Status showed accepted sample counts but not why learning was slow.
Each room's model now counts samples it rejected (outliers, implausible
inputs), persisted as an optional rejected_count key and shown as
models.<room>.rejected_samples. Status also counts every poll outcome
since start (sample_results_since_start).
Add tests that a model in the exact shape 0.2.1 wrote restores with its
coefficients, and that today's saved model still has the keys and types
0.2.1's restore code reads, so rolling back keeps the learned model.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Polling only sees the relay's current state and last change, so two or
more changes between polls (e.g. an on/off round trip) made the lag
estimate unknown and paused learning for about half an hour.
Subscribe to the relay's state changes (read-only) and integrate every
recorded change between polls. Because HA delivers these events one
loop iteration late, the current state stays authoritative and fills in
a change not yet recorded. Unknown periods (non on/off states, missed
transitions, event overflow) still restart the estimate with an
explicit error bound. Without the listener, behavior is as before.
HA skips unload callbacks after unexpected setup errors, so setup now
removes the listener itself on any failure and registers it for unload
only once setup succeeds.
Tested on HA Core 2025.12.0 and 2026.2.3.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
The lag proxy was reset to the new relay command at every relay change
and after any skipped interval, so it read 1.0 straight after ON and 0.0
straight after OFF. This disabled the lag model around every boiler
cycle: on a synthetic lagged plant cycled by a hysteresis thermostat the
learner's heat gain came out 34% low (0.079 vs 0.12) and the loss term
43% low, and the optimizer started post-transition plans from the wrong
lag.
Track the lag at every poll with a valid relay state, independently of
room sensors. Integrate a single change between polls at its
last_changed time. For ambiguous cases (round trip between polls, relay
outage, restart, clock jump) restart from time-in-state with an explicit
error half-width, and learn only once it is at most 0.1 (about 32 min
after an HA restart). The same synthetic test now learns 0.1195/0.00389
(true 0.12/0.004).
Expose heat_lag, heat_lag_uncertainty and sample_result on Status so
users can see why a poll was or was not learned. The HA test fixture
relay now has an hour-old timestamp; a new test covers a freshly
written relay state.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
With forgetting 0.999 and no heat excitation (summer, long OFF periods),
the heat-coefficient variance grew by 1/0.999 per sample without limit:
about 8.7e12 after 104 simulated OFF days. The first ON samples could
then swing coefficients to their bounds, and accumulated asymmetry
approached the absolute 1e-8 restore tolerance, past which a restart
silently reset the whole model.
Symmetrize P and bound its diagonal by the prior after every update
(row/column scaling keeps it positive definite). Restore uses a relative
round-off tolerance and applies the same bound, so wound-up v0.2.1
models load instead of being discarded. Payload schema is unchanged.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
buzeraandClaude Opus 5.5