Document the stabilization pass

README: window unknown states, what configuration changes do to the learned
model, inputs_changed tolerances, coefficient confidence, model_changes, and
Model Error vs Forecast Error. docs/MODEL.md: configuration-change table,
confidence semantics, both validation modes. CHANGELOG gains an Unreleased
section (version unchanged); AUDIT and ROADMAP record findings, evidence and
remaining risks.

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

ROADMAP.md

@@ -85,6 +85,16 @@ Release 0.2.2 adds that event-based relay history (read-only listener), keeps
 models of rooms removed in Options, shows each sensor's longest report gap, stops
 recording volatile attributes, and tests against HA 2025.12.0 and 2026.2.3.
 
+### Stabilization after 0.4.0 — 2026-10-27
+
+See AUDIT.md. Four confirmed problems fixed, each with regression tests: unknown
+window states read as closed; learned coefficients reused after incompatible
+configuration changes (now per-component fingerprints with selective
+recalibration, logged in `model_changes`); plans published from inputs that
+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). Not yet released or 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.
@@ -92,9 +102,9 @@ recording volatile attributes, and tests against HA 2025.12.0 and 2026.2.3.
 - [ ] Expose trajectories for each room at 5–15-minute resolution for 2–3 hours, with forecast generation timestamp, horizon and input provenance. *(0.3.0: 15-minute trajectory over the horizon; generation timestamp and provenance still to add.)*
 - [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. 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.
-- [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: Forecast Error sensors; replay with parameters frozen at the forecast start and the actual relay history; `skill` against persistence. No confidence intervals yet.)*
+- [ ] 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.)*
+- [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.
 
@@ -175,8 +185,11 @@ Items 1–3 of the original backlog are done (see AUDIT.md and CHANGELOG.md 0.2.
    control, decide a minimum `weight_coverage` and the outdoor-temperature outage
    policy, which still pauses.
 3. Configure zones, the UPS heat source and the windows on HAOS; after a week of
-   heating review `improvement_vs_base`, per-zone bias and which coefficients are
-   identified. Switch off optional terms that do not help.
+   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
+   vs counterfactual). Switch off optional terms that do not help. Also try an
+   unavailable window sensor and a zone edit during HAOS acceptance.
 4. Milestone 1 (rest): a dashboard example; forecast timestamps/provenance;
    explanations for the proposed action; an actual-policy forecast that simulates
    Generic Thermostat.