Release 0.4.2

Final stability fixes after 0.4.1: Model Error leaves out spans with
unknown inputs; window sensors unknown at startup stay unknown, with last
known states restored as unknown across restarts; unload, reload and
removal can no longer publish stale results or recreate a deleted model;
Forecast Error scores only forecasts whose plan the relay matched within
2 minutes. Adds Reset learned model to Configure. Still observation-only.

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

AUDIT.md

@@ -397,3 +397,73 @@ Remaining risks:
 ruff, 159 offline tests, 28 HA Core tests; the offline tests also pass on Python
 3.9.6. `sh scripts/check.sh` additionally validates packaging, translations and
 the changelog.
+
+## 0.4.2 — final stability fixes after 0.4.1 — 2026-10-27
+
+Scope: four issues from an independent review of 0.4.1, plus a user-requested
+*Reset learned model* action. Repository code and tests only (HA Core 2025.12.0
+and 2026.2.3 harness); no HAOS, device or deployment testing. Still no actuator
+code; the static and runtime service-call guards pass. Released as 0.4.2.
+
+Confirmed issues, root causes and fixes:
+
+1. **Model Error scored spans with unknown ventilation inputs** (2cfba02).
+   Reproduced: a window unknown for a whole 15-minute span gave a Model Error
+   sample (MAE 0.76 °C). Root cause: the replay used the prediction path, which
+   drops the ventilation term when the opening is unknown, i.e. assumes closed.
+   Fix: per zone and step, `inputs_complete` checks every input the zone's model
+   used (window opening and confirmed recovery; wind, direction and other zones'
+   windows while a window is open; electrical heat and neighbours for the extended
+   layer, including the neighbour's own completeness). Incomplete spans are
+   counted, not scored, with the base model's error kept apart; unreplayable
+   spans are counted as skipped. Learning also stopped substituting a zero facing
+   term or zero cross-ventilation input for unknown values.
+2. **Window unknown at startup treated as closed** (13cb972). Reproduced
+   (0 % assumed opening). Root cause: last known states lived only in memory, and
+   "never observed" fell through to the closed case; a first reading of an open
+   window was also logged as a confirmed opening. Fix: confirmation only for
+   witnessed transitions; last known states, open events and recovery saved (also
+   on every window state change) and restored as unknown; per-window forecast
+   assumptions (observed / last known open, bounded by the open-window time after
+   it was last seen / last known closed / unobserved), exposed in Status and on
+   zone forecasts; durations as ranges when an end was not seen. Chosen over
+   open/closed scenario pairs to avoid a second plan simulation per poll.
+3. **Lifecycle and storage races** (28ff953). Confirmed by reading HA's unload
+   order: the coordinator's own shutdown runs only after platforms unload, and the
+   save checked for shutdown before waiting for the storage lock (check-then-act).
+   Found during testing: unloading cancels a running scheduled update, which
+   released the lock while the file write continued in a worker thread, so a
+   deletion could run first and be undone. Fix: closing mark at the start of
+   unload (restored if unload fails); checks after acquiring the lock, including a
+   removed-model mark set under the lock by removal; the write runs as a shielded,
+   HA-tracked task that holds the lock until it finishes; failed setup shuts the
+   coordinator down; queued relay callbacks are ignored once closing. Mutation
+   checks: removing the locked check or the shield makes the deletion tests fail;
+   removing the early closing mark fails the ordering test (the HA-level unload
+   test cannot distinguish it, as HA's own shutdown also lands before release).
+4. **Forecast Error accepted materially different relay behaviour** (0b82a8e).
+   Reproduced: 12 of 15 planned minutes was scored. Root cause: a per-block
+   on-share tolerance of 20 %, blind to timing within a block. Fix: deviation =
+   time in another state than planned, block by block from the relay's switch
+   times, with unobserved time as possible deviation; matched ≤ 2 min (scored),
+   approximate ≤ 5 min (separate), counterfactual > 5 min observed, otherwise
+   incomplete with a reason; forecasts made with assumed inputs are incomplete.
+   Ledger statistics are versioned; 0.4.1 figures are dropped.
+
+Added: *Reset learned model* (49565d6): unload, strip learned keys from the
+stored model under the lock, set up again; configuration, heat-source baselines
+and window history kept.
+
+Tests added (by area): Model Error incomplete/skipped (7), plan matching (10),
+window startup/restart/mixed/strict inputs (9), coordinator window reload and
+uncertain forecast (2), lifecycle offline (5) and in HA (4), reset flow (1).
+
+Remaining limitations: unknown-but-last-closed and never-seen windows still
+produce a closed-window forecast (flagged, never scored); the 2 and 5 minute
+limits rest on typical learned heat coefficients, not a per-forecast calculation;
+spans lost when the replay window restarts are not counted; saved window state can
+be up to one save old, which only makes the restored unknown period longer.
+
+**Verification.** `sh scripts/check.sh` passes in both environments (pip check,
+ruff, 192 offline tests, 33 HA Core tests, compilation, package/translation/
+changelog checks, diff whitespace); the offline tests also pass on Python 3.9.6.

CHANGELOG.md

@@ -2,6 +2,78 @@
 
 Engineering findings and test evidence behind each change are in [AUDIT.md](AUDIT.md).
 
+## 0.4.2 — 2026-10-27
+
+Four correctness fixes from an independent review of 0.4.1, and a **Reset learned
+model** action. Still **observation-only**: no service calls; the boiler and
+thermostat are untouched.
+Tested in the Home Assistant Core harness with HA 2025.12.0 and 2026.2.3; not on
+HAOS.
+
+### Upgrade notes
+
+- No reconfiguration needed. Zones, sensor and comfort weights, weather sources,
+  heat sources, windows, learned coefficients and fingerprints carry over; entity
+  IDs are unchanged.
+- **Configure now opens a menu**: *Settings* (the previous form) or *Reset learned
+  model*.
+- **Forecast Error statistics restart.** 0.4.1 accepted plans the relay followed
+  within 20 % per block; those figures are not comparable with the new rule and
+  are dropped on the first start (they covered at most a day).
+- Model Error statistics carry over. Those from 0.4.0/0.4.1 may include a few
+  spans with unknown window states; they age out within 7 days.
+
+### Added
+
+- **Reset learned model** (Configure, with a confirmation): forgets every zone's
+  learned coefficients, models kept for removed zones, and the Model Error and
+  Forecast Error statistics; keeps the configuration, heat-source baselines and the
+  window event log, and logs the reset in `model_changes`.
+
+### Fixed
+
+- **Model Error no longer scores spans with unknown inputs.** A window unknown
+  during a span was replayed as closed and scored. Spans where a window state, an
+  open window's weather input, or the extended layer's electrical heat or
+  neighbour was unknown are now counted as `incomplete` (with the base model's
+  error kept apart as `incomplete_base_model_mae`); spans that cannot be replayed
+  are counted as `skipped`.
+- **Window sensors unavailable at startup are unknown, not closed.** Last known
+  states, open events and recovery are saved and restored as unknown until the
+  sensor reports; openings and closings count as confirmed only when seen; an
+  unknown window last seen open is assumed open only until the open-window time
+  after it was last seen; other unknown windows are flagged as uncertain in
+  forecasts (`uncertain_windows`) and in Status (`forecast_assumption`). Event
+  durations are given as a range when an end was not seen.
+- **Unload, reload and removal cannot publish or save stale work.** The
+  coordinator is marked closing before platforms unload; the model save checks
+  this, and a removed-model mark, after taking the storage lock and holds the lock
+  until the file write finishes, so a deletion can no longer be undone by a save
+  that was waiting or cancelled mid-write. A failed setup shuts the coordinator
+  down; relay callbacks queued before unload are ignored.
+- **Forecast Error compares the relay's actual timing with the plan.** The
+  deviation is the time the relay spent in another state than planned (unobserved
+  time counts as possible deviation). Up to 2 minutes: matched and scored; up to 5:
+  approximate, scored separately; more: counterfactual; otherwise incomplete, with
+  a reason (relay history, assumed inputs, no poll at the due time, missing
+  reading). Forecasts made with an unknown window or electrical heat are never
+  scored.
+
+### Changed
+
+- Learning no longer substitutes values for an open window: samples where the
+  wind direction (for the directional term) or another zone's windows (for
+  cross-ventilation) were unknown are not learned. With every window closed, the
+  opening terms need no weather input, so recovery can be learned without wind.
+- The gust option's hint refers to Model Error.
+
+### Persisted data
+
+- `schema_version` stays 2. `ventilation` gains `state` (last known window
+  states, open events, recovery); `evaluation` gains `excluded` (incomplete and
+  skipped spans); `operational_evaluation` gains `version` 2 and `approximate`.
+  Missing optional keys load as empty.
+
 ## 0.4.1 — 2026-10-27
 
 Stability and correctness: fixes four problems found in 0.4.0 and separates model

README.md

@@ -1,4 +1,4 @@
-# Heating MPC 0.4.1 — custom integration for Home Assistant OS
+# Heating MPC 0.4.2 — custom integration for Home Assistant OS
 
 **Experimental, observation-only (shadow mode).** It **never** changes the Generic Thermostat, the physical relay, the boiler's setpoint, `heat/off`, or Comfort/Away/Sleep. No Docker add-on, MQTT or InfluxDB connection is needed.
 
@@ -23,8 +23,8 @@ On Home Assistant 2026.3 or newer the integration shows its own icon (a radiator
 
 On **Settings → Devices & services → Heating MPC**, the buttons **Add zone**, **Add
 heat source** and **Add window** create items that can each be edited or deleted
-later. **Configure** holds everything else: thermostat, relay, weather sources,
+later. **Configure** offers **Settings** (thermostat, relay, weather sources,
-report-age limits, MPC settings and model options.
+report-age limits, MPC settings and model options) and **Reset learned model**.
 
 **Zones** are rooms or areas. Each has a name, one or more temperature sensors, a
 comfort weight (how much the zone matters to the MPC), an optional target offset
@@ -45,6 +45,15 @@ or the recovery time resets only the coefficients that depend on it. Comfort
 weight, target offset and names never touch the model. Each recalibration is
 listed in the Status attribute `model_changes`.
 
+**Reset learned model** (in **Configure**, after ticking the confirmation) forgets
+everything learned: every zone's coefficients, including those kept for removed
+zones, with their confidence and sample counts, and the Model Error and Forecast
+Error statistics. Zones, sensors, weights, heat sources, windows, weather sources
+and settings stay as they are, and so do the heat sources' baselines and the window
+event log. The integration restarts and learns from the beginning (first sample
+after about 30 minutes, *Ready* after a day or more); `model_changes` records the
+reset. It never touches the boiler or the thermostat.
+
 **Heat sources** are equipment whose electricity ends up as heat in a zone, such as
 a UPS feeding computers. Settings: an active-power sensor (W or kW; VA and % are
 rejected), the zone, the share of the power that becomes heat there (1.0 by
@@ -57,12 +66,24 @@ modeled; the persistent load is already part of the zone's learned behavior.
 **Windows** need a contact sensor (binary sensor), the zone, whether the sensor
 reports *on* when closed (inverted), the direction the outside of the window faces
 (degrees clockwise from north; default 345°, editable) and an opening factor
-(1.0 normal, lower for tilted, higher for large). Unknown or unavailable states are
+(1.0 normal, lower for tilted, higher for large).
-treated as unknown, never as closed: a window last seen open counts as possibly
+
-open (the forecast assumes it open, the zone's base model does not learn), and an
+A window is **open**, **closed** or **unknown** (unknown, unavailable or no state);
-opening that ended while the sensor was unknown is logged as unconfirmed, with its
+unknown never means closed. While any window of a zone is unknown, the zone does
-longest possible duration. Recovery after closing is learned only from a confirmed
+not learn. What forecasts assume for an unknown window:
-close.
+
+- last seen open: open, but only until the configured open-window time after it was
+  last seen (never renewed), then closed and recovering;
+- last seen closed, or never seen since setup: no ventilation, but the forecast is
+  flagged (`uncertain_windows` on the zone's forecast entity, `forecast_assumption`
+  in Status) and is never used to measure forecast accuracy.
+
+An opening or closing is *confirmed* only when it was seen happen: the previous
+reading was the other state, with no unknown period or restart in between. Events
+report a duration only when both ends are confirmed, otherwise a minimum and, where
+possible, a maximum; recovery is learned only after a confirmed close. The last
+known states, an open event and a recovery in progress are saved, so a restart or
+reload continues them, but they come back as unknown until the sensor reports.
 
 Model options in **Configure**: how fast extra electrical heat is assumed to fade in
 forecasts (half-life, default 30 minutes), how long an open window is assumed to
@@ -177,9 +198,10 @@ the plan is discarded and the next poll computes a fresh one. Useful attributes:
   included sources), baseline, variable power above it, thermal response, whether it
   has settled.
 - `windows` — per window: state (`open`, `closed` or `unknown`), the last known
-  state and for how long the sensor has been unknown, minutes open, wind direction
+  state and for how long the sensor has been unknown, what forecasts assume
-  relative to the window's facing and the resulting exposure (1 = wind straight at
+  (`forecast_assumption`: `observed`, `last_known_open`, `last_known_closed` or
-  it).
+  `unobserved`; `forecast_open_minutes`), minutes open, wind direction relative to
+  the window's facing and the resulting exposure (1 = wind straight at it).
   `ventilation` — per zone: current opening, recovery, temperature before opening,
   its contribution in °C/h and the ventilation coefficients. `ventilation_events` —
   the last five openings with temperatures before, lowest, at closing and 30
@@ -279,35 +301,57 @@ model is scored on the same spans. Each sensor's state is the mean absolute erro
 - `base_model_mae`, `improvement_vs_base` — the base model's error on the same
   spans, and the share of it the extra layers remove (0 = no gain, negative = they
   make it worse: switch that option off).
+- `incomplete` — spans left out because an input the zone's model needed was not
+  observed at some step: a window state (or the start of a recovery), the wind,
+  wind direction or another zone's windows while a window was open, or, for the
+  extended layer, electrical heat or a neighbour (whose own span must be complete
+  too). The base model needs none of these, so its error on those spans is still
+  reported as `incomplete_base_model_mae`.
+- `skipped` — spans that could not be replayed: the radiator-lag estimate was
+  uncertain along the way, or the zone had no reading at the end.
 - `zones` — the same numbers per zone (not written to the Recorder).
 
-Spans are scored only where the inputs were known: polls 4.5–5.5 minutes apart, a
+Spans need polls 4.5–5.5 minutes apart; a missing relay, outdoor temperature or
-usable reading at the start and the end, and a well-known radiator-lag estimate
+all zones restarts the replay window, so scores resume 15–120 minutes later (these
-throughout. A missing relay, outdoor temperature or all zones restarts the replay
+gaps are not counted). Statistics are kept with the model and survive restarts;
-window, so scores resume 15–120 minutes later. Statistics are kept with the model
+recalibrating a zone or resetting the learned model clears them.
-and survive restarts; recalibrating a zone or deleting the model resets them.
 
 ### Forecast Error
 
 Each time a plan is published, its forecast is recorded as it was: start
 temperatures, the trajectory, the plan, the model revision and the assumptions
 (outdoor temperature and wind held, electrical heat fading, open windows closing).
-When the forecast's horizon comes, it is scored against the reading, but only if
+When the forecast's horizon comes, the relay's actual behaviour is compared with
-the relay actually followed the plan: in every 15-minute block up to that horizon
+the plan, block by block, from the relay's own switch times: the **deviation** is
-the relay must have been in the planned state for at least 12 of the 15 minutes.
+how long the relay was in another state than planned, so the same on-time at other
-Otherwise the forecast is not an error measurement, and is counted instead:
+times than planned counts as deviation. Relay time that was not observed counts as
+possible deviation. Each forecast is then:
 
-- `counterfactual` — the relay did something else: Generic Thermostat, which
+- `matched` — at most 2 minutes of deviation. Only these make the Forecast Error
-  controls it, disagreed with the plan. Expected in observation mode whenever the
+  figures.
-  two disagree.
+- `approximate` — at most 5 minutes (of which at most 2 unobserved), for example
-- `unverifiable` — the relay history is incomplete for that span.
+  the thermostat switching a few minutes earlier than planned. Scored separately
-- `missing` — no poll near the due time, or no reading for a zone.
+  in `approximate_stats`.
+- `counterfactual` — more than 5 minutes: Generic Thermostat, which controls the
+  relay, did something else. Not an error of the forecast. Expected in observation
+  mode whenever the two disagree.
+- `incomplete` — not comparable, with `incomplete_reasons`: `relay_history` (too
+  much relay time unknown), `uncertain_inputs` (the forecast had to assume an
+  unknown window or electrical heat), `no_poll` (no poll near the due time) or
+  `reading` (a zone had no reading; counted per zone).
 
-The state is the 60-minute MAE once its quality is at least `indicative`; the
+Why these limits: d seconds of boiler heat that the plan did not assume move a room
-attribute `horizons` (not written to the Recorder) gives MAE, RMSE, bias, the
+by roughly *heat* × d / 300, where *heat* is the learned boiler coefficient
-no-change error, `improvement_vs_persistence`, sample counts, quality, the counts
+(typically 0.05–0.12 °C per 5 minutes with warm radiators). 2 minutes stay below
-above and the per-zone figures for each horizon. Statistics cover 7 days and
+half a typical 0.1 °C sensor step and absorb timestamp jitter; 5 minutes are about
-survive restarts; forecasts not yet due when HA restarts are dropped.
+one step.
+
+The state is the 60-minute MAE of matched forecasts once its quality is at least
+`indicative`; the attribute `horizons` (not written to the Recorder) gives MAE,
+RMSE, bias, the no-change error, `improvement_vs_persistence`, sample counts,
+quality, the counts above, `approximate_stats` and the per-zone figures for each
+horizon. Statistics cover 7 days and survive restarts; forecasts not yet due when
+HA restarts are dropped.
 
 What neither covers: outdoor temperature changes during the horizon (held
 constant, as in the MPC), and periods when the model was still learning (included,
@@ -350,9 +394,10 @@ The brand icons in `custom_components/heating_mpc/brand/` are drawn by
 `python scripts/make_brand_icons.py` (uses Pillow from the test requirements);
 the package check verifies their sizes and transparency.
 
-HA tests cover setup, config/options forms, migration, reload (a single relay
+HA tests cover setup, config/options forms (including the reset), migration,
-listener and polling coordinator after repeated reloads) and entity cleanup,
+reload (a single relay listener and polling coordinator after repeated reloads),
-scheduled sampling and poll cancellation, the relay listener, stored model
+unload, reload and removal while an optimization or a save is still running,
+entity cleanup, scheduled sampling and poll cancellation, the relay listener, stored model
 restoration, retention and deletion, missing inputs, manual off/presets, optimizer
 failure and Recorder exclusions. A fixture rejects all HA service calls throughout
 each test, and a static test fails if actuator service calls are added to the

ROADMAP.md

@@ -95,6 +95,18 @@ changed during optimization or by an overtaken run; and model accuracy reported
 as if it were forecast accuracy. Reload/unload tests now cover repeated reloads
 (one relay listener, one polling coordinator). Released as 0.4.1; not yet tested on HAOS.
 
+### Final stability fixes after 0.4.1 — 2026-10-27
+
+An independent review of 0.4.1 reported four problems; three were reproduced and
+the fourth (lifecycle races) confirmed by reading Home Assistant's unload order.
+Fixed with regression tests (see AUDIT.md): Model Error spans with unknown inputs
+are now `incomplete`, not scored; window sensors unavailable at startup are
+unknown, with last known states saved and restored as unknown; unload, reload
+and removal can no longer publish stale results or recreate a deleted model;
+Forecast Error scores only forecasts whose plan the relay matched within 2
+minutes. Also added: **Reset learned model** in Configure. Released as 0.4.2; not
+yet tested on HAOS.
+
 ## Milestone 1 — Explainable forecasts and dashboards (target v0.3.0)
 
 **Objective:** Users can verify what the model predicts and whether it is actually accurate.
@@ -103,7 +115,7 @@ as if it were forecast accuracy. Reload/unload tests now cover repeated reloads
 - [x] Expose predicted +15/+30/+60/+120 minute temperatures and full proposed ON/OFF schedule. *(0.3.0: `forecast_15min` and `plan_15min` on each room forecast entity.)*
 - [ ] Provide an HA dashboard example using native cards or a clearly documented optional chart card (do not bundle a heavyweight frontend unnecessarily).
 - [ ] Make clear that the **MPC plan forecast is counterfactual** while Generic Thermostat is controlling the relay; add a separate actual-policy forecast/evaluation pathway. *(0.3.0: plan forecasts labelled counterfactual; evaluation uses the actual relay history. Stabilization: published forecasts are scored only when the relay followed their plan and otherwise counted as `counterfactual`. An actual-policy forecast, simulating Generic Thermostat, is still open.)*
-- [ ] Store a bounded history of predictions with snapshots of inputs, proposed plan and eventually observed outcome. *(Stabilization: each published forecast is kept with its start readings, plan, model revision and assumptions until scored, in memory only; the scored statistics persist. No browsable history yet.)*
+- [ ] Store a bounded history of predictions with snapshots of inputs, proposed plan and eventually observed outcome. *(Stabilization: each published forecast is kept with its start readings, plan, model revision and assumptions, including any it had to assume, until scored, in memory only; the scored statistics persist, split into matched, approximate, counterfactual and incomplete. No browsable history yet.)*
 - [x] Implement out-of-sample/walk-forward MAE and bias at 15, 30, 60 and 120 minutes; include sample counts and confidence/quality labels. *(0.3.0: replay with parameters frozen at the forecast start and the actual relay history; `skill` against persistence. Stabilization: these are now the Model Error sensors, with RMSE, effective sample counts and quality labels; a separate Forecast Error sensor scores published forecasts. No confidence intervals yet.)*
 - [ ] Compare against simple benchmarks such as current-temperature persistence and a fixed thermal baseline. *(0.3.0: persistence done; fixed baseline open.)*
 - [ ] Add inspectable explanations: expected heat loss, expected heating response, target, room weights and reason for the next proposed action.
@@ -187,9 +199,12 @@ Items 1–3 of the original backlog are done (see AUDIT.md and CHANGELOG.md 0.2.
 3. Configure zones, the UPS heat source and the windows on HAOS; after a week of
    heating review Model Error `improvement_vs_base` and `quality`, per-zone bias,
    coefficient `confidence` (base layer included), `model_changes` after any
-   reconfiguration, and the Forecast Error counts (how many forecasts were scored
+   reconfiguration, and the Forecast Error counts (matched, approximate,
-   vs counterfactual). Switch off optional terms that do not help. Also try an
+   counterfactual, incomplete and why). Switch off optional terms that do not
-   unavailable window sensor and a zone edit during HAOS acceptance.
+   help. During HAOS acceptance also try: a window sensor unavailable across an HA
+   restart, a zone edit, a reload while the integration is running, *Reset learned
+   model* and removing a disposable entry (its `.storage/heating_mpc.<entry_id>`
+   file must be gone afterwards).
 4. Milestone 1 (rest): a dashboard example; forecast timestamps/provenance;
    explanations for the proposed action; an actual-policy forecast that simulates
    Generic Thermostat.

custom_components/heating_mpc/__init__.py

@@ -12,7 +12,7 @@ from homeassistant.helpers.event import async_track_state_change_event
 
 from .const import CONF_HEATER, CONF_ROOMS, CONF_WEIGHTS, DOMAIN, CONF_OUTSIDE, WEATHER_SOURCES
 from .weather_sources import sources_for
-from .coordinator import HeatingMPCCoordinator, storage_lock
+from .coordinator import HeatingMPCCoordinator, mark_model_removed, storage_lock
 from .zones import SUBENTRY_ZONE, ZONE_KEY, ZONE_NAME, legacy_zone_data
 
 PLATFORMS: list[Platform] = [Platform.SENSOR, Platform.BINARY_SENSOR]
@@ -29,9 +29,11 @@ async def async_setup_entry(hass: HomeAssistant, entry: ConfigEntry) -> bool:
         hass.data.setdefault(DOMAIN, {})[entry.entry_id] = coordinator
         await hass.config_entries.async_forward_entry_setups(entry, PLATFORMS)
     except BaseException:
-        # HA skips unload callbacks for unexpected setup errors, so clean up here.
+        # HA skips unload callbacks for unexpected setup errors, so clean up here,
+        # including a refresh scheduled by entities that were already added.
         unsub_relay()
         hass.data.get(DOMAIN, {}).pop(entry.entry_id, None)
+        await coordinator.async_shutdown()
         raise
     entry.async_on_unload(unsub_relay)
     # Remove only obsolete zone forecasts belonging to this entry/platform. Zones
@@ -53,17 +55,25 @@ async def async_reload_entry(hass: HomeAssistant, entry: ConfigEntry) -> None:
 
 
 async def async_unload_entry(hass: HomeAssistant, entry: ConfigEntry) -> bool:
+    coordinator = hass.data.get(DOMAIN, {}).get(entry.entry_id)
+    if coordinator is not None:
+        # Before anything is unloaded: an update still running must not publish or save.
+        coordinator.async_begin_shutdown()
     ok = await hass.config_entries.async_unload_platforms(entry, PLATFORMS)
     if ok:
         hass.data[DOMAIN].pop(entry.entry_id, None)
+    elif coordinator is not None:
+        coordinator.async_cancel_shutdown()
     return ok
 
 
 async def async_remove_entry(hass: HomeAssistant, entry: ConfigEntry) -> None:
     """Remove the learned model when this integration instance is deleted."""
     store = Store(hass, 1, f"{DOMAIN}.{entry.entry_id}")
-    # Wait for a save still in progress, which would otherwise recreate the file.
+    # Wait for a save still in progress, which would otherwise recreate the file;
+    # a save still waiting for the lock sees the mark and does not write.
     async with storage_lock(hass, entry.entry_id):
+        mark_model_removed(hass, entry.entry_id)
         await store.async_remove()
     hass.data.get(f"{DOMAIN}_storage_locks", {}).pop(entry.entry_id, None)
 

custom_components/heating_mpc/config_flow.py

@@ -23,6 +23,7 @@ from .heat_sources import (
     SOURCE_KEY, SOURCE_NAME, SOURCE_RESPONSE, SOURCE_SENSOR, SOURCE_ZONE, SUBENTRY_HEAT_SOURCE,
     HeatSource, build_sources, validate_source,
 )
+from .coordinator import async_reset_learned_model
 from .weather_sources import sources_for
 from .windows import (
     DEFAULT_AZIMUTH, OPENING_RANGE, SUBENTRY_WINDOW, WINDOW_AZIMUTH, WINDOW_ENABLED, WINDOW_INVERTED,
@@ -205,6 +206,9 @@ class HeatingMPCConfigFlow(config_entries.ConfigFlow, domain=DOMAIN):
                 SUBENTRY_WINDOW: WindowSubentryFlow}
 
 
+CONFIRM_RESET = "confirm_reset"
+
+
 class HeatingMPCOptionsFlow(config_entries.OptionsFlow):
     """Edit sensors, ranked alternative weather providers and MPC parameters."""
 
@@ -215,13 +219,26 @@ class HeatingMPCOptionsFlow(config_entries.OptionsFlow):
         return [sensor for zone in entry_zones(self.config_entry) for sensor in zone.sensors]
 
     async def async_step_init(self, user_input: dict[str, Any] | None = None):
+        return self.async_show_menu(step_id="init", menu_options=["settings", "reset_model"])
+
+    async def async_step_settings(self, user_input: dict[str, Any] | None = None):
         config = {**self.config_entry.data, **self.config_entry.options}
         if user_input is not None:
             self._draft = user_input
             return await self.async_step_weather()
-        return self.async_show_form(step_id="init", data_schema=_main_schema(
+        return self.async_show_form(step_id="settings", data_schema=_main_schema(
             config, detailed=True))
 
+    async def async_step_reset_model(self, user_input: dict[str, Any] | None = None):
+        """Forget the learned model after an explicit confirmation; settings stay."""
+        if user_input is not None:
+            if user_input.get(CONFIRM_RESET) is not True:
+                return self.async_abort(reason="model_not_reset")
+            await async_reset_learned_model(self.hass, self.config_entry)
+            return self.async_abort(reason="model_reset")
+        return self.async_show_form(step_id="reset_model", data_schema=vol.Schema(
+            {vol.Required(CONFIRM_RESET, default=False): selector.BooleanSelector()}))
+
     async def async_step_weather(self, user_input: dict[str, Any] | None = None):
         config = {**self.config_entry.data, **self.config_entry.options, **self._draft}
         if user_input is not None:

custom_components/heating_mpc/coordinator.py

@@ -56,6 +56,43 @@ def storage_lock(hass: HomeAssistant, entry_id: str) -> asyncio.Lock:
     return hass.data.setdefault(f"{DOMAIN}_storage_locks", {}).setdefault(entry_id, asyncio.Lock())
 
 
+def mark_model_removed(hass: HomeAssistant, entry_id: str) -> None:
+    """Record, under the storage lock, that the entry's model file was deleted for good."""
+    hass.data.setdefault(f"{DOMAIN}_removed", set()).add(entry_id)
+
+
+def model_removed(hass: HomeAssistant, entry_id: str) -> bool:
+    return entry_id in hass.data.get(f"{DOMAIN}_removed", ())
+
+
+# Stored keys holding what was learned. The rest is observation, not model: the
+# heat sources' baseline samples, the window history and the heater's identity.
+LEARNED_KEYS = ("learners", "retired_learners", "zone_models", "ventilation_models", "evaluation",
+                "operational_evaluation", "fingerprint_version", "fingerprints", "on_samples", "off_samples")
+
+
+async def async_reset_learned_model(hass: HomeAssistant, entry: ConfigEntry) -> None:
+    """Forget everything learned for this entry; the configuration is not touched.
+
+    The entry is unloaded first, so no running update can save the old model
+    afterwards, then set up again from the stripped data. Accuracy statistics go
+    too: they describe the model being discarded.
+    """
+    await hass.config_entries.async_unload(entry.entry_id)
+    store = Store(hass, 1, f"{DOMAIN}.{entry.entry_id}")
+    async with storage_lock(hass, entry.entry_id):
+        stored = await store.async_load()
+        if isinstance(stored, dict):
+            kept = {key: value for key, value in stored.items() if key not in LEARNED_KEYS}
+            changes = kept.get("model_changes") if isinstance(kept.get("model_changes"), list) else []
+            kept["model_changes"] = [*changes, {
+                "time": datetime.now(timezone.utc).isoformat(timespec="seconds"), "zone": "all",
+                "recalibrated": ["all"], "reasons": ["learned model reset by the user"]}][-MAX_MODEL_CHANGES:]
+            await store.async_save(kept)
+    _LOGGER.info("Heating MPC: learned model reset by the user")
+    await hass.config_entries.async_setup(entry.entry_id)
+
+
 def entry_zone_list(entry: ConfigEntry, config: dict[str, Any]) -> list[Zone]:
     """Zones from the entry's zone subentries; one zone per legacy room sensor otherwise."""
     subentries = getattr(entry, "subentries", None) or {}
@@ -148,18 +185,57 @@ class HeatingMPCCoordinator(DataUpdateCoordinator[dict[str, Any]]):
         self._off_samples = 0
         # Increases with every update; an older update finishing late never publishes.
         self._run = 0
+        self._closing = False
 
     async def async_restore_model(self) -> None:
         async with storage_lock(self.hass, self.entry.entry_id):
             await self._async_restore_model()
 
-    async def _async_save(self, outside_source: str | None) -> None:
+    async def _async_save(self, outside_source: str | None) -> bool:
+        """Write the model unless unloading began or the model was deleted.
+
+        Shielded: unloading cancels a running scheduled update, but the file write
+        itself continues in a worker thread. The lock must stay held until that
+        write has finished, or a deletion could run first and be undone by it.
+        """
+        write = self.hass.async_create_task(self._async_write(outside_source), f"{DOMAIN} model save")
+        return await asyncio.shield(write)
+
+    async def _async_write(self, outside_source: str | None) -> bool:
         async with storage_lock(self.hass, self.entry.entry_id):
+            # Checked after acquiring the lock, not before: a save that waited for
+            # a deletion to finish must not write the file again.
+            if self.closing or model_removed(self.hass, self.entry.entry_id):
+                return False
             await self._store.async_save(self._payload(outside_source))
+            return True
+
+    @property
+    def closing(self) -> bool:
+        """True from the start of unloading: nothing is published, saved or recorded."""
+        return self._closing or getattr(self, "_shutdown_requested", False)
+
+    @callback
+    def async_begin_shutdown(self) -> None:
+        """Mark unloading before anything is unloaded.
+
+        Home Assistant's own shutdown of the coordinator runs only after platforms
+        are unloaded; an update still running in that window must not publish or save.
+        """
+        self._closing = True
+
+    @callback
+    def async_cancel_shutdown(self) -> None:
+        """Unloading failed and the entry stays loaded: carry on."""
+        self._closing = False
+
+    async def async_shutdown(self) -> None:
+        self._closing = True
+        await super().async_shutdown()
 
     def _stale(self, run: int) -> bool:
         """True when a newer update started or the entry is unloading."""
-        return run != self._run or getattr(self, "_shutdown_requested", False)
+        return run != self._run or self.closing
 
     async def _async_restore_model(self) -> None:
         stored = await self._store.async_load() or {}
@@ -303,6 +379,8 @@ class HeatingMPCCoordinator(DataUpdateCoordinator[dict[str, Any]]):
     @callback
     def async_relay_changed(self, event: Event) -> None:
         """Record relay state changes as they happen; read-only state listener."""
+        if self.closing:
+            return  # a callback queued before the listener was removed
         old, new = event.data.get("old_state"), event.data.get("new_state")
         if new is None:
             self.record_relay_state(event.time_fired, "removed")
@@ -442,7 +520,9 @@ class HeatingMPCCoordinator(DataUpdateCoordinator[dict[str, Any]]):
             details[key] = {"temperature": round(temp, 3),
                             "model_in_use": "extended" if model.uses_extended else "base",
                             "contributions_c_per_h": model.contributions(temp, inputs),
-                            "coefficients": coefficients}
+                            "coefficients": coefficients,
+                            # Windows whose state the forecast had to assume (sensor unknown).
+                            "uncertain_windows": list((vent_inputs.get(key) or {}).get("unknown_windows", []))}
         return details
 
     def _window_diagnostics(self, now: float, weather: dict[str, Any], temps: dict[str, float | None],
@@ -453,11 +533,14 @@ class HeatingMPCCoordinator(DataUpdateCoordinator[dict[str, Any]]):
             is_open, since = info.get("open"), info.get("since")
             direction = weather.get("wind_direction")
             last_known, unknown_since = info.get("last_known_open"), info.get("unknown_since")
+            assumed = self._ventilation.assumption(window, float(self.config[CONF_WINDOW_OPEN_MINUTES]), now)
             windows[window.name] = {
                 "zone": self.zones[window.zone].name, "sensor": window.sensor, "enabled": window.enabled,
                 "state": "unknown" if is_open is None else "open" if is_open else "closed",
                 "last_known_state": None if last_known is None else "open" if last_known else "closed",
                 "unknown_minutes": round((now - unknown_since) / 60, 1) if unknown_since else None,
+                # What forecasts assume: observed, or a bounded guess from the last reading.
+                "forecast_assumption": assumed["basis"], "forecast_open_minutes": assumed["open_minutes"],
                 "open_minutes": round((now - since) / 60, 1) if is_open and since else None,
                 "azimuth": window.azimuth, "opening_factor": window.opening, "inverted": window.inverted,
                 "relative_wind_direction": info.get("relative_wind_direction"),
@@ -527,6 +610,8 @@ class HeatingMPCCoordinator(DataUpdateCoordinator[dict[str, Any]]):
         snapshot = {entity: self.hass.states.get(entity)
                     for entity in set(self.sensors) | weather_entity_ids}
         self._run += 1
+        if self.closing:
+            return self._superseded({"status": "inputs_changed"})
         run = self._run
         inputs_before = self._input_snapshot()
         sensor_quality = {}
@@ -719,8 +804,10 @@ class HeatingMPCCoordinator(DataUpdateCoordinator[dict[str, Any]]):
                                                    self.ventilation.get(room))) for room in vals},
             inputs={room: {"internal": internal[room], "ventilation": vent_inputs.get(room)} for room in vals})
         status["forecast_accuracy"] = self._evaluator.summary(self.rooms)
-        if learned and not self._stale(run):
+        # Persist at each sample so restart does not erase hours of training, and when
-            # Persist at each sample so restart does not erase hours of training.
+        # a window's state changes so a restart continues from it.
+        window_changed, self._ventilation.changed = self._ventilation.changed, False
+        if (learned or window_changed) and not self._stale(run):
             await self._async_save(active_sources["temperature"])
         if self._stale(run):
             return self._superseded(status)
@@ -808,9 +895,16 @@ class HeatingMPCCoordinator(DataUpdateCoordinator[dict[str, Any]]):
             _LOGGER.warning("MPC prediction failed: %s", err)
             status["status"] = "prediction_error"
             return status
+        # Inputs the forecast had to assume: such forecasts are not compared with reality.
+        uncertain = [f"window: {name}" for room in vals
+                     for name in (vent_inputs.get(room) or {}).get("unknown_windows", [])]
+        uncertain += [f"electrical heat: {self.zones[room].name}" for room in vals
+                      if internal[room] is None and room in self.extended
+                      and ZoneModel(self.learners[room], self.extended[room]).uses_extended]
         self._forecasts.record(now.timestamp(), vals, output["trajectory_15min"], output["plan_15min"],
                                self._revision(), {
             "strategy": "mpc_plan", "outside": outside, "wind": weather.get("wind"),
+            "uncertain_inputs": uncertain,
             "internal_half_life_minutes": float(self.config.get(CONF_INTERNAL_HALF_LIFE, 30)),
             "window_open_minutes": float(self.config[CONF_WINDOW_OPEN_MINUTES]),
             "internal_kw": {room: internal[room] for room in vals},

custom_components/heating_mpc/evaluation.py

@@ -34,7 +34,7 @@ from math import isfinite, sqrt
 from typing import Any
 
 from .physics import Learner
-from .windows import ventilation_vector_for_prediction
+from .windows import ventilation_known, ventilation_vector_for_prediction
 from .zone_model import feature_vector
 
 HORIZONS = (15, 30, 60, 120)  # minutes
@@ -97,19 +97,50 @@ def snapshot_delta(snapshot: dict[str, Any], temp: float, inputs: dict[str, Any]
     return main
 
 
-def replay_zones(origin: dict[str, Any], path: list[dict[str, Any]]) -> dict[str, float]:
+def inputs_complete(snapshot: dict[str, Any], temp: float, inputs: dict[str, Any],
-    """All zones advanced together with the origin's models and the actual inputs."""
+                    incomplete: set[str]) -> bool:
+    """Whether a step of a zone's model used only observed inputs.
+
+    Not when a window state, or a weather input an open window's terms need, was
+    unknown; nor when the extended layer is in use and its electrical heat or a
+    neighbour is unknown, or that neighbour's own replay is already incomplete.
+    The replay itself still falls back as forecasts do; the result is just not a
+    measurement of the model.
+    """
+    vent_features = snapshot["ventilation"][0] if snapshot.get("ventilation") else ()
+    if not ventilation_known(vent_features, inputs.get("ventilation")):
+        return False
+    if snapshot["uses_extended"] and snapshot["extended"]:
+        features = snapshot["extended"][0]
+        if feature_vector(features, temp, inputs) is None:
+            return False
+        if any(f.startswith("couple:") and f.split(":", 1)[1] in incomplete for f in features):
+            return False
+    return True
+
+
+def replay_zones(origin: dict[str, Any], path: list[dict[str, Any]]) -> tuple[dict[str, float], set[str]]:
+    """All zones advanced together with the origin's models and the actual inputs.
+
+    Returns the replayed temperatures and the zones whose replay needed an input
+    that was not observed at some step (see inputs_complete).
+    """
     state = dict(origin["temps"])
+    incomplete: set[str] = set()
     for before, poll in zip(path, path[1:]):
         seconds = (poll["time"] - before["time"]).total_seconds()
         new = {}
+        unknown = set(incomplete)
         for zone, temp in state.items():
             actual = poll["inputs"].get(zone, {})
             inputs = {"outside": origin["outside"], "lag": poll["lag"], "temps": state,
                       "internal": actual.get("internal"), "ventilation": actual.get("ventilation")}
-            new[zone] = temp + snapshot_delta(origin["models"][zone], temp, inputs) * seconds / STEP_SECONDS
+            snapshot = origin["models"][zone]
-        state = new
+            if not inputs_complete(snapshot, temp, inputs, incomplete):
-    return state
+                unknown.add(zone)
+            new[zone] = temp + snapshot_delta(snapshot, temp, inputs) * seconds / STEP_SECONDS
+        state, incomplete = new, unknown
+    return state, incomplete
 
 
 class ForecastEvaluator:
@@ -121,6 +152,9 @@ class ForecastEvaluator:
         # UTC day -> horizon -> room -> [count, sum error, sum |error|, sum |persistence error|,
         #   sum |base model error|, sum error^2, count with squares (0 for 0.3/0.4 data)]
         self._days: dict[str, dict[str, dict[str, list[float]]]] = {}
+        # UTC day -> horizon -> room -> [incomplete spans, skipped spans, sum |base model error|
+        #   over the incomplete ones]. Neither kind enters the statistics above.
+        self._excluded: dict[str, dict[str, dict[str, list[float]]]] = {}
 
     def reset_window(self) -> None:
         """Inputs were missing: no forecast may be scored across this poll."""
@@ -147,6 +181,11 @@ class ForecastEvaluator:
         self._score()
         self._prune(time)
 
+    def _exclude(self, day: str, horizon: int, room: str, kind: int, base_error: float = 0.0) -> None:
+        bucket = self._excluded.setdefault(day, {}).setdefault(str(horizon), {}).setdefault(room, [0, 0, 0.0])
+        bucket[kind] += 1
+        bucket[2] += abs(base_error)
+
     def _score(self) -> None:
         history = list(self._history)
         latest = history[-1]
@@ -156,19 +195,27 @@ class ForecastEvaluator:
             if len(history) < steps + 1:
                 continue
             path = history[-(steps + 1):]
+            origin = path[0]
             # The actual heat input must be known along the way.
             if any(poll["lag_uncertainty"] > self._max_lag_uncertainty for poll in path[1:]):
+                for room in origin["temps"]:
+                    self._exclude(day, horizon, room, SKIPPED)
                 continue
-            origin = path[0]
             inputs = [(poll["lag"], (poll["time"] - before["time"]).total_seconds())
                       for before, poll in zip(path, path[1:])]
-            full = replay_zones(origin, path)
+            full, incomplete = replay_zones(origin, path)
-            for room, actual in latest["temps"].items():
+            for room, start in origin["temps"].items():
-                start, theta = origin["temps"].get(room), origin["theta"].get(room)
+                actual, theta = latest["temps"].get(room), origin["theta"].get(room)
-                if start is None or theta is None:
+                if actual is None or theta is None:
+                    self._exclude(day, horizon, room, SKIPPED)  # no reading at the end
                     continue
                 error = full[room] - actual
                 base_error = replay(start, origin["outside"], theta, inputs) - actual
+                if room in incomplete:
+                    # The base model needs only the relay and outdoor temperature: its
+                    # error is still a measurement, kept apart from the main figures.
+                    self._exclude(day, horizon, room, INCOMPLETE, base_error)
+                    continue
                 bucket = self._days.setdefault(day, {}).setdefault(str(horizon), {}).setdefault(
                     room, [0, 0.0, 0.0, 0.0, 0.0, 0.0, 0])
                 bucket[0] += 1
@@ -181,8 +228,9 @@ class ForecastEvaluator:
 
     def _prune(self, time: datetime) -> None:
         oldest = (time.date() - timedelta(days=WINDOW_DAYS - 1)).isoformat()
-        for day in [day for day in self._days if day < oldest]:
+        for store in (self._days, self._excluded):
-            del self._days[day]
+            for day in [day for day in store if day < oldest]:
+                del store[day]
 
     def summary(self, rooms: list[str]) -> dict[int, dict[str, Any]]:
         """Error statistics per horizon, overall and per configured room."""
@@ -196,15 +244,27 @@ class ForecastEvaluator:
                         for i, value in enumerate(values):
                             totals[i] += value
             overall = [sum(values[i] for values in per_room.values()) for i in range(7)]
-            result[horizon] = {**_stats(overall, horizon, len(per_room)),
+            excluded: dict[str, list[float]] = {}
-                               "rooms": {room: _stats(values, horizon) for room, values in per_room.items()}}
+            for day in self._excluded.values():
+                for room, values in day.get(str(horizon), {}).items():
+                    if room in rooms:
+                        totals = excluded.setdefault(room, [0, 0, 0.0])
+                        for i, value in enumerate(values):
+                            totals[i] += value
+            all_excluded = [sum(values[i] for values in excluded.values()) for i in range(3)]
+            result[horizon] = {**_stats(overall, horizon, len(per_room)), **_excluded_stats(all_excluded),
+                               "rooms": {room: {**_stats(per_room.get(room, [0, 0.0, 0.0, 0.0, 0.0, 0.0, 0]),
+                                                         horizon),
+                                                **_excluded_stats(excluded.get(room, [0, 0, 0.0]))}
+                                         for room in rooms if room in per_room or room in excluded}}
         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 store in (self._days, self._excluded):
-            for by_room in by_horizon.values():
+            for by_horizon in store.values():
-                by_room.pop(room, None)
+                for by_room in by_horizon.values():
+                    by_room.pop(room, None)
         for poll in self._history:
             poll["temps"].pop(room, None)
 
@@ -213,11 +273,20 @@ class ForecastEvaluator:
         return {"days": {day: {horizon: {room: [v[0], *(round(x, 6) for x in v[1:6]), v[6]]
                                          for room, v in by_room.items()}
                                for horizon, by_room in by_horizon.items()}
-                         for day, by_horizon in self._days.items()}}
+                         for day, by_horizon in self._days.items()},
+                "excluded": {day: {horizon: {room: [v[0], v[1], round(v[2], 6)] for room, v in by_room.items()}
+                                   for horizon, by_room in by_horizon.items()}
+                             for day, by_horizon in self._excluded.items()}}
 
     def load(self, data: Any) -> None:
         """Restore stored statistics, keeping only well-formed entries."""
-        self._days = {}
+        self._days, self._excluded = {}, {}
+        excluded = data.get("excluded") if isinstance(data, dict) else None
+        for day, horizon, room, values in _entries(excluded):
+            if (isinstance(values, list) and len(values) == 3 and all(type(v) is int and v >= 0 for v in values[:2])
+                    and type(values[2]) in (int, float) and isfinite(values[2]) and values[2] >= 0):
+                self._excluded.setdefault(day, {}).setdefault(horizon, {})[room] = [
+                    values[0], values[1], float(values[2])]
         days = data.get("days") if isinstance(data, dict) else None
         if not isinstance(days, dict):
             return
@@ -243,6 +312,30 @@ class ForecastEvaluator:
                             *squares]
 
 
+INCOMPLETE, SKIPPED = 0, 1  # columns of the excluded-span counts
+
+
+def _entries(days: Any):
+    """(day, horizon, zone, values) of stored per-day statistics with a valid shape."""
+    if not isinstance(days, dict):
+        return
+    horizons = {str(horizon) for horizon in HORIZONS}
+    for day, by_horizon in days.items():
+        if not _valid_day(day) or not isinstance(by_horizon, dict):
+            continue
+        for horizon, by_zone in by_horizon.items():
+            if horizon in horizons and isinstance(by_zone, dict):
+                for zone, values in by_zone.items():
+                    if isinstance(zone, str):
+                        yield day, horizon, zone, values
+
+
+def _excluded_stats(values: list[float]) -> dict[str, Any]:
+    incomplete, skipped, base = values
+    return {"incomplete": int(incomplete), "skipped": int(skipped),
+            "incomplete_base_model_mae": round(base / incomplete, 3) if incomplete else None}
+
+
 def _valid_bucket(room: Any, values: Any) -> bool:
     if not isinstance(room, str) or not isinstance(values, list) or len(values) not in (4, 5, 7):
         return False
@@ -278,11 +371,24 @@ def _stats(values: list[float], horizon: int, zones: int = 1) -> dict[str, Any]:
     }
 
 
-# A forecast's plan counts as executed when, in every 15-minute block, the share
+# How far the relay may depart from a forecast's plan (seconds in another state than
-# of time the relay was on is within this of what the plan assumed (0 or 1).
+# planned, up to the horizon) for the forecast to be compared with what happened.
-PLAN_TOLERANCE = 0.2
+# d seconds of boiler heat that the plan did not assume (or the reverse) move the
+# room by roughly heat x d / 300, where heat is the learned boiler coefficient
+# (typically 0.05-0.12 °C per 5 minutes with warm radiators). Up to 2 minutes keeps
+# that below half a typical 0.1 °C sensor step and absorbs timestamp jitter;
+# matched forecasts make the Forecast Error figures. Up to 5 minutes (one poll) is
+# about one sensor step: kept apart as approximate. Beyond, the relay did
+# something else: the forecast is counterfactual and is not scored. Relay time
+# that was not observed counts as possible deviation, and more than 2 minutes of it
+# leaves the forecast incomplete rather than approximate.
+MATCH_SECONDS = 120
+APPROXIMATE_SECONDS = 300
 DUE_TOLERANCE_SECONDS = 150  # half a poll: the poll closest to the forecast's due time
-VERDICTS = ("counterfactual", "unverifiable", "missing")
+VERDICTS = ("matched", "approximate", "counterfactual", "incomplete")
+# Why a forecast (or, for a missing reading, a zone's forecast) could not be compared.
+INCOMPLETE_REASONS = ("relay_history", "uncertain_inputs", "no_poll", "reading")
+LEDGER_VERSION = 2  # 1 (0.4.1) accepted plans within 20 % per 15-minute block
 
 
 class OperationalForecastLedger:
@@ -290,10 +396,10 @@ class OperationalForecastLedger:
 
     Nothing is replaced with hindsight: the start temperatures, trajectory, boiler
     plan and assumptions are those of the moment the forecast was made. Because the
-    plan is the MPC's proposal while Generic Thermostat controls the relay, a forecast
+    plan is the MPC's proposal while Generic Thermostat controls the relay, a
-    is only scored when the relay actually followed the plan up to that horizon;
+    forecast is compared only when the relay actually followed that plan (see
-    otherwise it is counted as counterfactual. Pending forecasts live in memory;
+    MATCH_SECONDS), and not at all when it was made with an input it had to
-    the statistics are saved.
+    assume. Pending forecasts live in memory; the statistics are saved.
     """
 
     def __init__(self, saved: Any = None) -> None:
@@ -301,8 +407,9 @@ class OperationalForecastLedger:
         # (interval end, seconds the relay was on or None if unknown, interval seconds)
         self._relay: deque[tuple[float, float | None, float]] = deque(maxlen=48)
         # UTC day -> horizon -> zone -> [count, sum error, sum |error|, sum error^2, sum |persistence error|]
-        self._days: dict[str, dict[str, dict[str, list[float]]]] = {}
+        self._days: dict[str, dict[str, dict[str, list[float]]]] = {}       # matched forecasts
-        # UTC day -> horizon -> verdict -> count of forecasts not scored, and why
+        self._approximate: dict[str, dict[str, dict[str, list[float]]]] = {}
+        # UTC day -> horizon -> verdict (incomplete ones as "incomplete:<reason>") -> count
         self._verdicts: dict[str, dict[str, dict[str, int]]] = {}
         self.load(saved)
 
@@ -327,20 +434,18 @@ class OperationalForecastLedger:
                 forecast["done"].add(horizon)
                 if index >= len(forecast["plan"]):
                     continue  # beyond this forecast's horizon
-                if now > due + DUE_TOLERANCE_SECONDS:
+                verdict = self._classify(forecast, horizon, now)
-                    self._count(day, horizon, "missing")  # no poll near the due time
+                self._count(day, horizon, verdict)
-                    continue
+                if verdict not in ("matched", "approximate"):
-                verdict = self._verify(forecast, horizon)
-                if verdict:
-                    self._count(day, horizon, verdict)
                     continue
+                store = self._days if verdict == "matched" else self._approximate
                 for zone, path in forecast["trajectory"].items():
                     measured, start = actual.get(zone), forecast["start"].get(zone)
                     if measured is None or start is None or len(path) <= index:
-                        self._count(day, horizon, "missing")
+                        self._count(day, horizon, "incomplete:reading")
                         continue
                     error = path[index] - measured
-                    bucket = self._days.setdefault(day, {}).setdefault(str(horizon), {}).setdefault(
+                    bucket = store.setdefault(day, {}).setdefault(str(horizon), {}).setdefault(
                         zone, [0, 0.0, 0.0, 0.0, 0.0])
                     bucket[0] += 1
                     bucket[1] += error
@@ -350,38 +455,62 @@ class OperationalForecastLedger:
             if len(forecast["done"]) == len(HORIZONS) or now > forecast["time"] + (HORIZONS[-1] + 5) * 60:
                 self._pending.remove(forecast)
         oldest = (datetime.fromtimestamp(now, timezone.utc).date() - timedelta(days=WINDOW_DAYS - 1)).isoformat()
-        for store in (self._days, self._verdicts):
+        for store in (self._days, self._approximate, self._verdicts):
             for old in [d for d in store if d < oldest]:
                 del store[old]
 
-    def _verify(self, forecast: dict[str, Any], horizon: int) -> str | None:
+    def _classify(self, forecast: dict[str, Any], horizon: int, now: float) -> str:
-        """None when the relay followed the plan up to `horizon`, else the reason."""
+        if now > forecast["time"] + horizon * 60 + DUE_TOLERANCE_SECONDS:
-        for block in range(horizon // 15):
+            return "incomplete:no_poll"
-            start = forecast["time"] + block * 900
+        if forecast["assumptions"].get("uncertain_inputs"):
-            end = start + 900
+            return "incomplete:uncertain_inputs"
-            on = covered = 0.0
+        deviation, unobserved = self.deviation(forecast, horizon)
-            for interval_end, on_seconds, seconds in self._relay:
+        if deviation > APPROXIMATE_SECONDS:
-                overlap = min(end, interval_end) - max(start, interval_end - seconds)
+            return "counterfactual"  # whatever happened in the unobserved part
-                if overlap <= 0:
+        # Otherwise judged by the worst case: unobserved time may all have deviated.
-                    continue
+        if deviation + unobserved <= MATCH_SECONDS:
-                if on_seconds is None:
+            return "matched"
-                    return "unverifiable"
+        if deviation + unobserved <= APPROXIMATE_SECONDS and unobserved <= MATCH_SECONDS:
-                on += on_seconds * overlap / seconds
+            return "approximate"
-                covered += overlap
+        return "incomplete:relay_history"
-            if covered < 0.8 * 900:
+
-                return "unverifiable"
+    def deviation(self, forecast: dict[str, Any], horizon: int) -> tuple[float, float]:
-            if abs(on / covered - forecast["plan"][block]) > PLAN_TOLERANCE:
+        """Seconds up to `horizon` the relay was seen in another state than planned,
-                return "counterfactual"
+        and seconds its state is not known.
-        return None
+
+        Compared per 15-minute plan block, from the relay's on-time per poll interval,
+        itself integrated at the relay's own switch times. So timing counts: the same
+        total on-time at other times than planned is a deviation, while a switch a few
+        seconds off a block boundary is not.
+        """
+        start, end = forecast["time"], forecast["time"] + horizon * 60
+        deviation = covered = unobserved = 0.0
+        for interval_end, on_seconds, seconds in self._relay:
+            lo, hi = max(start, interval_end - seconds), min(end, interval_end)
+            if hi <= lo:
+                continue
+            covered += hi - lo
+            if on_seconds is None:
+                unobserved += hi - lo
+                continue
+            share = on_seconds / seconds
+            t = lo
+            while t < hi:  # split at plan block boundaries
+                block = min(int((t - start) // 900), len(forecast["plan"]) - 1)
+                part_end = min(hi, start + (block + 1) * 900)
+                deviation += (part_end - t) * (share if not forecast["plan"][block] else 1.0 - share)
+                t = part_end
+        return deviation, unobserved + max(0.0, (end - start) - covered)
 
     def _count(self, day: str, horizon: int, verdict: str) -> None:
         counts = self._verdicts.setdefault(day, {}).setdefault(str(horizon), {})
         counts[verdict] = counts.get(verdict, 0) + 1
 
     def forget(self, zone: str) -> None:
-        for by_horizon in self._days.values():
+        for store in (self._days, self._approximate):
-            for by_zone in by_horizon.values():
+            for by_horizon in store.values():
-                by_zone.pop(zone, None)
+                for by_zone in by_horizon.values():
+                    by_zone.pop(zone, None)
         for forecast in self._pending:
             forecast["trajectory"].pop(zone, None)
 
@@ -392,58 +521,70 @@ class OperationalForecastLedger:
     def summary(self, zones: list[str]) -> dict[int, dict[str, Any]]:
         result = {}
         for horizon in HORIZONS:
-            per_zone: dict[str, list[float]] = {}
+            per_zone = _sum_by_zone(self._days, horizon, zones)
-            verdicts = {verdict: 0 for verdict in VERDICTS}
+            approximate = _sum_by_zone(self._approximate, horizon, zones)
-            for by_horizon in self._days.values():
+            counts: dict[str, int] = {}
-                for zone, values in by_horizon.get(str(horizon), {}).items():
-                    if zone in zones:
-                        totals = per_zone.setdefault(zone, [0, 0.0, 0.0, 0.0, 0.0])
-                        for i, value in enumerate(values):
-                            totals[i] += value
             for by_horizon in self._verdicts.values():
                 for verdict, count in by_horizon.get(str(horizon), {}).items():
-                    verdicts[verdict] = verdicts.get(verdict, 0) + count
+                    counts[verdict] = counts.get(verdict, 0) + count
+            reasons = {reason: counts.get(f"incomplete:{reason}", 0) for reason in INCOMPLETE_REASONS}
             overall = [sum(values[i] for values in per_zone.values()) for i in range(5)]
-            result[horizon] = {**_operational_stats(overall, horizon, len(per_zone)), **verdicts,
+            approx_overall = [sum(values[i] for values in approximate.values()) for i in range(5)]
-                               "zones": {zone: _operational_stats(values, horizon)
+            result[horizon] = {
-                                         for zone, values in per_zone.items()}}
+                **_operational_stats(overall, horizon, len(per_zone)),
+                # Forecasts by how the relay compared with their plan.
+                "matched": counts.get("matched", 0), "approximate": counts.get("approximate", 0),
+                "counterfactual": counts.get("counterfactual", 0), "incomplete": sum(reasons.values()),
+                "incomplete_reasons": reasons,
+                "approximate_stats": _operational_stats(approx_overall, horizon, len(approximate)),
+                "zones": {zone: _operational_stats(values, horizon) for zone, values in per_zone.items()},
+            }
         return result
 
     def as_dict(self) -> dict[str, Any]:
-        return {"days": {day: {h: {zone: [v[0], *(round(x, 6) for x in v[1:])] for zone, v in by_zone.items()}
+        def rounded(store):
-                               for h, by_zone in by_horizon.items()} for day, by_horizon in self._days.items()},
+            return {day: {h: {zone: [v[0], *(round(x, 6) for x in v[1:])] for zone, v in by_zone.items()}
-                "verdicts": self._verdicts}
+                          for h, by_zone in by_horizon.items()} for day, by_horizon in store.items()}
+        return {"version": LEDGER_VERSION, "days": rounded(self._days),
+                "approximate": rounded(self._approximate), "verdicts": self._verdicts}
 
     def load(self, data: Any) -> None:
-        self._days, self._verdicts = {}, {}
+        self._days, self._approximate, self._verdicts = {}, {}, {}
-        if not isinstance(data, dict):
+        # 0.4.1 statistics used a looser plan criterion: they are not comparable.
+        if not isinstance(data, dict) or data.get("version") != LEDGER_VERSION:
             return
-        horizons = {str(h) for h in HORIZONS}
+        for name, store in (("days", self._days), ("approximate", self._approximate)):
-        days = data.get("days") if isinstance(data.get("days"), dict) else {}
+            for day, horizon, zone, values in _entries(data.get(name)):
-        for day, by_horizon in days.items():
+                if (isinstance(values, list) and len(values) == 5 and type(values[0]) is int and values[0] >= 0
-            if not _valid_day(day) or not isinstance(by_horizon, dict):
+                        and all(type(v) in (int, float) and isfinite(v) for v in values[1:])
-                continue
+                        and min(values[2:]) >= 0):
-            for horizon, by_zone in by_horizon.items():
+                    store.setdefault(day, {}).setdefault(horizon, {})[zone] = [
-                if horizon not in horizons or not isinstance(by_zone, dict):
+                        values[0], *(float(v) for v in values[1:])]
-                    continue
+        known = {"matched", "approximate", "counterfactual", *(f"incomplete:{r}" for r in INCOMPLETE_REASONS)}
-                for zone, values in by_zone.items():
-                    if (isinstance(zone, str) and isinstance(values, list) and len(values) == 5
-                            and type(values[0]) is int and values[0] >= 0
-                            and all(type(v) in (int, float) and isfinite(v) for v in values[1:])
-                            and min(values[2:]) >= 0):
-                        self._days.setdefault(day, {}).setdefault(horizon, {})[zone] = [
-                            values[0], *(float(v) for v in values[1:])]
         verdicts = data.get("verdicts") if isinstance(data.get("verdicts"), dict) else {}
+        horizons = {str(h) for h in HORIZONS}
         for day, by_horizon in verdicts.items():
             if not _valid_day(day) or not isinstance(by_horizon, dict):
                 continue
             for horizon, counts in by_horizon.items():
                 if horizon in horizons and isinstance(counts, dict):
-                    clean = {k: v for k, v in counts.items() if k in VERDICTS and type(v) is int and v >= 0}
+                    clean = {k: v for k, v in counts.items() if k in known and type(v) is int and v >= 0}
                     if clean:
                         self._verdicts.setdefault(day, {})[horizon] = clean
 
 
+def _sum_by_zone(store: dict[str, dict[str, dict[str, list[float]]]], horizon: int,
+                 zones: list[str]) -> dict[str, list[float]]:
+    totals: dict[str, list[float]] = {}
+    for by_horizon in store.values():
+        for zone, values in by_horizon.get(str(horizon), {}).items():
+            if zone in zones:
+                bucket = totals.setdefault(zone, [0, 0.0, 0.0, 0.0, 0.0])
+                for i, value in enumerate(values):
+                    bucket[i] += value
+    return totals
+
+
 def _valid_day(day: Any) -> bool:
     try:
         datetime.strptime(day, "%Y-%m-%d")

custom_components/heating_mpc/manifest.json

@@ -9,5 +9,5 @@
   "iot_class": "local_polling",
   "requirements": [],
   "single_config_entry": true,
-  "version": "0.4.1"
+  "version": "0.4.2"
 }

custom_components/heating_mpc/sensor.py

@@ -103,6 +103,8 @@ class MPCRoomPrediction(CoordinatorEntity, SensorEntity):
             "forecast_15min": trajectory or None,
             "plan_15min": data.get("plan_15min"),
             "basis": "proposed MPC plan; counterfactual while Generic Thermostat controls the relay",
+            # Windows with an unknown state: the forecast assumes their state (see Status).
+            "uncertain_windows": details.get("uncertain_windows") or [],
             "model_in_use": details.get("model_in_use"),
             "contributions_c_per_h": details.get("contributions_c_per_h"),
             "coefficients": details.get("coefficients"),
@@ -178,6 +180,11 @@ class MPCForecastError(CoordinatorEntity, SensorEntity):
             "skill": accuracy.get("skill"),
             "base_model_mae": accuracy.get("base_model_mae"),
             "improvement_vs_base": accuracy.get("improvement_vs_base"),
+            # Spans left out of the figures above: an input the model needed was unknown
+            # (the base model's error on them is still measured), or no replay was possible.
+            "incomplete": accuracy.get("incomplete", 0),
+            "incomplete_base_model_mae": accuracy.get("incomplete_base_model_mae"),
+            "skipped": accuracy.get("skipped", 0),
             # Per zone, by name.
             "zones": {self.coordinator.zones[key].name if key in self.coordinator.zones else key: stats
                       for key, stats in accuracy.get("rooms", {}).items()},
@@ -222,7 +229,8 @@ class MPCOperationalForecastError(CoordinatorEntity, SensorEntity):
             horizons[f"{horizon}_min"] = {**{k: v for k, v in stats.items() if k != "zones"},
                                           "zones": {names.get(z, z): v for z, v in stats.get("zones", {}).items()}}
         return {
-            "kind": "forecast accuracy: published forecasts, scored only where the relay followed the plan",
+            "kind": ("forecast accuracy: published forecasts, scored only where the relay matched the plan "
+                     "(at most 2 minutes in another state); approximate matches (up to 5 minutes) apart"),
             "horizons": horizons,
             "window_days": WINDOW_DAYS,
             "unit_of_attributes": "°C",

custom_components/heating_mpc/strings.json

@@ -56,6 +56,13 @@
   "options": {
     "step": {
       "init": {
+        "title": "Heating MPC",
+        "menu_options": {
+          "settings": "Settings",
+          "reset_model": "Reset learned model"
+        }
+      },
+      "settings": {
         "title": "Heating MPC options",
         "description": "Change room sensors and controller settings. This integration remains shadow-only.",
         "data": {
@@ -83,10 +90,17 @@
           "window_open_minutes": "Forecasts assume an open window closes after this many more minutes, then the room recovers.",
           "recovery_minutes": "Time constant of the air warming back towards the walls' temperature.",
           "ventilation_wind": "Needs wind sources; direction also needs wind direction sources.",
-          "ventilation_gust": "Adds a term for gusts above the mean wind. Keep it only if Forecast Error improves.",
+          "ventilation_gust": "Adds a term for gusts above the mean wind. Keep it only if Model Error improves.",
           "ventilation_cross": "Adds a term for windows open in several zones at once. Needs windows in two or more zones."
         }
       },
+      "reset_model": {
+        "title": "Reset learned model",
+        "description": "Forgets everything the model has learned: every zone's thermal coefficients (also those kept for removed zones), their confidence and sample counts, and the Model Error and Forecast Error statistics.\n\nKept: zones, sensors and weights, heat sources, windows, weather sources and all settings, plus the heat sources' baselines and the window event log.\n\nThe integration restarts and learns from the beginning: the first sample after about 30 minutes, forecasts after a day or more. This cannot be undone. The boiler and the thermostat are not affected.",
+        "data": {
+          "confirm_reset": "Yes, forget the learned model"
+        }
+      },
       "weather": {
         "title": "Weather source priority",
         "description": "Choose as many sensors per metric as needed and arrange them in priority order. If the preferred source is unavailable, the next one is used automatically. Outdoor temperature is mandatory; wind and cloud coverage are diagnostic-only in this version.",
@@ -113,6 +127,10 @@
     },
     "error": {
       "invalid_weather": "Select at least one outdoor temperature sensor, distinct from room sensors."
+    },
+    "abort": {
+      "model_reset": "The learned model was reset. Learning starts again from the beginning.",
+      "model_not_reset": "Nothing was changed: the reset was not confirmed."
     }
   },
   "entity": {

custom_components/heating_mpc/translations/en.json

@@ -56,6 +56,13 @@
   "options": {
     "step": {
       "init": {
+        "title": "Heating MPC",
+        "menu_options": {
+          "settings": "Settings",
+          "reset_model": "Reset learned model"
+        }
+      },
+      "settings": {
         "title": "Heating MPC options",
         "description": "Change room sensors and controller settings. This integration remains shadow-only.",
         "data": {
@@ -83,10 +90,17 @@
           "window_open_minutes": "Forecasts assume an open window closes after this many more minutes, then the room recovers.",
           "recovery_minutes": "Time constant of the air warming back towards the walls' temperature.",
           "ventilation_wind": "Needs wind sources; direction also needs wind direction sources.",
-          "ventilation_gust": "Adds a term for gusts above the mean wind. Keep it only if Forecast Error improves.",
+          "ventilation_gust": "Adds a term for gusts above the mean wind. Keep it only if Model Error improves.",
           "ventilation_cross": "Adds a term for windows open in several zones at once. Needs windows in two or more zones."
         }
       },
+      "reset_model": {
+        "title": "Reset learned model",
+        "description": "Forgets everything the model has learned: every zone's thermal coefficients (also those kept for removed zones), their confidence and sample counts, and the Model Error and Forecast Error statistics.\n\nKept: zones, sensors and weights, heat sources, windows, weather sources and all settings, plus the heat sources' baselines and the window event log.\n\nThe integration restarts and learns from the beginning: the first sample after about 30 minutes, forecasts after a day or more. This cannot be undone. The boiler and the thermostat are not affected.",
+        "data": {
+          "confirm_reset": "Yes, forget the learned model"
+        }
+      },
       "weather": {
         "title": "Weather source priority",
         "description": "Choose as many sensors per metric as needed and arrange them in priority order. If the preferred source is unavailable, the next one is used automatically. Outdoor temperature is mandatory; wind and cloud coverage are diagnostic-only in this version.",
@@ -113,6 +127,10 @@
     },
     "error": {
       "invalid_weather": "Select at least one outdoor temperature sensor, distinct from room sensors."
+    },
+    "abort": {
+      "model_reset": "The learned model was reset. Learning starts again from the beginning.",
+      "model_not_reset": "Nothing was changed: the reset was not confirmed."
     }
   },
   "entity": {

custom_components/heating_mpc/translations/ro.json

@@ -56,6 +56,13 @@
   "options": {
     "step": {
       "init": {
+        "title": "Heating MPC",
+        "menu_options": {
+          "settings": "Setări",
+          "reset_model": "Resetează modelul învățat"
+        }
+      },
+      "settings": {
         "title": "Configurare Heating MPC",
         "description": "Modifică senzorii, ponderile și optimizatorul. Rămâne activ doar modul de observație.",
         "data": {
@@ -83,10 +90,17 @@
           "window_open_minutes": "Prognozele presupun că o fereastră deschisă se închide după atâtea minute, apoi camera revine.",
           "recovery_minutes": "Constanta de timp a aerului care se încălzește din nou spre temperatura pereților.",
           "ventilation_wind": "Necesită surse de vânt; direcția necesită și surse pentru direcția vântului.",
-          "ventilation_gust": "Adaugă un termen pentru rafalele peste vântul mediu. Păstrează-l doar dacă eroarea de prognoză scade.",
+          "ventilation_gust": "Adaugă un termen pentru rafalele peste vântul mediu. Păstrează-l doar dacă Model Error scade.",
           "ventilation_cross": "Adaugă un termen pentru ferestre deschise în mai multe zone deodată. Necesită ferestre în cel puțin două zone."
         }
       },
+      "reset_model": {
+        "title": "Resetează modelul învățat",
+        "description": "Șterge tot ce a învățat modelul: coeficienții termici ai fiecărei zone (inclusiv cei păstrați pentru zonele eliminate), încrederea și numărul de eșantioane, precum și statisticile Model Error și Forecast Error.\n\nSe păstrează: zonele, senzorii și ponderile, sursele de căldură, ferestrele, sursele meteo și toate setările, plus nivelurile de bază ale surselor de căldură și jurnalul ferestrelor.\n\nIntegrarea repornește și învață de la început: primul eșantion după aproximativ 30 de minute, prognozele după o zi sau mai mult. Operația nu poate fi anulată. Centrala și termostatul nu sunt afectate.",
+        "data": {
+          "confirm_reset": "Da, șterge modelul învățat"
+        }
+      },
       "weather": {
         "title": "Prioritatea surselor meteo",
         "description": "Alege câți senzori dorești pentru fiecare variabilă și ordonează-i după prioritate. Când prima sursă este indisponibilă, integrarea încearcă automat următoarea. Temperatura exterioară este obligatorie; vântul și nebulozitatea sunt acum doar diagnostice.",
@@ -113,6 +127,10 @@
     },
     "error": {
       "invalid_weather": "Alege cel puțin un senzor de temperatură exterioară, diferit de senzorii camerelor."
+    },
+    "abort": {
+      "model_reset": "Modelul învățat a fost resetat. Învățarea reîncepe de la zero.",
+      "model_not_reset": "Nu s-a modificat nimic: resetarea nu a fost confirmată."
     }
   },
   "entity": {

custom_components/heating_mpc/windows.py

@@ -150,17 +150,22 @@ def ventilation_vector(features: Iterable[str], temp: float, outside: float,
                        inputs: Mapping[str, Any]) -> list[float] | None:
     """Ventilation feature values for one zone and step; None if an input is unknown.
 
-    `inputs`: opening, facing (sum of opening x cos), other_opening, recovery,
+    `inputs`: opening, facing (sum of opening x cos; None when the wind direction is
-    t_ref (pre-ventilation temperature), wind, gust.
+    unknown), other_opening (None when another zone's windows are unknown),
+    recovery, t_ref (pre-ventilation temperature), wind, gust. With every window
+    closed the opening terms are 0 whatever the weather, so only the opening and,
+    while recovering, the reference temperature must be known.
     """
     difference = outside - temp
+    opening = inputs.get("opening")
+    if opening is None:
+        return None
     values = []
     for name in features:
-        opening = inputs.get("opening")
-        if opening is None:
-            return None
         if name == "vent_natural":
             values.append(opening * difference)
+        elif name in ("vent_wind", "vent_wind_dir", "vent_gust", "vent_cross") and opening == 0:
+            values.append(0.0)
         elif name in ("vent_wind", "vent_wind_dir", "vent_gust"):
             wind = inputs.get("wind")
             if wind is None:
@@ -178,7 +183,10 @@ def ventilation_vector(features: Iterable[str], temp: float, outside: float,
                     return None
                 values.append(opening * max(0.0, gust - wind) * difference)
         elif name == "vent_cross":
-            values.append(opening * (inputs.get("other_opening") or 0.0) * difference)
+            other = inputs.get("other_opening")
+            if other is None:
+                return None
+            values.append(opening * other * difference)
         elif name == "recovery":
             recovery = inputs.get("recovery") or 0.0
             t_ref = inputs.get("t_ref")
@@ -203,16 +211,36 @@ def ventilation_vector_for_prediction(features: Iterable[str], temp: float, outs
     if x is None and inputs.get("opening") is not None:
         x = ventilation_vector(features, temp, outside, {
             **inputs, "wind": inputs.get("wind") or 0.0, "gust": inputs.get("gust") or inputs.get("wind") or 0.0,
-            "facing": inputs.get("facing") or 0.0})
+            "facing": inputs.get("facing") or 0.0, "other_opening": inputs.get("other_opening") or 0.0})
     return x
 
 
+def ventilation_known(features: Iterable[str], inputs: Mapping[str, Any] | None) -> bool:
+    """True when a zone's ventilation term follows from observed inputs alone.
+
+    False while a window state is unknown or a recovery's start is unconfirmed,
+    or while a window is open and a term it activates lacks its weather input.
+    Zones without windows (`inputs` None or empty) need nothing.
+    """
+    if not inputs:
+        return True
+    return ventilation_vector(list(features), 0.0, 0.0, inputs) is not None
+
+
 def _iso(timestamp: float) -> str:
     return datetime.fromtimestamp(timestamp, timezone.utc).isoformat(timespec="seconds")
 
 
 class VentilationTracker:
-    """Window states, ventilation inputs per zone, recovery after closing, event log."""
+    """Window states, ventilation inputs per zone, recovery after closing, event log.
+
+    Each window is OPEN, CLOSED or UNKNOWN (unknown, unavailable or missing).
+    Unknown never means closed. The last known state is kept apart from the
+    current one, and an opening or closing counts as confirmed only when it was
+    seen: the previous reading was the other state, with no unknown period in
+    between. Last known states, open events and recovery are saved so a restart
+    or reload continues them; they come back as unknown until the sensor reports.
+    """
 
     def __init__(self, windows: list[Window], recovery_minutes: float = DEFAULT_RECOVERY_MINUTES,
                  saved: Any = None) -> None:
@@ -223,25 +251,24 @@ class VentilationTracker:
         self._t_ref: dict[str, float | None] = {}  # zone -> temperature before it opened
         self._last_temps: dict[str, float | None] = {}
         self._open_event: dict[str, dict[str, Any]] = {}
-        # window -> last known open/closed state, since when, and since when it is unknown.
+        # window -> last known open/closed state, since when, when it was last seen
+        # and since when it is unknown (None while known).
         self._memory: dict[str, dict[str, Any]] = {}
         self._recovery_confirmed: dict[str, bool] = {}
+        self._last_update: float | None = None
+        self._restored_at: float | None = None
+        self.changed = False  # something worth saving happened since the last save
         saved = saved if isinstance(saved, Mapping) else {}
         events = saved.get("events") if isinstance(saved.get("events"), list) else []
         self.events: deque[dict[str, Any]] = deque(
             (e for e in events if isinstance(e, dict)), maxlen=MAX_EVENTS)
         self.window_state: dict[str, dict[str, Any]] = {}
         self.zone_state: dict[str, dict[str, Any]] = {}
+        self._restore(saved.get("state"))
 
     def update(self, now: float, states: Mapping[str, tuple[str | None, float | None]],
                temps: Mapping[str, float | None], weather: Mapping[str, float | None]) -> None:
-        """One poll. `states`: window key -> (binary sensor state, last_changed timestamp).
+        """One poll. `states`: window key -> (binary sensor state, last_changed timestamp)."""
-
-        Each window is OPEN, CLOSED or UNKNOWN (unknown/unavailable/missing). Unknown
-        never means closed: the last known state is kept, an open window's event
-        stays open, and a close that happened while the sensor was unknown is
-        recorded as unconfirmed.
-        """
         self.window_state = {}
         zones: dict[str, dict[str, Any]] = {}
         for window in self.windows:
@@ -254,11 +281,12 @@ class VentilationTracker:
             if previous and changed is not None and 0 < now - previous[0] <= 900:
                 fraction = open_fraction(previous[1], is_open, changed, previous[0], now)
             self._previous[window.key] = (now, is_open)
-            memory = self._memory.setdefault(window.key, {"last_known": None, "since": None, "unknown_since": None})
+            memory = self._memory.setdefault(window.key, {"last_known": None, "since": None, "seen": None,
+                                                          "unknown_since": None})
             zone = zones.setdefault(window.zone, {
                 "opening": 0.0, "facing_unit": 0.0, "known": True, "open_now": False, "possibly_open": False,
                 "open_windows": [], "last_close": None, "close_confirmed": True, "first_open": None,
-                "open_confirmed": True, "unknown_windows": []})
+                "open_confirmed": True, "unknown_windows": [], "facing_known": True})
             if fraction is None:
                 zone["known"] = False
             else:
@@ -266,21 +294,26 @@ class VentilationTracker:
                 zone["facing_unit"] += window.opening * fraction * (
                     facing_cos(weather["wind_direction"], window.azimuth)
                     if weather.get("wind_direction") is not None else 0.0)
+                if fraction > 0 and weather.get("wind_direction") is None:
+                    zone["facing_known"] = False
             if is_open is None:
                 if memory["unknown_since"] is None:
-                    memory["unknown_since"] = previous[0] if previous and previous[1] is not None else now
+                    memory["unknown_since"] = memory["seen"] if memory["seen"] is not None else now
+                    self.changed = True
                 zone["unknown_windows"].append(window.name)
                 if memory["last_known"]:
                     zone["possibly_open"] = True  # last seen open: still treated as open
             else:
-                after_outage = memory["unknown_since"] is not None
+                if memory["last_known"] is not is_open:  # a change, or the first reading
-                if memory["last_known"] is not is_open:
                     memory["since"] = changed
-                    if is_open and after_outage:
+                    # Seen only if the previous reading was the other state, with no gap.
-                        zone["open_confirmed"] = False  # opened at some point during the outage
+                    witnessed = memory["last_known"] is not None and memory["unknown_since"] is None
-                    if is_open is False and memory["last_known"] and after_outage:
+                    if not witnessed:
-                        zone["close_confirmed"] = False  # closed at some point during the outage
+                        zone["open_confirmed" if is_open else "close_confirmed"] = False
-                memory["last_known"], memory["unknown_since"] = is_open, None
+                    self.changed = True
+                elif memory["unknown_since"] is not None:
+                    self.changed = True  # the same state again after a gap
+                memory.update(last_known=is_open, seen=now, unknown_since=None)
                 if is_open:
                     zone["open_now"] = True
                     zone["open_windows"].append(window.name)
@@ -290,13 +323,21 @@ class VentilationTracker:
                     zone["last_close"] = max(zone["last_close"] or 0.0, changed)
             self.window_state[window.key] = {
                 "open": is_open, "since": changed, "last_known_open": memory["last_known"],
-                "last_known_since": memory["since"], "unknown_since": memory["unknown_since"],
+                "last_known_since": memory["since"], "seen": memory["seen"],
+                "unknown_since": memory["unknown_since"],
                 "relative_wind_direction": (round(angle_difference(weather["wind_direction"], window.azimuth), 1)
                                             if weather.get("wind_direction") is not None else None)}
+        # Time the integration was not running counts as unknown for events it interrupted.
+        gap = now - self._restored_at if self._restored_at is not None else 0.0
+        self._restored_at = None
         for key, zone in zones.items():
+            if gap > 330 and key in self._open_event:
+                event = self._open_event[key]
+                event["sensor_unknown_minutes"] = round(event.get("sensor_unknown_minutes", 0.0) + gap / 60, 1)
             self._update_zone(now, key, zone, temps.get(key), weather, zones)
         self.zone_state = zones
         self._last_temps = dict(temps)
+        self._last_update = now
 
     def _update_zone(self, now: float, key: str, zone: dict[str, Any], temp: float | None,
                      weather: Mapping[str, float | None], zones: Mapping[str, dict[str, Any]]) -> None:
@@ -314,32 +355,41 @@ class VentilationTracker:
                 "wind_direction": weather.get("wind_direction"), "wind_gust": weather.get("wind_gust"),
                 "windows": list(zone["open_windows"]),
                 "other_open_windows": [n for k, z in zones.items() if k != key for n in z["open_windows"]],
-                "lowest_temperature": temp, "sensor_unknown_minutes": 0.0,
+                "lowest_temperature": temp, "sensor_unknown_minutes": 0.0, "last_seen_open": _iso(now),
             }
             if not zone["open_confirmed"]:
                 self._t_ref[key] = None  # the room may already have cooled before we saw it
+            self.changed = True
         elif ventilating and was_open:
             event = self._open_event[key]
             if temp is not None:
                 event["lowest_temperature"] = min(t for t in (event["lowest_temperature"], temp) if t is not None)
             if zone["unknown_windows"]:
                 event["sensor_unknown_minutes"] = round(event["sensor_unknown_minutes"] + 5.0, 1)
+            if zone["open_now"]:
+                event["last_seen_open"] = _iso(now)
         elif was_open and not ventilating:
             event = self._open_event.pop(key)
-            if zone["close_confirmed"]:
+            opened = _parse(event["opened"])  # the confirmed opening, or when it was first seen open
-                closed = zone["last_close"] or now
+            seen_open = _parse(event.get("last_seen_open", event["opened"]))
-                event.update(closed=_iso(closed), closed_confirmed=True,
+            closed = zone["last_close"] or now if zone["close_confirmed"] else now
-                             duration_minutes=round((closed - _parse(event["opened"])) / 60, 1))
+            confirmed = zone["close_confirmed"] and event.get("opened_confirmed", True)
-            else:
+            # Uncertain periods give a range instead of a duration.
-                # Closed while the sensor was unknown: no confirmed time, only a range.
+            event.update(
-                closed = now
+                closed=_iso(closed) if zone["close_confirmed"] else None,
-                event.update(closed=None, closed_confirmed=False, closed_reported=_iso(now),
+                closed_confirmed=zone["close_confirmed"],
-                             duration_minutes=None,
+                duration_minutes=round((closed - opened) / 60, 1) if confirmed else None,
-                             duration_max_minutes=round((now - _parse(event["opened"])) / 60, 1))
+                duration_min_minutes=round(((closed if zone["close_confirmed"] else seen_open) - opened) / 60, 1),
+                duration_max_minutes=(round((now - opened) / 60, 1)
+                                      if not zone["close_confirmed"] and event.get("opened_confirmed", True)
+                                      else None))
+            if not zone["close_confirmed"]:
+                event["closed_reported"] = _iso(now)  # closed at some point before this
             self._closed_at[key] = closed
-            self._recovery_confirmed[key] = zone["close_confirmed"] and event.get("opened_confirmed", True)
+            self._recovery_confirmed[key] = confirmed
             event["temperature_at_close"] = temp
             self.events.append(event)
+            self.changed = True
         elif not ventilating and key in self._closed_at and temp is not None:
             # Fill in the temperature half an hour after closing, for recovery review.
             for event in reversed(self.events):
@@ -354,7 +404,8 @@ class VentilationTracker:
             recovery = 0.0
         zone.update(recovery=recovery, t_ref=self._t_ref.get(key),
                     recovery_confirmed=self._recovery_confirmed.get(key, True),
-                    other_opening=sum(z["opening"] for k, z in zones.items() if k != key and z["known"]))
+                    other_opening=sum(z["opening"] for k, z in zones.items() if k != key and z["known"]),
+                    others_known=all(z["known"] for k, z in zones.items() if k != key))
         # Learning of the closed-window model is unsafe while anything ventilation-related goes on.
         zone["affected"] = (not zone["known"]) or zone["opening"] > 0 or ventilating or recovery > 0
 
@@ -368,49 +419,139 @@ class VentilationTracker:
         if zone is None:
             return None
         known = zone["known"] and (zone["recovery"] == 0 or zone["recovery_confirmed"])
-        return {"opening": zone["opening"] if known else None, "facing": zone["facing_unit"],
+        return {"opening": zone["opening"] if known else None,
-                "other_opening": zone["other_opening"], "recovery": zone["recovery"],
+                "facing": zone["facing_unit"] if zone["facing_known"] else None,
+                "other_opening": zone["other_opening"] if zone["others_known"] else None,
+                "recovery": zone["recovery"],
                 "t_ref": zone["t_ref"], "wind": weather.get("wind"), "gust": weather.get("wind_gust"),
                 "affected": zone["affected"], "known": known,
                 "unknown_windows": list(zone["unknown_windows"])}
 
+    def assumption(self, window: Window, open_minutes: float, now: float) -> dict[str, Any]:
+        """What forecasts assume for one window, and on what basis.
+
+        Observed open: open for `open_minutes` more. Unknown but last seen open: open
+        until `open_minutes` after it was last seen, never renewed, so an unavailable
+        sensor does not keep a window open forever. Unknown otherwise: no ventilation,
+        marked uncertain (`last_known_closed`, or `unobserved` with no reading at all).
+        """
+        state = self.window_state.get(window.key, {})
+        if state.get("open"):
+            return {"basis": "observed", "open_minutes": float(open_minutes), "uncertain": False}
+        if state.get("open") is False:
+            return {"basis": "observed", "open_minutes": 0.0, "uncertain": False}
+        if state.get("last_known_open"):
+            seen = state.get("seen")
+            left = open_minutes - (now - seen) / 60 if seen is not None else 0.0
+            return {"basis": "last_known_open", "open_minutes": round(max(0.0, left), 1), "uncertain": True,
+                    "assumed_closed_at": seen + open_minutes * 60 if seen is not None else now}
+        basis = "last_known_closed" if state.get("last_known_open") is False else "unobserved"
+        return {"basis": basis, "open_minutes": 0.0, "uncertain": True}
+
     def forecast(self, key: str, steps: int, open_minutes: float, weather: Mapping[str, float | None],
                  now: float) -> list[dict[str, Any]]:
-        """Assumed ventilation inputs per forecast step.
+        """Assumed ventilation inputs per forecast step (see `assumption`).
 
-        Windows open now (or unknown but last seen open) are assumed to close after
+        Each window open now, or assumed open, closes after its assumed time; once
-        `open_minutes` (at least one step); after that the zone recovers. Weather
+        the last one has closed the zone recovers. Weather is held at current values.
-        is held at current values.
         """
         zone = self.zone_state.get(key)
         if zone is None:
             return [{} for _ in range(steps)]
-        windows = [w for w in self.windows if w.zone == key and w.enabled
+        windows = [w for w in self.windows if w.zone == key and w.enabled]
-                   and (self.window_state.get(w.key, {}).get("open")
+        assumed = {w.key: self.assumption(w, open_minutes, now) for w in windows}
-                        or (self.window_state.get(w.key, {}).get("open") is None
+        # Steps each window stays open: at least one for an observed open window.
-                            and self.window_state.get(w.key, {}).get("last_known_open")))]
+        open_steps = {w.key: (max(1, round(open_minutes / 5)) if self.window_state.get(w.key, {}).get("open")
-        opening = sum(w.opening for w in windows)
+                              else round(assumed[w.key]["open_minutes"] / 5)) for w in windows}
-        facing = sum(w.opening * facing_cos(weather["wind_direction"], w.azimuth) for w in windows) \
+        direction = weather.get("wind_direction")
-            if weather.get("wind_direction") is not None else 0.0
+        last_open = max(open_steps.values(), default=0)
-        open_steps = max(1, round(open_minutes / 5)) if windows else 0
+        if last_open:
-        closed_at = self._closed_at.get(key)
+            start = now + last_open * 300.0
+        else:
+            # Recovery from the last close: observed, or assumed for a window last seen open.
+            closes = [a["assumed_closed_at"] for a in assumed.values() if "assumed_closed_at" in a]
+            closed_at = self._closed_at.get(key)
+            start = max([t for t in (closed_at, *closes) if t is not None], default=None)
         result = []
         for k in range(steps):
             t = now + (k + 1) * 300.0
-            if k < open_steps:
+            open_now = [w for w in windows if k < open_steps[w.key]]
-                result.append({"opening": opening, "facing": facing, "recovery": 0.0, "t_ref": zone["t_ref"],
+            if open_now:
-                               "other_opening": zone["other_opening"], "wind": weather.get("wind") or 0.0,
+                result.append({
-                               "gust": weather.get("wind_gust") or weather.get("wind") or 0.0})
+                    "opening": sum(w.opening for w in open_now),
+                    "facing": (sum(w.opening * facing_cos(direction, w.azimuth) for w in open_now)
+                               if direction is not None else 0.0),
+                    "recovery": 0.0, "t_ref": zone["t_ref"], "other_opening": zone["other_opening"],
+                    "wind": weather.get("wind") or 0.0,
+                    "gust": weather.get("wind_gust") or weather.get("wind") or 0.0})
                 continue
-            start = now + open_steps * 300.0 if windows else closed_at
+            recovery = exp(-(t - start) / self.tau) if start is not None and t > start else 0.0
-            recovery = exp(-(t - start) / self.tau) if start is not None else 0.0
             result.append({"opening": 0.0, "facing": 0.0, "other_opening": 0.0, "wind": 0.0, "gust": 0.0,
                            "recovery": recovery if recovery >= RECOVERY_THRESHOLD else 0.0,
                            "t_ref": zone["t_ref"]})
         return result
 
     def as_dict(self) -> dict[str, Any]:
-        return {"events": list(self.events)}
+        """Events, plus the state a restart or reload needs to continue them."""
+        windows = {w.key: w for w in self.windows if w.enabled}
+        state = {
+            "time": self._last_update,
+            "windows": {key: {"sensor": windows[key].sensor, "inverted": windows[key].inverted,
+                              "last_known": m["last_known"], "since": m["since"], "seen": m["seen"]}
+                        for key, m in self._memory.items()
+                        if key in windows and m["last_known"] is not None and m["seen"] is not None},
+            "zones": {zone: {"open_event": self._open_event.get(zone), "closed_at": self._closed_at.get(zone),
+                             "recovery_confirmed": self._recovery_confirmed.get(zone, True),
+                             "t_ref": self._t_ref.get(zone)}
+                      for zone in {w.zone for w in windows.values()}
+                      if zone in self._open_event or zone in self._closed_at},
+        }
+        return {"events": list(self.events), "state": state}
+
+    def _restore(self, state: Any) -> None:
+        """Continue from saved state. Nothing restored is live: every window is unknown
+        from when it was last seen until its sensor reports again."""
+        if not isinstance(state, Mapping):
+            return
+        windows = {w.key: w for w in self.windows if w.enabled}
+        saved_windows = state.get("windows") if isinstance(state.get("windows"), Mapping) else {}
+        for key, item in saved_windows.items():
+            window = windows.get(key)
+            if (window is None or not isinstance(item, Mapping) or item.get("sensor") != window.sensor
+                    or item.get("inverted") is not window.inverted or not isinstance(item.get("last_known"), bool)
+                    or not _finite(item.get("seen"))):
+                continue  # another sensor now, or polarity flipped: nothing carries over
+            seen = float(item["seen"])
+            self._memory[key] = {"last_known": item["last_known"], "seen": seen, "unknown_since": seen,
+                                 "since": float(item["since"]) if _finite(item.get("since")) else None}
+        remembered_open = {windows[key].zone for key, m in self._memory.items() if m["last_known"]}
+        saved_zones = state.get("zones") if isinstance(state.get("zones"), Mapping) else {}
+        for zone, item in saved_zones.items():
+            if zone not in {w.zone for w in windows.values()} or not isinstance(item, Mapping):
+                continue
+            event = item.get("open_event")
+            # An open event continues only while one of its windows is remembered open.
+            if isinstance(event, dict) and zone in remembered_open and _valid_iso(event.get("opened")):
+                self._open_event[zone] = dict(event)
+            if _finite(item.get("closed_at")):
+                self._closed_at[zone] = float(item["closed_at"])
+                self._recovery_confirmed[zone] = item.get("recovery_confirmed") is True
+            if _finite(item.get("t_ref")):
+                self._t_ref[zone] = float(item["t_ref"])
+        if self._open_event and _finite(state.get("time")):
+            self._restored_at = float(state["time"])
+
+
+def _finite(value: Any) -> bool:
+    return isinstance(value, (int, float)) and not isinstance(value, bool) and isfinite(value)
+
+
+def _valid_iso(value: Any) -> bool:
+    try:
+        datetime.fromisoformat(value)
+        return True
+    except (TypeError, ValueError):
+        return False
 
 
 def _parse(iso: str) -> float:

docs/MODEL.md

@@ -98,11 +98,19 @@ known. While a window is open, possibly open (its sensor is unknown and it was l
 seen open) or the zone is recovering, the base and extended layers do **not**
 learn, so window events cannot inflate `loss` or shift `bias`.
 
-An unknown window state is never read as closed. A window last seen open stays
+An unknown window state is never read as closed, and an opening or closing counts
-assumed open in forecasts; one last seen closed contributes no ventilation term.
+as confirmed only when it was seen: the previous reading was the other state with
-If a window closes while its sensor was unknown, the event's duration is reported
+no unknown period (or restart) in between. If a window closes while its sensor was
-as a range and the recovery term is not learned from it, because the closing time
+unknown, the event's duration is reported as a range and the recovery term is not
-is not known.
+learned from it, because the closing time is not known. Last known states, open
+events and recovery are saved and restored across restarts, as unknown until the
+sensor reports again.
+
+An input that a term needs is never filled in for learning or for Model Error:
+while a window is open, the wind, its direction (for `vent_wind_dir`) and the other
+zones' windows (for `vent_cross`) must be known. With every window closed, the
+opening terms are 0 and need no weather. Forecasts, which must produce a number,
+assume calm air for a missing wind input.
 
 `vent_wind_dir` is bounded by `vent_wind` (|vent_wind_dir| ≤ vent_wind), so the
 combined wind term never goes negative: a leeward window still ventilates.
@@ -202,7 +210,10 @@ minutes):
   its response time. A gaming PC drawing 600 W now is not assumed to run on for two
   hours.
 * **Windows:** an open window is assumed to stay open for a configurable time
-  (default 15 minutes), then close; the zone then recovers.
+  (default 15 minutes), then close; the zone then recovers. A window whose sensor
+  is unknown but was last seen open is assumed open only until that time after it
+  was last seen, never renewed. One last seen closed, or never seen since setup, is
+  assumed closed, and the forecast is flagged as uncertain (`uncertain_windows`).
 * **Relay:** the candidate plan being evaluated.
 
 For the plan search the step is compiled into exact per-zone coefficients (every
@@ -228,13 +239,34 @@ scored on exactly the same spans:
 * `base_model_mae`, `improvement_vs_base`: against the base layer alone. A feature
   that does not improve this should be switched off (for example the gust term).
 
+A span counts only if every input the zone's model used was observed at every
+step (see *Ventilation layer*; for the extended layer also electrical heat and
+neighbours, whose own replay must be complete). Otherwise it is counted as
+`incomplete`, with the base model's error on it kept apart
+(`incomplete_base_model_mae`), since the base model needs only the relay and the
+outdoor temperature. Spans that cannot be replayed (uncertain radiator lag, no
+reading at the end) are counted as `skipped`.
+
 **Forecast accuracy (Forecast Error sensor)** scores forecasts as published: the
 start readings, trajectory, plan, model revision and assumptions of the moment
 they were made, nothing replaced with hindsight. Because the plan is the MPC's
-proposal while Generic Thermostat controls the relay, a forecast is scored at a
+proposal while Generic Thermostat controls the relay, a forecast is compared only
-horizon only if the relay was in the planned state for at least 12 minutes of
+when the relay followed it. The deviation is the time, up to the horizon, the
-every 15-minute block up to it; otherwise it is counted as `counterfactual` (or
+relay spent in another state than planned, compared block by block from the
-`unverifiable` when the relay history is incomplete) and never as an error.
+relay's own switch times; unobserved relay time counts as possible deviation.
+
+| Deviation (worst case) | Class | Used for |
+|---|---|---|
+| ≤ 2 min | matched | Forecast Error figures |
+| ≤ 5 min, ≤ 2 min unobserved | approximate | `approximate_stats` only |
+| > 5 min observed | counterfactual | counted, never an error |
+| otherwise | incomplete | counted, with a reason |
+
+Forecasts made with an assumed input (an unknown window or electrical heat) are
+incomplete as well. The limits follow from the model: d seconds of unplanned
+boiler heat change a room by about heat × d / 300 with the learned `heat`
+coefficient (typically 0.05–0.12 °C per 5 minutes), so 2 minutes stay below half a
+0.1 °C sensor step and 5 minutes are about one step.
 
 Both report how much evidence there is. Forecasts start every 5 minutes, so at
 horizon h consecutive forecasts share most of their span, and all zones share one
@@ -267,5 +299,8 @@ the contamination the separate ventilation layer prevents.
   variation their split is uncertain even when their combined effect is accurate.
 * Forecasts hold weather constant and use one scenario for electrical heat and
   windows; no forecast uncertainty band is computed yet.
-* Window sensors with a permanently unknown state keep their zone out of learning;
+* Window sensors with a permanently unknown state keep their zone out of learning
-  disable such a window.
+  and its Model Error spans incomplete; disable such a window.
+* A window that is unknown and was last seen closed (or never seen) is assumed
+  closed in forecasts, flagged as uncertain; no open/closed scenario pair is
+  computed.

tests/ha/test_concurrency.py

@@ -0,0 +1,134 @@
+"""Unload, reload and removal while work is still in flight, in the real HA lifecycle."""
+import asyncio
+import threading
+from datetime import timedelta
+from unittest.mock import patch
+
+from homeassistant.helpers import entity_registry as er
+from homeassistant.helpers.storage import Store
+from homeassistant.util import dt as dt_util
+from pytest_homeassistant_custom_component.common import async_fire_time_changed
+
+from custom_components.heating_mpc import coordinator as coordinator_module
+from custom_components.heating_mpc.const import DOMAIN
+
+REAL_OPTIMIZE = coordinator_module.optimize
+
+
+async def setup(hass, entry):
+    entry.add_to_hass(hass)
+    assert await hass.config_entries.async_setup(entry.entry_id)
+    await hass.async_block_till_done()
+    return hass.data[DOMAIN][entry.entry_id]
+
+
+def trained(coordinator):
+    model = coordinator.learners["sensor.room"]
+    model.count, model.on_count, model.off_count = 250, 125, 125
+    coordinator._update_counts()
+
+
+class BlockedOptimizer:
+    """Holds the optimizer in its worker thread until released."""
+
+    def __init__(self):
+        self.started, self.release = threading.Event(), threading.Event()
+
+    def __call__(self, *args, **kwargs):
+        self.started.set()
+        self.release.wait(5)
+        return REAL_OPTIMIZE(*args, **kwargs)
+
+    async def wait_started(self):
+        # Polled on the event loop: the test executor may run one job at a time.
+        for _ in range(500):
+            if self.started.is_set():
+                return True
+            await asyncio.sleep(0.01)
+        return False
+
+
+def status_id(hass, entry):
+    return er.async_get(hass).async_get_entity_id("sensor", DOMAIN, f"{entry.entry_id}_status")
+
+
+async def test_unload_during_optimization_publishes_nothing(hass, entry, inputs):
+    coordinator = await setup(hass, entry)
+    trained(coordinator)
+    shown = coordinator.data
+    blocked = BlockedOptimizer()
+    with patch.object(coordinator_module, "optimize", blocked):
+        refresh = hass.async_create_task(coordinator.async_refresh())
+        assert await blocked.wait_started()
+        assert await hass.config_entries.async_unload(entry.entry_id)
+        state_after_unload = hass.states.get(status_id(hass, entry))
+        blocked.release.set()
+        await refresh
+        await hass.async_block_till_done()
+    # The plan finished after unloading began: kept out of the data and the states.
+    assert coordinator.data is shown
+    assert coordinator.data["status"] == "learning"
+    assert hass.states.get(status_id(hass, entry)) == state_after_unload
+
+
+async def test_reload_during_optimization_shows_only_the_new_coordinator(hass, entry, inputs):
+    old = await setup(hass, entry)
+    trained(old)  # only in memory: the reloaded coordinator is not ready
+    blocked = BlockedOptimizer()
+    with patch.object(coordinator_module, "optimize", blocked):
+        refresh = hass.async_create_task(old.async_refresh())
+        assert await blocked.wait_started()
+        assert await hass.config_entries.async_reload(entry.entry_id)
+        await hass.async_block_till_done()
+        new = hass.data[DOMAIN][entry.entry_id]
+        blocked.release.set()
+        await refresh
+        await hass.async_block_till_done()
+    assert new is not old and old.closing
+    assert old.data["status"] != "ready"
+    assert hass.states.get(status_id(hass, entry)).state == new.data["status"] == "learning"
+
+
+async def test_removal_during_a_save_leaves_no_model_behind(hass, entry, inputs, freezer):
+    coordinator = await setup(hass, entry)
+    started, release = asyncio.Event(), asyncio.Event()
+    write = coordinator._store.async_save
+
+    async def slow_save(data):
+        started.set()
+        await release.wait()
+        await write(data)
+    coordinator._store.async_save = slow_save
+    future = dt_util.utcnow() + timedelta(minutes=5, seconds=1)
+    freezer.move_to(future)
+    async_fire_time_changed(hass, future)  # a learning poll, which saves
+    await asyncio.wait_for(started.wait(), 5)
+    removal = hass.async_create_task(hass.config_entries.async_remove(entry.entry_id))
+    for _ in range(5):
+        await asyncio.sleep(0)
+    assert not removal.done()  # deletion waits for the save in progress
+    release.set()
+    assert await removal
+    await hass.async_block_till_done()
+    assert await Store(hass, 1, f"{DOMAIN}.{entry.entry_id}").async_load() is None
+
+
+async def test_repeated_setup_and_unload_leave_nothing_running(hass, entry, inputs, freezer):
+    entry.add_to_hass(hass)
+    coordinators = []
+    for _ in range(3):
+        assert await hass.config_entries.async_setup(entry.entry_id)
+        await hass.async_block_till_done()
+        coordinators.append(hass.data[DOMAIN][entry.entry_id])
+        assert await hass.config_entries.async_unload(entry.entry_id)
+        await hass.async_block_till_done()
+    assert entry.entry_id not in hass.data[DOMAIN]
+    assert all(c.closing and c._unsub_refresh is None and not c._listeners for c in coordinators)
+    counts = [c.learners["sensor.room"].count for c in coordinators]
+    hass.states.async_set("switch.boiler", "on")
+    future = dt_util.utcnow() + timedelta(minutes=10)
+    freezer.move_to(future)
+    async_fire_time_changed(hass, future)
+    await hass.async_block_till_done()
+    assert not any(c._relay_events for c in coordinators)
+    assert [c.learners["sensor.room"].count for c in coordinators] == counts

tests/ha/test_config_flow.py

@@ -21,6 +21,55 @@ async def setup_entry(hass, entry):
     return hass.data[DOMAIN][entry.entry_id]
 
 
+async def open_settings(hass, entry):
+    """Configure opens a menu; Settings is the first choice."""
+    result = await hass.config_entries.options.async_init(entry.entry_id)
+    assert result["type"] is FlowResultType.MENU
+    assert result["menu_options"] == ["settings", "reset_model"]
+    return await hass.config_entries.options.async_configure(result["flow_id"], {"next_step_id": "settings"})
+
+
+async def open_reset(hass, entry):
+    result = await hass.config_entries.options.async_init(entry.entry_id)
+    return await hass.config_entries.options.async_configure(result["flow_id"], {"next_step_id": "reset_model"})
+
+
+async def test_reset_learned_model_keeps_configuration_and_observations(hass, entry, inputs, hass_storage):
+    coordinator = await setup_entry(hass, entry)
+    model = coordinator.learners["sensor.room"]
+    model.count, model.on_count, model.off_count = 250, 125, 125
+    payload = coordinator._payload("sensor.outside")
+    payload["retired_learners"] = {"sensor.gone": {**model.as_dict()}}
+    payload["ventilation"] = {"events": [{"zone": "sensor.room", "opened": "2026-10-27T10:00:00+00:00"}]}
+    payload["heat_sources"] = {"samples": {"ups": [[1.0, 200.0]]}}
+    await coordinator._store.async_save(payload)
+    assert await hass.config_entries.async_reload(entry.entry_id)
+    await hass.async_block_till_done()
+    assert hass.data[DOMAIN][entry.entry_id].data["status"] == "ready"
+    before = (dict(entry.data), dict(entry.options), dict(entry.subentries))
+    # Not confirmed: nothing happens.
+    result = await open_reset(hass, entry)
+    assert (result["type"], result["step_id"]) == (FlowResultType.FORM, "reset_model")
+    result = await hass.config_entries.options.async_configure(result["flow_id"], {"confirm_reset": False})
+    assert (result["type"], result["reason"]) == (FlowResultType.ABORT, "model_not_reset")
+    assert hass.data[DOMAIN][entry.entry_id].learners["sensor.room"].count == 250
+    # Confirmed: a fresh model, the same configuration.
+    result = await open_reset(hass, entry)
+    result = await hass.config_entries.options.async_configure(result["flow_id"], {"confirm_reset": True})
+    assert (result["type"], result["reason"]) == (FlowResultType.ABORT, "model_reset")
+    await hass.async_block_till_done()
+    fresh = hass.data[DOMAIN][entry.entry_id]
+    assert fresh is not coordinator and fresh.learners["sensor.room"].count == 0
+    assert fresh.data["status"] == "learning" and not fresh._retired
+    assert (dict(entry.data), dict(entry.options), dict(entry.subentries)) == before
+    stored = hass_storage[f"{DOMAIN}.{entry.entry_id}"]["data"]
+    assert not {"learners", "retired_learners", "evaluation", "operational_evaluation", "fingerprints"} & set(stored)
+    assert stored["ventilation"]["events"][0]["opened"] == "2026-10-27T10:00:00+00:00"
+    assert stored["heat_sources"] == {"samples": {"ups": [[1.0, 200.0]]}}
+    assert stored["model_changes"][-1]["reasons"] == ["learned model reset by the user"]
+    assert fresh.data["model_changes"][-1]["recalibrated"] == ["all"]
+
+
 async def test_config_flow_creates_one_zone_per_sensor(hass, inputs):
     hass.states.async_set("sensor.bedroom", "19", {"unit_of_measurement": "°C", "friendly_name": "Bedroom"})
     result = await hass.config_entries.flow.async_init(DOMAIN, context={"source": SOURCE_USER})
@@ -51,7 +100,7 @@ async def test_config_flow_creates_one_zone_per_sensor(hass, inputs):
 
 async def test_options_flow_keeps_settings_without_rooms(hass, entry, inputs):
     await setup_entry(hass, entry)
-    result = await hass.config_entries.options.async_init(entry.entry_id)
+    result = await open_settings(hass, entry)
     assert "room_entities" not in {str(key) for key in result["data_schema"].schema}
     main = result["data_schema"]({**MAIN, "room_max_age_minutes": 45, "room_stale_use_last_value": True})
     result = await hass.config_entries.options.async_configure(result["flow_id"], main)
@@ -65,14 +114,14 @@ async def test_options_flow_keeps_settings_without_rooms(hass, entry, inputs):
     assert coordinator.config["room_max_age_minutes"] == 45
     assert coordinator.config["room_stale_use_last_value"] is True
     assert coordinator.rooms == ["sensor.room"]  # zones are untouched by Configure
-    result = await hass.config_entries.options.async_init(entry.entry_id)
+    result = await open_settings(hass, entry)
     assert result["data_schema"](MAIN)["room_stale_use_last_value"] is True
     hass.config_entries.options.async_abort(result["flow_id"])
 
 
 async def test_weather_source_may_not_be_a_zone_sensor(hass, entry, inputs):
     await setup_entry(hass, entry)
-    result = await hass.config_entries.options.async_init(entry.entry_id)
+    result = await open_settings(hass, entry)
     result = await hass.config_entries.options.async_configure(result["flow_id"], result["data_schema"](MAIN))
     result = await hass.config_entries.options.async_configure(
         result["flow_id"], result["data_schema"]({"outside_temperature_sources": ["sensor.room"]}))
@@ -172,7 +221,7 @@ async def test_options_flow_clears_optional_weather_sources(hass, inputs):
         "weights": {"sensor.room": 1.0}})
     await setup_entry(hass, entry)
     flow = hass.config_entries.options
-    result = await flow.async_init(entry.entry_id)
+    result = await open_settings(hass, entry)
     result = await flow.async_configure(result["flow_id"], result["data_schema"](MAIN))
     markers = {str(key): key for key in result["data_schema"].schema}
     assert markers["outside_wind_sources"].description == {"suggested_value": ["sensor.wind"]}
@@ -245,7 +294,7 @@ async def test_window_subentry_flow_and_ventilation_options(hass, entry, inputs)
                                 ("sensor.gust", "9", "m/s")):
         hass.states.async_set(sensor, value, {"unit_of_measurement": unit})
     options = hass.config_entries.options
-    result = await options.async_init(entry.entry_id)
+    result = await open_settings(hass, entry)
     result = await options.async_configure(result["flow_id"], result["data_schema"](
         {**MAIN, "ventilation_gust": True, "ventilation_cross": True}))
     result = await options.async_configure(result["flow_id"], result["data_schema"]({

tests/ha/test_lifecycle.py

@@ -302,6 +302,7 @@ async def test_forecast_error_sensors_score_scheduled_polls(hass, entry, inputs,
     assert state.attributes["mae"] == accuracy["mae"] > 0
     assert state.attributes["quality"] == "insufficient"
     assert state.attributes["samples"] == 1
+    assert (state.attributes["incomplete"], state.attributes["skipped"]) == (0, 0)
     assert state.attributes["zones"]["room"]["samples"] == 1  # by zone name
     assert state.attributes["unit_of_measurement"] == "°C"
     assert state.attributes["friendly_name"] == "Heating MPC Model Error 15 min"

tests/test_coordinator.py

@@ -24,6 +24,9 @@ class CoordinatorBase:
     async def async_config_entry_first_refresh(self):
         self.data = await self._async_update_data()
 
+    async def async_shutdown(self):
+        self._shutdown_requested = True
+
 
 class Store:
     def __init__(self, *args):
@@ -72,6 +75,7 @@ class BoundaryTests(unittest.IsolatedAsyncioTestCase):
             config=types.SimpleNamespace(units=types.SimpleNamespace(temperature_unit='°C')),
             services=types.SimpleNamespace(async_call=Mock(side_effect=AssertionError('No service calls permitted'))),
             track_state=Mock(return_value=Mock(name='unsub_relay')),
+            async_create_task=lambda coro, name=None, eager_start=True: asyncio.ensure_future(coro),
             registry=types.SimpleNamespace(entries=[], async_remove=Mock()),
             config_entries=types.SimpleNamespace(
                 async_forward_entry_setups=AsyncMock(), async_unload_platforms=AsyncMock(return_value=True),
@@ -1215,6 +1219,35 @@ class BoundaryTests(unittest.IsolatedAsyncioTestCase):
         base = self.c.learners['office']
         base.count, base.on_count, base.off_count = 200, 100, 100
 
+    async def test_reload_with_an_unavailable_window_keeps_its_last_known_state_uncertain(self):
+        self.ready_office()
+        await self.sample()
+        self.fresh('sensor.room', 'sensor.outside', 'sensor.ups_power')
+        window = self.states['binary_sensor.office_window']
+        window.state, window.last_changed = 'on', self.now - timedelta(seconds=100)
+        self.c._store.async_save.reset_mock()
+        await self.sample()
+        self.c._store.async_save.assert_awaited()   # a window change is saved even without learning
+        payload = self.c._store.async_save.await_args.args[0]
+        # Reload: a new coordinator restores the saved state; the sensor is now unavailable.
+        window.state = 'unavailable'
+        self.fresh('sensor.room', 'sensor.outside', 'sensor.ups_power')
+        successor = coordinator.HeatingMPCCoordinator(self.hass, self.entry)
+        successor._store.async_load.return_value = payload
+        await successor.async_restore_model()
+        base = successor.learners['office']
+        base.count, base.on_count, base.off_count = 200, 100, 100
+        successor._update_counts()
+        self.c = successor
+        data = await self.sample()
+        info = data['windows']['Window']
+        self.assertEqual((info['state'], info['last_known_state'], info['forecast_assumption']),
+                         ('unknown', 'open', 'last_known_open'))
+        self.assertGreater(info['forecast_open_minutes'], 0)
+        self.assertEqual(data['zone_details']['office']['uncertain_windows'], ['Window'])
+        self.assertTrue(data['ventilation']['Office']['affected'])
+        self.assertEqual(data['sample_result'], 'first_sample')  # nothing learned across the reload
+
     async def change_during_optimization(self, change):
         async def worker(job):
             output = job()
@@ -1257,7 +1290,11 @@ class BoundaryTests(unittest.IsolatedAsyncioTestCase):
         with patch.object(coordinator, 'datetime') as clock:
             clock.now.return_value = self.now
             older = asyncio.ensure_future(self.c._async_update_data())
-            await asyncio.sleep(0)
+            for _ in range(50):  # until the older run is inside the optimizer (it may save first)
+                if getattr(self, '_first_started', False):
+                    break
+                await asyncio.sleep(0)
+            self.assertTrue(self._first_started)
             clock.now.return_value = self.now + timedelta(seconds=60)
             newer = await self.c._async_update_data()
             self.c.data = newer   # what DataUpdateCoordinator publishes
@@ -1292,8 +1329,24 @@ class BoundaryTests(unittest.IsolatedAsyncioTestCase):
                 # Longer horizons are not due yet.
                 self.assertEqual(data['operational_forecast'][30]['samples'], 0)
                 stored = self.c._payload('sensor.outside')['operational_evaluation']
-                self.assertEqual(sum(sum(v.get('15', {}).values()) for v in stored['verdicts'].values()),
+                counts = {}
-                                 0 if follows else 1)
+                for by_horizon in stored['verdicts'].values():
+                    for name, count in by_horizon.get('15', {}).items():
+                        counts[name] = counts.get(name, 0) + count
+                self.assertEqual(counts, {'matched': 1} if follows else {'counterfactual': 1})
+
+    async def test_a_forecast_made_with_an_unknown_window_is_never_scored(self):
+        self.ready_office()
+        self.set_state('sensor.room', '23.5', unit_of_measurement='°C')  # plan: off, as the relay
+        self.states['binary_sensor.office_window'].state = 'unavailable'
+        first = await self.sample()
+        self.assertEqual(first['status'], 'ready')
+        self.assertEqual(self.c._forecasts._pending[-1]['assumptions']['uncertain_inputs'], ['window: Window'])
+        for _ in range(3):
+            self.fresh('sensor.room', 'sensor.outside', 'sensor.ups_power')
+            data = await self.sample()
+        fifteen = data['operational_forecast'][15]
+        self.assertEqual((fifteen['samples'], fifteen['incomplete_reasons']['uncertain_inputs']), (0, 1))
 
     async def test_unloading_during_save_or_optimization_publishes_nothing_new(self):
         self.ready_office()
@@ -1308,6 +1361,83 @@ class BoundaryTests(unittest.IsolatedAsyncioTestCase):
         self.assertIs(await self.sample(), previous)
         worker.assert_not_called()
 
+    async def test_unload_marks_shutdown_before_platforms_unload(self):
+        await integration.async_setup_entry(self.hass, self.entry)
+        c = self.hass.data['heating_mpc']['entry']
+        seen = []
+        async def unload_platforms(entry, platforms):
+            seen.append(c.closing)
+            return True
+        self.hass.config_entries.async_unload_platforms.side_effect = unload_platforms
+        self.assertTrue(await integration.async_unload_entry(self.hass, self.entry))
+        self.assertEqual(seen, [True])
+        # A failed unload leaves the entry loaded, so the coordinator carries on.
+        await integration.async_setup_entry(self.hass, self.entry)
+        c = self.hass.data['heating_mpc']['entry']
+        self.hass.config_entries.async_unload_platforms.side_effect = None
+        self.hass.config_entries.async_unload_platforms.return_value = False
+        self.assertFalse(await integration.async_unload_entry(self.hass, self.entry))
+        self.assertFalse(c.closing)
+
+    async def test_nothing_is_learned_saved_or_recorded_once_unloading_began(self):
+        self.ready_office()
+        await self.sample()
+        self.c._store.async_save.reset_mock()  # the window's first reading is saved
+        shown = self.c.data = {'status': 'learning', 'proposal': None}
+        self.fresh('sensor.room', 'sensor.outside', 'sensor.ups_power')
+        self.c.async_begin_shutdown()
+        count = self.c.learners['office'].count
+        self.assertIs(await self.sample(), shown)
+        self.assertEqual(self.c.learners['office'].count, count)
+        self.c._store.async_save.assert_not_awaited()
+        self.assertFalse(await self.c._async_save('sensor.outside'))
+        # A relay callback queued before the listener was removed is ignored.
+        new = types.SimpleNamespace(state='on', last_changed=self.now)
+        self.c.async_relay_changed(types.SimpleNamespace(
+            data={'old_state': types.SimpleNamespace(state='off'), 'new_state': new}, time_fired=self.now))
+        self.assertFalse(self.c._relay_events)
+
+    async def test_failed_setup_shuts_the_coordinator_down(self):
+        self.hass.config_entries.async_forward_entry_setups.side_effect = RuntimeError('test')
+        with patch.object(coordinator.HeatingMPCCoordinator, 'async_shutdown', autospec=True) as shutdown:
+            with self.assertRaises(RuntimeError):
+                await integration.async_setup_entry(self.hass, self.entry)
+        shutdown.assert_awaited_once()
+
+    async def test_simultaneous_saves_never_overlap(self):
+        self.ready_office()
+        order = []
+        async def slow_save(payload):
+            order.append('start')
+            await asyncio.sleep(0)
+            await asyncio.sleep(0)
+            order.append('end')
+        self.c._store.async_save.side_effect = slow_save
+        results = await asyncio.gather(*(self.c._async_save('sensor.outside') for _ in range(3)))
+        self.assertEqual(results, [True, True, True])
+        self.assertEqual(order, ['start', 'end'] * 3)
+
+    async def test_a_save_waiting_for_a_deletion_does_not_recreate_the_model(self):
+        self.ready_office()
+        order, release = [], asyncio.Event()
+        store = Store()
+        async def slow_remove():
+            order.append('delete started')
+            await release.wait()
+            order.append('deleted')
+        store.async_remove.side_effect = slow_remove
+        with patch.object(integration, 'Store', return_value=store):
+            removal = asyncio.ensure_future(integration.async_remove_entry(self.hass, self.entry))
+            await asyncio.sleep(0)
+            saving = asyncio.ensure_future(self.c._async_save('sensor.outside'))
+            await asyncio.sleep(0)
+            self.assertEqual(order, ['delete started'])  # the save is waiting for the lock
+            release.set()
+            await removal
+            self.assertFalse(await saving)
+        self.assertEqual(order, ['delete started', 'deleted'])
+        self.c._store.async_save.assert_not_awaited()
+
     async def test_restore_waits_for_a_save_still_in_progress(self):
         self.ready_office()
         order = []

tests/test_data_quality.py

@@ -65,7 +65,12 @@ class FreshnessTests(unittest.TestCase):
     def test_freshness_settings_translated(self):
         for name in ('strings.json', 'translations/en.json', 'translations/ro.json'):
             data = json.loads((ROOT / name).read_text())
-            for section, step in (('config', 'user'), ('options', 'init')):
+            # Configure opens a menu: settings, or resetting the learned model.
+            options = data['options']
+            self.assertEqual(set(options['step']['init']['menu_options']), {'settings', 'reset_model'})
+            self.assertTrue(options['step']['reset_model']['data']['confirm_reset'])
+            self.assertEqual(set(options['abort']), {'model_reset', 'model_not_reset'})
+            for section, step in (('config', 'user'), ('options', 'settings')):
                 labels = data[section]['step'][step]['data']
                 for key in ('room_max_age_minutes', 'weather_max_age_minutes', 'room_stale_use_last_value'):
                     self.assertTrue(labels[key])

tests/test_evaluation.py

@@ -116,15 +116,22 @@ class EvaluationTests(unittest.TestCase):
             day: {'45': {'sensor.room': [1, 0.0, 0.1, 0.1]},
                   '15': {'sensor.a': [1, 0.0, -0.1, 0.1], 'sensor.b': [True, 0, 0, 0],
                          'sensor.c': [1, float('nan'), 0, 0], 'sensor.d': [2, 0.1, 0.2, 0.4],
-                         'sensor.e': [2, 0.1, 0.2, 0.4, 0.3], 'sensor.f': [2, 0.1, 0.2, 0.4, -1]}}}})
+                         'sensor.e': [2, 0.1, 0.2, 0.4, 0.3], 'sensor.f': [2, 0.1, 0.2, 0.4, -1]}}},
+            'excluded': {day: {'15': {'sensor.d': [1, 2, 0.3], 'sensor.x': [1, -2, 0.3], 'sensor.y': [1, 2]},
+                               '45': {'sensor.d': [1, 2, 0.3]}}}})
         # A 0.3 bucket has no base-model column; its model was the base model then.
         # Neither 0.3 nor 0.4 buckets have squared errors: they do not count for RMSE.
+        # 0.4 data has no excluded-span counts.
         self.assertEqual(restored.as_dict(), {'days': {day: {'15': {
-            'sensor.d': [2, 0.1, 0.2, 0.4, 0.2, 0.0, 0], 'sensor.e': [2, 0.1, 0.2, 0.4, 0.3, 0.0, 0]}}}})
+            'sensor.d': [2, 0.1, 0.2, 0.4, 0.2, 0.0, 0], 'sensor.e': [2, 0.1, 0.2, 0.4, 0.3, 0.0, 0]}}},
+            'excluded': {day: {'15': {'sensor.d': [1, 2, 0.3]}}}})
         self.assertIsNone(restored.summary(['sensor.d'])[15]['rmse'])
+        self.assertEqual({k: restored.summary(['sensor.d'])[15][k] for k in ('incomplete', 'skipped',
+                                                                             'incomplete_base_model_mae')},
+                         {'incomplete': 1, 'skipped': 2, 'incomplete_base_model_mae': 0.3})
         for garbage in (None, [], {'days': []}):
             restored.load(garbage)
-            self.assertEqual(restored.as_dict(), {'days': {}})
+            self.assertEqual(restored.as_dict(), {'days': {}, 'excluded': {}})
 
 
 T0 = START.timestamp()
@@ -152,7 +159,7 @@ class OperationalForecastTests(unittest.TestCase):
         for horizon, actual in ((15, 19.9), (30, 19.8), (60, 19.6), (120, 19.2)):
             with self.subTest(horizon=horizon):
                 s = stats[horizon]
-                self.assertEqual((s['samples'], s['counterfactual'], s['unverifiable']), (1, 0, 0))
+                self.assertEqual((s['samples'], s['matched'], s['counterfactual'], s['incomplete']), (1, 1, 0, 0))
                 self.assertAlmostEqual(s['mae'], 0, places=3)
                 self.assertAlmostEqual(s['persistence_mae'], 20.0 - actual, places=3)
                 self.assertEqual(s['improvement_vs_persistence'], 1.0)
@@ -172,12 +179,14 @@ class OperationalForecastTests(unittest.TestCase):
 
     def test_unknown_relay_history_or_missing_observations(self):
         stats = run_ledger(self.PLAN_OFF, lambda k: False, lambda k: 20.0, self.TRAJECTORY, unknown_relay_at=2)
-        self.assertEqual((stats[15]['samples'], stats[15]['unverifiable'], stats[60]['unverifiable']), (0, 1, 1))
+        self.assertEqual((stats[15]['samples'], stats[15]['incomplete_reasons']['relay_history'],
+                          stats[60]['incomplete']), (0, 1, 1))
         missing = run_ledger(self.PLAN_OFF, lambda k: False, lambda k: None if k == 12 else 20.0, self.TRAJECTORY)
-        self.assertEqual((missing[60]['samples'], missing[60]['missing'], missing[30]['samples']), (0, 1, 1))
+        self.assertEqual((missing[60]['samples'], missing[60]['incomplete_reasons']['reading'],
+                          missing[30]['samples']), (0, 1, 1))
         # No poll near the due time (polls 11 to 13 missing): the 60-minute point cannot be scored.
         gap = run_ledger(self.PLAN_OFF, lambda k: False, lambda k: 20.0, self.TRAJECTORY, skip=(11, 12, 13))
-        self.assertEqual((gap[60]['samples'], gap[60]['missing']), (0, 1))
+        self.assertEqual((gap[60]['samples'], gap[60]['incomplete_reasons']['no_poll']), (0, 1))
 
     def test_due_time_alignment(self):
         ledger = ev.OperationalForecastLedger()
@@ -200,9 +209,205 @@ class OperationalForecastTests(unittest.TestCase):
         self.assertEqual(restored.summary(['r'])[15]['counterfactual'], 1)
         restored.forget('r')
         self.assertEqual(restored.summary(['r'])[15]['samples'], 0)
-        for garbage in (None, [], {'days': {'x': {}}, 'verdicts': {'2026-10-27': {'15': {'bogus': 3}}}}):
+        empty = {'version': 2, 'days': {}, 'approximate': {}, 'verdicts': {}}
-            self.assertEqual(ev.OperationalForecastLedger(garbage).as_dict(), {'days': {}, 'verdicts': {}})
+        for garbage in (None, [], {'version': 2, 'days': {'x': {}}, 'verdicts': {'2026-10-27': {'15': {'bogus': 3}}}}):
+            self.assertEqual(ev.OperationalForecastLedger(garbage).as_dict(), empty)
+        # 0.4.1 statistics accepted plans within 20 % per block: not comparable, dropped.
+        old = {**stats_ledger.as_dict(), 'version': None}
+        old.pop('version')
+        self.assertEqual(ev.OperationalForecastLedger(old).as_dict(), empty)
 
 
 if __name__ == '__main__':
     unittest.main()
+
+
+BASE = (0.004, 0.12, 0.0005)
+
+
+def ventilated(observed, actual=None, wind=4.0, features=('vent_natural',), zones=('r',), internal=None,
+               extended=None, lag_uncertain_at=(), missing_end_at=()):
+    """Zones cooled by real ventilation; `observed[k]` is what the window sensor said
+    for the interval ending at poll k (None: unknown). Returns the summary at 15 min."""
+    actual = actual if actual is not None else [1.0 if o is None else o for o in observed]
+    snapshot = {'base': BASE, 'uses_extended': extended is not None, 'extended': extended,
+                'ventilation': (list(features), (0.02,) * len(features))}
+    evaluator, temps = ev.ForecastEvaluator(0.1), {zone: 20.0 for zone in zones}
+    for k, seen in enumerate(observed):
+        if k:
+            temps = {zone: t + 0.004 * (5 - t) + 0.12 * 0.5 + 0.0005
+                     + 0.02 * actual[k] * (5 - t) * (1 + (wind or 0) * ('vent_wind' in features))
+                     for zone, t in temps.items()}
+        vent = {'opening': seen, 'facing': 0.0, 'other_opening': 0.0, 'recovery': 0.0, 't_ref': None,
+                'wind': wind}
+        readings = {z: t for z, t in temps.items() if not (k in missing_end_at and z == zones[0])}
+        evaluator.observe(START + timedelta(seconds=300 * k), readings, 5.0, 0.5,
+                          0.5 if k in lag_uncertain_at else 0.0, {z: BASE for z in zones},
+                          models={z: snapshot for z in zones},
+                          inputs={z: {'internal': internal, 'ventilation': vent if z == zones[0] else
+                                      {**vent, 'opening': actual[k]}} for z in zones})
+    return evaluator.summary(list(zones))[15]
+
+
+class IncompleteInputTests(unittest.TestCase):
+    """Unknown ventilation or extended inputs never become Model Error samples."""
+
+    def test_window_unknown_for_the_whole_span_is_incomplete(self):
+        stats = ventilated([0.0, None, None, None])
+        self.assertEqual((stats['samples'], stats['mae'], stats['incomplete']), (0, None, 1))
+        # The base model needs no window input: its error is still kept, apart.
+        self.assertGreater(stats['incomplete_base_model_mae'], 0.1)
+
+    def test_window_unknown_halfway_or_recovering_during_the_span(self):
+        self.assertEqual(ventilated([0.0, 1.0, None, 1.0])['incomplete'], 1)   # unavailable halfway
+        stats = ventilated([0.0, None, 1.0, 1.0, 1.0, 1.0, 1.0])            # back after one poll
+        # Spans ending at polls 3-6: only the first contains the unknown interval.
+        self.assertEqual((stats['incomplete'], stats['samples']), (1, 3))
+        self.assertAlmostEqual(stats['mae'], 0, places=6)                   # the known spans are exact
+
+    def test_one_zone_unknown_leaves_the_other_zone_scored(self):
+        stats = ventilated([0.0, None, None, None], zones=('a', 'b'))
+        self.assertEqual((stats['rooms']['a']['incomplete'], stats['rooms']['a']['samples']), (1, 0))
+        self.assertEqual((stats['rooms']['b']['incomplete'], stats['rooms']['b']['samples']), (0, 1))
+
+    def test_missing_wind_matters_only_while_a_window_is_open(self):
+        features = ('vent_natural', 'vent_wind')
+        open_span = ventilated([0.0, 1.0, 1.0, 1.0], wind=None, features=features)
+        closed_span = ventilated([0.0, 0.0, 0.0, 0.0], wind=None, features=features)
+        self.assertEqual((open_span['incomplete'], open_span['samples']), (1, 0))
+        self.assertEqual((closed_span['incomplete'], closed_span['samples']), (0, 1))
+        self.assertEqual(ventilated([0.0, 1.0, 1.0, 1.0], features=features)['samples'], 1)
+
+    def test_unknown_electrical_heat_makes_the_extended_replay_incomplete(self):
+        extended = (['loss', 'heat', 'bias', 'internal'], BASE + (0.15,))
+        stats = ventilated([0.0] * 4, extended=extended, internal=None)
+        self.assertEqual((stats['incomplete'], stats['samples']), (1, 0))
+        self.assertIsNotNone(stats['incomplete_base_model_mae'])
+        self.assertEqual(ventilated([0.0] * 4, extended=extended, internal=0.0)['samples'], 1)
+
+    def test_a_neighbour_with_unknown_inputs_spoils_a_coupled_zone(self):
+        coupled = {'base': BASE, 'uses_extended': True, 'ventilation': None,
+                   'extended': (['loss', 'heat', 'bias', 'couple:a'], BASE + (0.01,))}
+        plain = {'base': BASE, 'uses_extended': False, 'extended': None,
+                 'ventilation': (['vent_natural'], (0.02,))}
+        origin = {'temps': {'a': 20.0, 'b': 20.0}, 'outside': 5.0, 'models': {'a': plain, 'b': coupled}}
+        path = [origin] + [{'time': START + timedelta(seconds=300 * k), 'lag': 0.5,
+                            'inputs': {'a': {'ventilation': {'opening': None}}, 'b': {}}} for k in range(4)]
+        origin['time'] = START - timedelta(seconds=300)
+        _, incomplete = ev.replay_zones(origin, path)
+        self.assertEqual(incomplete, {'a', 'b'})
+        path = [origin] + [{**poll, 'inputs': {'a': {'ventilation': {'opening': 0.0}}, 'b': {}}} for poll in path[1:]]
+        self.assertEqual(ev.replay_zones(origin, path)[1], set())
+
+    def test_skipped_spans_are_counted_and_samples_stay_correct(self):
+        stats = ventilated([0.0] * 9, lag_uncertain_at=(4,))
+        # Spans ending at 3..8: those crossing poll 4 (ending 4, 5, 6) are skipped.
+        self.assertEqual((stats['samples'], stats['skipped'], stats['incomplete']), (3, 3, 0))
+        stats = ventilated([0.0] * 5, missing_end_at=(4,), zones=('a', 'b'))
+        self.assertEqual((stats['rooms']['a']['samples'], stats['rooms']['a']['skipped']), (1, 1))
+        self.assertEqual(stats['rooms']['b']['samples'], 2)
+
+
+def ledger_with(plan, on_seconds, temps=None, uncertain=None, polls=None):
+    """One forecast at T0; `on_seconds[k-1]` is the relay's on-time in the interval ending
+    at poll k (None: unknown). Returns the ledger."""
+    ledger = ev.OperationalForecastLedger()
+    trajectory = [20.0 + 0.1 * (b + 1) for b in range(len(plan))]
+    ledger.observe(T0, {'r': 20.0}, None, None)
+    ledger.record(T0, {'r': 20.0}, {'r': trajectory}, plan, 'rev1',
+                  {'strategy': 'mpc_plan', 'uncertain_inputs': uncertain or []})
+    for k in range(1, (polls or len(on_seconds)) + 1):
+        on = on_seconds[k - 1] if k - 1 < len(on_seconds) else 0.0
+        ledger.observe(T0 + 300 * k, {'r': (temps or (lambda k: 20.0 + k / 30))(k)}, on, 300.0)
+    return ledger
+
+
+def verdict(ledger, horizon):
+    stats = ledger.summary(['r'])[horizon]
+    for name in ('matched', 'approximate', 'counterfactual'):
+        if stats[name]:
+            return name
+    return next(f'incomplete:{r}' for r, n in stats['incomplete_reasons'].items() if n)
+
+
+class PlanMatchTests(unittest.TestCase):
+    """Forecasts are compared with reality only when the relay did what their plan assumed."""
+
+    ON, OFF = 300.0, 0.0
+
+    def test_exact_match_is_scored(self):
+        ledger = ledger_with([1, 1, 0, 0], [self.ON] * 6 + [self.OFF] * 6)
+        self.assertEqual([verdict(ledger, h) for h in (15, 30, 60)], ['matched'] * 3)
+        self.assertEqual(ledger.deviation({'time': T0, 'plan': [1, 1, 0, 0]}, 60), (0.0, 0.0))
+        stats = ledger.summary(['r'])[60]
+        self.assertEqual((stats['samples'], stats['approximate_stats']['samples']), (1, 0))
+
+    def test_small_transition_jitter_still_matches(self):
+        # Planned off at 30 minutes; the relay switched off 40 s later.
+        ledger = ledger_with([1, 1, 0, 0], [self.ON] * 6 + [40.0] + [self.OFF] * 5)
+        self.assertEqual(ledger.deviation({'time': T0, 'plan': [1, 1, 0, 0]}, 60), (40.0, 0.0))
+        self.assertEqual(verdict(ledger, 60), 'matched')
+
+    def test_twelve_of_fifteen_planned_minutes_is_only_approximate(self):
+        ledger = ledger_with([1, 0], [self.ON, self.ON, 120.0, self.OFF, self.OFF, self.OFF])
+        self.assertEqual(verdict(ledger, 15), 'approximate')
+        stats = ledger.summary(['r'])[15]
+        # Not in the main figures; kept apart.
+        self.assertEqual((stats['samples'], stats['mae'], stats['approximate']), (0, None, 1))
+        self.assertEqual(stats['approximate_stats']['samples'], 1)
+
+    def test_same_on_time_at_another_time_is_counterfactual(self):
+        # 15 minutes on as planned, but in the second block instead of the first.
+        ledger = ledger_with([1, 0, 0, 0], [self.OFF] * 3 + [self.ON] * 3 + [self.OFF] * 6)
+        self.assertEqual(ledger.deviation({'time': T0, 'plan': [1, 0, 0, 0]}, 30), (1800.0, 0.0))
+        self.assertEqual([verdict(ledger, h) for h in (15, 30, 60)], ['counterfactual'] * 3)
+
+    def test_early_shutdown_and_unexpected_activation(self):
+        early = ledger_with([1, 1, 1, 1], [self.ON] * 4 + [self.OFF] * 8)   # off after 20 of 60 minutes
+        self.assertEqual([verdict(early, h) for h in (15, 30, 60)], ['matched', 'counterfactual', 'counterfactual'])
+        brief = ledger_with([0, 0, 0, 0], [self.OFF] * 4 + [240.0] + [self.OFF] * 7)  # 4 minutes on
+        self.assertEqual([verdict(brief, h) for h in (15, 30)], ['matched', 'approximate'])
+        long = ledger_with([0, 0, 0, 0], [self.OFF] * 4 + [self.ON, self.ON] + [self.OFF] * 6)
+        self.assertEqual(verdict(long, 30), 'counterfactual')
+
+    def test_several_cycles_followed_closely_match(self):
+        plan = [1, 0, 1, 0, 1, 0, 1, 0]
+        relay = []
+        for block in plan:
+            relay += [self.ON] * 3 if block else [30.0, self.OFF, self.OFF]  # each off 30 s late
+        ledger = ledger_with(plan, relay)
+        self.assertEqual(ledger.deviation({'time': T0, 'plan': plan}, 120)[0], 120.0)  # four transitions
+        self.assertEqual(verdict(ledger, 60), 'matched')
+        self.assertEqual(verdict(ledger, 120), 'matched')
+
+    def test_missing_relay_history_is_incomplete_not_matched(self):
+        ledger = ledger_with([0, 0, 0, 0], [self.OFF, None] + [self.OFF] * 10)
+        self.assertEqual([verdict(ledger, h) for h in (15, 60)], ['incomplete:relay_history'] * 2)
+        # A plan already contradicted is counterfactual whatever the gap hides.
+        ledger = ledger_with([1, 1, 1, 1], [None] + [self.OFF] * 11)
+        self.assertEqual(verdict(ledger, 30), 'counterfactual')
+
+    def test_thermostat_disagreeing_is_counterfactual_never_an_error(self):
+        ledger = ledger_with([1] * 4, [self.OFF] * 12, temps=lambda k: 20.0)
+        stats = ledger.summary(['r'])
+        self.assertEqual([stats[h]['counterfactual'] for h in (15, 30, 60)], [1, 1, 1])
+        self.assertEqual([stats[h]['samples'] for h in (15, 30, 60)], [0, 0, 0])
+
+    def test_forecasts_made_with_assumed_inputs_are_incomplete(self):
+        ledger = ledger_with([0] * 4, [self.OFF] * 12, uncertain=['window: Bedroom'])
+        self.assertEqual(verdict(ledger, 15), 'incomplete:uncertain_inputs')
+        self.assertEqual(ledger.summary(['r'])[15]['samples'], 0)
+
+    def test_counts_over_several_forecasts(self):
+        ledger = ev.OperationalForecastLedger()
+        ledger.observe(T0, {'r': 20.0}, None, None)
+        for k, plan in enumerate(([0, 0], [1, 1], [0, 0])):   # one forecast per poll, relay always off
+            ledger.record(T0 + 300 * k, {'r': 20.0}, {'r': [20.0, 20.0]}, plan, 'rev1', {})
+            ledger.observe(T0 + 300 * (k + 1), {'r': 20.0}, 0.0, 300.0)
+        for k in range(4, 12):
+            ledger.observe(T0 + 300 * k, {'r': 20.0}, 0.0, 300.0)
+        stats = ledger.summary(['r'])[15]
+        self.assertEqual((stats['matched'], stats['counterfactual'], stats['samples'], stats['incomplete']),
+                         (2, 1, 2, 0))
+        restored = ev.OperationalForecastLedger(ledger.as_dict())
+        self.assertEqual(restored.summary(['r']), ledger.summary(['r']))

tests/test_windows.py

@@ -253,3 +253,165 @@ class UnknownStateTests(unittest.TestCase):
         self.assertTrue(tracker.events[-1]['closed_confirmed'])
         inputs = tracker.zone_inputs('bed', WEATHER)
         self.assertEqual((inputs['known'], inputs['unknown_windows']), (False, ['B']))
+
+
+class StartupAndRestartTests(unittest.TestCase):
+    """Unknown at startup is not closed; saved state comes back as unknown, never live."""
+
+    WINDOWS = wm.build_windows([(window('w'), None)], ['bed'])
+
+    def setUp(self):
+        self.t = 2_000_000.0
+
+    def poll(self, tracker, state, changed_ago=100_000, temp=20.0):
+        tracker.update(self.t, {'w': (state, self.t - changed_ago)}, {'bed': temp}, WEATHER)
+        result = (tracker.zone_inputs('bed', WEATHER), tracker.forecast('bed', 6, 15, WEATHER, self.t),
+                  tracker.assumption(self.WINDOWS[0], 15, self.t))
+        self.t += 300
+        return result
+
+    def saved_with(self, *states):
+        tracker = wm.VentilationTracker(self.WINDOWS)
+        for state in states:
+            self.poll(tracker, state, changed_ago=200 if state == 'on' else 100_000, temp=21.0)
+        return tracker, tracker.as_dict()
+
+    def test_unavailable_from_startup_is_unobserved_not_closed(self):
+        tracker = wm.VentilationTracker(self.WINDOWS)
+        for _ in range(3):
+            inputs, forecast, assumed = self.poll(tracker, 'unavailable')
+        self.assertEqual(assumed, {'basis': 'unobserved', 'open_minutes': 0.0, 'uncertain': True})
+        self.assertEqual((inputs['known'], inputs['affected'], inputs['unknown_windows']), (False, True, ['W']))
+        self.assertEqual(forecast[0]['opening'], 0.0)  # no ventilation assumed, but flagged uncertain
+        self.assertEqual((len(tracker.events), tracker._open_event), (0, {}))
+
+    def test_first_reading_open_is_an_unconfirmed_opening(self):
+        tracker = wm.VentilationTracker(self.WINDOWS)
+        self.poll(tracker, 'on', changed_ago=30)       # we never saw it closed
+        event = tracker._open_event['bed']
+        self.assertEqual((event['opened_confirmed'], event['temperature_before']), (False, None))
+        self.poll(tracker, 'on')
+        self.poll(tracker, 'off', changed_ago=60)
+        event = tracker.events[-1]
+        self.assertTrue(event['closed_confirmed'])
+        # Opened at some unknown time before it was first seen: a minimum duration only.
+        self.assertEqual((event['duration_minutes'], event['duration_max_minutes']), (None, None))
+        self.assertEqual(event['duration_min_minutes'], 9.5)
+
+    def test_restart_with_saved_open_window_and_sensor_unavailable(self):
+        before, saved = self.saved_with('off', 'off', 'on', 'on')
+        self.assertEqual(saved['state']['windows']['w']['last_known'], True)
+        self.assertIn('bed', saved['state']['zones'])
+        self.t += 120                                   # restart takes two minutes
+        tracker = wm.VentilationTracker(self.WINDOWS, saved=saved)
+        self.assertEqual(tracker.window_state, {})      # nothing is live before the first reading
+        inputs, forecast, assumed = self.poll(tracker, 'unavailable')
+        state = tracker.window_state['w']
+        self.assertEqual((state['open'], state['last_known_open']), (None, True))
+        self.assertEqual(state['unknown_since'], saved['state']['windows']['w']['seen'])
+        # Assumed open only until 15 minutes after it was last seen (7 minutes ago).
+        self.assertEqual((assumed['basis'], assumed['open_minutes'], assumed['uncertain']),
+                         ('last_known_open', 8.0, True))
+        self.assertEqual([s['opening'] for s in forecast[:3]], [1.0, 1.0, 0.0])
+        self.assertEqual((inputs['known'], inputs['affected']), (False, True))
+        # The event from before the restart continues; the downtime counts as unknown.
+        self.assertEqual(len(tracker.events), 0)
+        event = tracker._open_event['bed']
+        self.assertEqual((event['opened'], event['opened_confirmed']), (before._open_event['bed']['opened'], True))
+        self.assertEqual(event['sensor_unknown_minutes'], 12.0)  # 7 minutes down + one unknown poll
+        # Not assumed open forever: past the bound the forecast assumes it closed.
+        for _ in range(3):
+            _, forecast, assumed = self.poll(tracker, 'unavailable')
+        self.assertEqual((assumed['open_minutes'], forecast[0]['opening']), (0.0, 0.0))
+        self.assertGreater(forecast[0]['recovery'], 0.0)
+        # Back as closed: closed at an unknown time, so an unconfirmed close and recovery.
+        inputs, _, _ = self.poll(tracker, 'off', changed_ago=30)
+        event = tracker.events[-1]
+        self.assertEqual((event['closed_confirmed'], event['duration_minutes']), (False, None))
+        self.assertEqual(event['duration_min_minutes'], 8.3)     # opened 3.3 min before a poll, seen open 5 min later
+        self.assertEqual((inputs['known'], inputs['affected']), (False, True))
+
+    def test_restart_with_saved_closed_window(self):
+        _, saved = self.saved_with('off', 'off')
+        tracker = wm.VentilationTracker(self.WINDOWS, saved=saved)
+        inputs, forecast, assumed = self.poll(tracker, 'unavailable')
+        self.assertEqual((assumed['basis'], assumed['uncertain'], forecast[0]['opening']),
+                         ('last_known_closed', True, 0.0))
+        self.assertEqual((inputs['known'], len(tracker.events)), (False, 0))
+        # It reports open: opened during the unknown period, so not a confirmed opening.
+        self.poll(tracker, 'on', changed_ago=60)
+        self.assertFalse(tracker._open_event['bed']['opened_confirmed'])
+
+    def test_restart_with_the_same_state_again_is_a_known_state(self):
+        _, saved = self.saved_with('off', 'off')
+        tracker = wm.VentilationTracker(self.WINDOWS, saved=saved)
+        self.poll(tracker, 'off')                    # first interval after restart: unknown
+        inputs, _, assumed = self.poll(tracker, 'off')
+        self.assertEqual((inputs['known'], inputs['affected'], assumed['basis']), (True, False, 'observed'))
+
+    def test_saved_state_of_another_sensor_or_polarity_is_ignored(self):
+        _, saved = self.saved_with('off', 'off', 'on', 'on')
+        for changed in ({'sensor': 'binary_sensor.other'}, {'inverted': True}):
+            with self.subTest(changed=changed):
+                windows = wm.build_windows([(window('w', **changed), None)], ['bed'])
+                tracker = wm.VentilationTracker(windows, saved=saved)
+                self.assertEqual((tracker._memory, tracker._open_event), ({}, {}))
+        corrupt = {'events': [], 'state': {'windows': {'w': {'sensor': 'binary_sensor.w', 'last_known': 'yes',
+                                                              'seen': 'x'}}, 'zones': {'bed': 5}}}
+        self.assertEqual(wm.VentilationTracker(self.WINDOWS, saved=corrupt)._memory, {})
+
+    def test_mixed_windows_each_get_their_own_assumption(self):
+        windows = wm.build_windows([(window('a'), None), (window('b', opening_factor=0.5), None),
+                                    (window('c', zone='living'), None)], ['bed', 'living'])
+        tracker = wm.VentilationTracker(windows)
+        t = 0.0
+        for a, b, c in (('off', 'off', 'off'), ('off', 'on', 'off'), ('on', 'on', 'unavailable'),
+                        ('on', 'unavailable', 'unavailable'), ('on', 'unavailable', 'unavailable'),
+                        ('on', 'unavailable', 'unavailable'), ('on', 'unavailable', 'unavailable')):
+            t += 300
+            tracker.update(t, {'a': (a, t - 200), 'b': (b, t - 200), 'c': (c, t - 200)},
+                           {'bed': 20.0, 'living': 21.0}, WEATHER)
+        # b was last seen open 20 minutes ago: no longer assumed open; a is observed open.
+        self.assertEqual(tracker.assumption(windows[1], 15, t)['open_minutes'], 0.0)
+        self.assertEqual(tracker.forecast('bed', 4, 15, WEATHER, t)[0]['opening'], 1.0)
+        self.assertEqual(tracker.assumption(windows[2], 15, t)['basis'], 'last_known_closed')
+        # An unknown window in one zone does not stop another zone with known windows.
+        tracker2 = wm.VentilationTracker(windows)
+        for t in (300.0, 600.0):
+            tracker2.update(t, {'a': ('off', 0), 'b': ('off', 0), 'c': ('unavailable', 0)},
+                            {'bed': 20.0, 'living': 21.0}, WEATHER)
+        self.assertEqual((tracker2.zone_inputs('bed', WEATHER)['known'],
+                          tracker2.zone_inputs('living', WEATHER)['known']), (True, False))
+
+    def test_changes_worth_saving_are_flagged(self):
+        tracker = wm.VentilationTracker(self.WINDOWS)
+        self.poll(tracker, 'off')
+        self.assertTrue(tracker.changed)                # first reading
+        tracker.changed = False
+        self.poll(tracker, 'off')
+        self.assertFalse(tracker.changed)               # nothing new
+        self.poll(tracker, 'unavailable')
+        self.assertTrue(tracker.changed)
+
+
+class StrictInputTests(unittest.TestCase):
+    """Weather inputs of an open window are required, never assumed, outside forecasts."""
+
+    def test_unknown_direction_or_other_zone_while_open(self):
+        windows = wm.build_windows([(window('a'), None), (window('c', zone='living'), None)], ['bed', 'living'])
+        tracker = wm.VentilationTracker(windows)
+        calm = {**WEATHER, 'wind_direction': None}
+        for t, a, c in ((300.0, 'off', 'off'), (600.0, 'on', 'unavailable'), (900.0, 'on', 'unavailable')):
+            tracker.update(t, {'a': (a, 450.0), 'c': (c, 450.0)}, {'bed': 20.0, 'living': 21.0}, calm)
+        bed = tracker.zone_inputs('bed', calm)
+        self.assertEqual((bed['opening'], bed['facing'], bed['other_opening']), (1.0, None, None))
+        features = ['vent_natural', 'vent_wind', 'vent_wind_dir', 'vent_cross']
+        self.assertFalse(wm.ventilation_known(features, bed))
+        self.assertTrue(wm.ventilation_known(['vent_natural', 'vent_wind'], bed))
+        self.assertIsNone(wm.ventilation_vector(features, 20.0, 2.0, bed))           # learning refuses
+        self.assertIsNotNone(wm.ventilation_vector_for_prediction(features, 20.0, 2.0, bed))
+        # Closed, every opening term is 0 whatever the weather: nothing is missing.
+        closed = {**bed, 'opening': 0.0, 'facing': 0.0, 'wind': None, 'other_opening': None}
+        self.assertEqual(wm.ventilation_vector(features, 20.0, 2.0, closed), [0.0, 0.0, 0.0, 0.0])
+        self.assertTrue(wm.ventilation_known(features, closed))
+        self.assertTrue(wm.ventilation_known(features, None))                        # no windows