Release 0.3.0

First part of Milestone 1: Forecast Error sensors scoring the thermal
model against reality, forecast trajectories, Learning Progress and Last
Sample sensors, rejected-sample and poll-outcome counters, plus lint
and rollback-contract tests. README explains forecast accuracy, the
30-minute learning wait after a restart and where Developer Tools now
lives. ROADMAP marks the items now backed by tests; AUDIT records the
evaluation design and evidence. Still observation-only.

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

AUDIT.md

@@ -225,3 +225,47 @@ frozen at 7) and the bedroom resumed one poll after reporting again.
 
 **Verification.** `sh scripts/check.sh` passes for HA 2025.12.0 and 2026.2.3 (69
 offline tests, 18 HA Core tests). The offline tests also pass on Python 3.9.6.
+
+
+## 0.3.0 — forecast evaluation
+
+Scope: repository code and tests (HA 2025.12.0 and 2026.2.3 harness); no HAOS.
+
+Design choices and why:
+
+- **Score the thermal model, not the MPC plan.** Generic Thermostat controls the
+  relay, so the plan is never executed and cannot be checked. Each forecast is
+  replayed with the radiator-lag values that the relay's actual history produced,
+  which tests the model under known heat input.
+- **Out of sample.** Each replay uses the parameters stored at the forecast start;
+  later learning never influences the forecast it is scored on.
+- **Only known inputs.** Spans with irregular polls, an uncertain lag estimate, or a
+  missing relay/outdoor/room reading are not scored, and a missing shared input
+  restarts the window.
+- **Baseline.** Persistence (no change) is scored on exactly the same spans; `skill`
+  reports the share of its error the model removes.
+- **Known limits.** The outdoor temperature is held at its start value (as the MPC
+  does); consecutive forecasts overlap, so samples are not independent; the 7-day
+  window includes learning-phase forecasts.
+
+Evidence: on the synthetic lagged plant (5 simulated days, thermostat cycling,
+outdoor ±5 °C daily swing) the 60-minute error is 0.03 °C against 0.39 °C for
+persistence (skill 0.92); the 120-minute error grows to 0.09 °C from the
+constant-outdoor assumption. A model matching the plant exactly scores zero error;
+a model without heat gain shows the expected negative bias. These are checks of
+the evaluation itself, not of real-world accuracy.
+
+Also in this release: per-room rejected-sample counters and per-outcome poll
+counters (Milestone 0 "failed fitting counters"), a rollback contract test against
+the 0.2.1 reader, ruff bug-finding lint, and removal of tracked `.DS_Store` files.
+
+User feedback during testing: after a reset the Status entity only said
+`learning`, and with Developer Tools moved under Settings in recent HA versions the
+attributes were hard to find, so learning activity was invisible for the first ~30
+minutes. Two sensors now show it directly: Learning Progress (%) and Last Sample
+(an enum with translated states, including *Waiting for radiators to settle*). A
+poll with missing shared inputs now reports `waiting_for_inputs` instead of nothing.
+
+**Verification.** `sh scripts/check.sh` passes in both environments: lint, 80
+offline tests, 20 HA Core tests, compilation, package checks. The offline tests
+also pass on Python 3.9.6.