 @@
           "heating_cost": "Heating runtime cost",
           "comfort_band": "Comfort tolerance (°C)",
           "room_max_age_minutes": "Maximum room sensor report age (minutes)",
-          "weather_max_age_minutes": "Maximum weather sensor report age (minutes)"
+          "weather_max_age_minutes": "Maximum weather sensor report age (minutes)",
+          "room_stale_use_last_value": "Keep using the last value of a room sensor that stopped reporting"
+        },
+        "data_description": {
+          "room_stale_use_last_value": "Off: a room sensor older than its report-age limit is left out and the other rooms are re-weighted. On: its last value is still used, for up to 24 hours without a report; for sensors that report only when the value changes. Unavailable or invalid room sensors are always left out. Nothing pauses unless no room is usable."
         }
       },
       "weather": {

custom_components/heating_mpc/translations/ro.json

@@ -10,7 +10,11 @@
           "heater_entity": "Releul fizic (numai citire)",
           "room_entities": "Senzorii individuali din camere",
           "room_max_age_minutes": "Vârsta maximă a raportării senzorilor interiori (minute)",
-          "weather_max_age_minutes": "Vârsta maximă a raportării senzorilor meteo (minute)"
+          "weather_max_age_minutes": "Vârsta maximă a raportării senzorilor meteo (minute)",
+          "room_stale_use_last_value": "Folosește în continuare ultima valoare a unui senzor de cameră care nu mai raportează"
+        },
+        "data_description": {
+          "room_stale_use_last_value": "Dezactivat: un senzor de cameră mai vechi decât limita de raportare este ignorat, iar ponderile celorlalte camere sunt recalculate. Activat: ultima lui valoare este folosită în continuare, cel mult 24 de ore fără raportare; pentru senzorii care raportează doar la schimbarea valorii. Senzorii de cameră indisponibili sau invalizi sunt mereu ignorați. Nimic nu se oprește decât dacă nicio cameră nu poate fi folosită."
         }
       },
       "weather": {
@@ -63,7 +67,11 @@
           "heating_cost": "Penalizare durată încălzire",
           "comfort_band": "Toleranță confort (°C)",
           "room_max_age_minutes": "Vârsta maximă a raportării senzorilor interiori (minute)",
-          "weather_max_age_minutes": "Vârsta maximă a raportării senzorilor meteo (minute)"
+          "weather_max_age_minutes": "Vârsta maximă a raportării senzorilor meteo (minute)",
+          "room_stale_use_last_value": "Folosește în continuare ultima valoare a unui senzor de cameră care nu mai raportează"
+        },
+        "data_description": {
+          "room_stale_use_last_value": "Dezactivat: un senzor de cameră mai vechi decât limita de raportare este ignorat, iar ponderile celorlalte camere sunt recalculate. Activat: ultima lui valoare este folosită în continuare, cel mult 24 de ore fără raportare; pentru senzorii care raportează doar la schimbarea valorii. Senzorii de cameră indisponibili sau invalizi sunt mereu ignorați. Nimic nu se oprește decât dacă nicio cameră nu poate fi folosită."
         }
       },
       "weather": {

tests/ha/test_config_flow.py

@@ -15,6 +15,7 @@ async def test_config_flow(hass, inputs):
     validated = result["data_schema"](main)
     assert validated["room_max_age_minutes"] == 30
     assert validated["weather_max_age_minutes"] == 120
+    assert validated["room_stale_use_last_value"] is False
     result = await hass.config_entries.flow.async_configure(result["flow_id"], validated)
     assert result["step_id"] == "weather"
     result = await hass.config_entries.flow.async_configure(
@@ -35,7 +36,7 @@ async def test_options_flow_keeps_settings(hass, entry, inputs):
     result = await hass.config_entries.options.async_init(entry.entry_id)
     main = result["data_schema"]({"climate_entity": "climate.reference",
                                  "heater_entity": "switch.boiler", "room_entities": ["sensor.room"],
-                                 "room_max_age_minutes": 45})
+                                 "room_max_age_minutes": 45, "room_stale_use_last_value": True})
     result = await hass.config_entries.options.async_configure(result["flow_id"], main)
     result = await hass.config_entries.options.async_configure(
         result["flow_id"], result["data_schema"]({"outside_temperature_sources": ["sensor.outside"]}))
@@ -44,7 +45,14 @@ async def test_options_flow_keeps_settings(hass, entry, inputs):
     assert result["type"] is FlowResultType.CREATE_ENTRY
     await hass.async_block_till_done()
     assert entry.options["room_max_age_minutes"] == 45
-    assert hass.data[DOMAIN][entry.entry_id].config["room_max_age_minutes"] == 45
+    coordinator = hass.data[DOMAIN][entry.entry_id]
+    assert coordinator.config["room_max_age_minutes"] == 45
+    assert coordinator.config["room_stale_use_last_value"] is True
+    # The next options form shows the saved choice.
+    result = await hass.config_entries.options.async_init(entry.entry_id)
+    assert result["data_schema"]({"climate_entity": "climate.reference", "heater_entity": "switch.boiler",
+                                  "room_entities": ["sensor.room"]})["room_stale_use_last_value"] is True
+    hass.config_entries.options.async_abort(result["flow_id"])
 
 
 async def test_v1_entry_migrates_on_setup(hass, inputs):

tests/test_coordinator.py

@@ -539,3 +539,79 @@ class BoundaryTests(unittest.IsolatedAsyncioTestCase):
                          ('valid', 0, 2400))
         weather = data['input_quality']['weather']['temperature']['sensor.outside']
         self.assertEqual(weather['max_age_seconds_24h'], 0)  # kept reporting; tracked separately
+
+    def two_rooms(self, keep_last=False):
+        self.entry.data['room_entities'] = ['sensor.room', 'sensor.bedroom']
+        self.entry.data['weights'] = {'sensor.room': 3, 'sensor.bedroom': 1}
+        self.entry.data['room_stale_use_last_value'] = keep_last
+        self.set_state('sensor.bedroom', '18', unit_of_measurement='°C')
+        self.c = coordinator.HeatingMPCCoordinator(self.hass, self.entry)
+
+    def fresh(self, *entities):
+        for entity in entities or ('sensor.room', 'sensor.bedroom', 'sensor.outside'):
+            self.states[entity].last_reported = self.now
+
+    async def test_stale_room_is_left_out_and_other_rooms_continue(self):
+        self.two_rooms()
+        await self.sample()
+        self.fresh('sensor.room', 'sensor.outside')
+        self.states['sensor.bedroom'].last_reported = self.now - timedelta(minutes=31)
+        data = await self.sample()
+        self.assertEqual(data['status'], 'learning')
+        self.assertEqual(data['excluded_rooms'], ['sensor.bedroom'])
+        rooms = data['input_quality']['rooms']
+        self.assertEqual((rooms['sensor.bedroom']['status'], rooms['sensor.bedroom']['used']), ('stale', False))
+        self.assertTrue(rooms['sensor.room']['used'])
+        self.assertEqual((data['weighted_temperature'], data['weight_coverage']), (20, 0.75))
+        self.assertEqual(data['sample_result'], 'learned')
+        counts = lambda: (self.c.learners['sensor.room'].count, self.c.learners['sensor.bedroom'].count)
+        self.assertEqual(counts(), (1, 0))
+        self.fresh()  # bedroom reports again: no learning across its gap
+        data = await self.sample()
+        self.assertEqual((data['excluded_rooms'], data['weight_coverage']), ([], 1.0))
+        self.assertEqual(data['weighted_temperature'], 19.5)
+        self.assertEqual(counts(), (2, 0))
+        self.fresh()
+        await self.sample()
+        self.assertEqual(counts(), (3, 1))
+
+    async def test_left_out_room_is_not_used_for_prediction(self):
+        self.two_rooms()
+        model = self.c.learners['sensor.room']  # the bedroom stays untrained
+        model.count, model.on_count, model.off_count = 200, 100, 100
+        self.states['sensor.bedroom'].state = 'unavailable'
+        with patch.object(coordinator, 'optimize', wraps=coordinator.optimize) as spy:
+            data = await self.sample()
+        self.assertEqual(data['status'], 'ready')
+        temps, weights, learners = (spy.call_args.args[i] for i in (0, 4, 5))
+        self.assertEqual((set(temps), weights, set(learners)),
+                         ({'sensor.room'}, {'sensor.room': 3.0}, {'sensor.room'}))
+        self.assertEqual(set(data['prediction_60m']), {'sensor.room'})
+
+    async def test_all_rooms_unusable_still_pauses(self):
+        self.two_rooms()
+        for room in ('sensor.room', 'sensor.bedroom'):
+            self.states[room].last_reported = self.now - timedelta(minutes=31)
+        data = await self.sample()
+        self.assertEqual(data['status'], 'waiting_for_sensors')
+        self.assertEqual(data['excluded_rooms'], ['sensor.room', 'sensor.bedroom'])
+        self.assertIsNone(data['proposal'])
+
+    async def test_keep_last_value_option_uses_stale_room_for_up_to_a_day(self):
+        self.two_rooms(keep_last=True)
+        self.states['sensor.bedroom'].last_reported = self.now - timedelta(hours=2)
+        await self.sample()
+        self.fresh('sensor.room', 'sensor.outside')
+        data = await self.sample()
+        bedroom = data['input_quality']['rooms']['sensor.bedroom']
+        self.assertEqual((bedroom['status'], bedroom['used']), ('stale', True))
+        self.assertEqual((data['excluded_rooms'], data['temperatures']['sensor.bedroom']), ([], 18))
+        self.assertEqual(self.c.learners['sensor.bedroom'].count, 1)
+        for state, age in (('18', timedelta(hours=24, seconds=1)), ('unavailable', timedelta(0))):
+            with self.subTest(state=state):
+                self.fresh('sensor.room', 'sensor.outside')
+                self.states['sensor.bedroom'].state = state
+                self.states['sensor.bedroom'].last_reported = self.now - age
+                data = await self.sample()
+                self.assertEqual(data['excluded_rooms'], ['sensor.bedroom'])
+                self.assertEqual(data['status'], 'learning')

tests/test_data_quality.py

@@ -48,5 +48,7 @@ class FreshnessTests(unittest.TestCase):
             data = json.loads((ROOT / name).read_text())
             for section, step in (('config', 'user'), ('options', 'init')):
                 labels = data[section]['step'][step]['data']
-                for key in ('room_max_age_minutes', 'weather_max_age_minutes'):
+                for key in ('room_max_age_minutes', 'weather_max_age_minutes', 'room_stale_use_last_value'):
                     self.assertTrue(labels[key])
+                help_text = data[section]['step'][step]['data_description']
+                self.assertIn('24', help_text['room_stale_use_last_value'])