heating-mpc-ha/CHANGELOG.md

Changelog

Engineering findings and test evidence behind each change are in AUDIT.md.

Unreleased — stability and correctness

Fixes four correctness problems found in 0.4.0 and separates model accuracy from forecast accuracy. No new product features; the version stays 0.4.0 until released. Still observation-only. Tested in the Home Assistant Core harness with HA 2025.12.0 and 2026.2.3; not yet on HAOS.

Upgrade notes

• Forecast Error 15/30/60/120 min are renamed Model Error. Unique IDs and, on existing installs, entity IDs are unchanged, so history continues. Their state now stays unknown until there are at least 5 roughly independent spans (quality is insufficient); the numbers stay in the attributes meanwhile.
• A new Heating MPC Forecast Error sensor scores forecasts as they were published. It stays unknown until the relay has followed enough MPC plans; while Generic Thermostat disagrees with the plan, most forecasts are counted as counterfactual instead of being scored.
• On the first start, the saved model gets configuration fingerprints. Models learned under 0.4.0 are assumed to match the configuration they are loaded with; later changes are checked against these fingerprints.
• Zone forecast attribute coefficients.base now gives value and confidence per coefficient instead of a bare value.
• Downgrading to 0.4.0 keeps the learned models but drops the Model Error statistics (0.4.0 rejects their new format).

Fixed

• Unknown window states no longer pass as closed. An unavailable window sensor used to end an open-window event, start recovery and let the forecast assume a closed window. Now the last known state is kept: a window last seen open is treated as possibly open (no base learning, forecast assumes open), an event closed while the sensor was unknown is marked unconfirmed with a bounded duration, and recovery is learned only after a confirmed close.
• Learned parameters are no longer reused after incompatible changes. Each learned component has a fingerprint of the configuration it was learned under (see Persisted data). Changing a zone's sensors or the ratio of their weights recalibrates that zone; changing a heat source, neighbour, window, orientation or recovery time resets only the affected coefficients; comfort weight, target offset and names never invalidate anything. Every recalibration is listed in the Status attribute model_changes.
• Plans are no longer published from inputs that changed meanwhile. Before and after the plan is computed, the integration compares every input it used (zone and outdoor temperatures beyond 0.25 °C, power beyond 50 W, wind, window states, relay and thermostat) and discards the plan if one changed meaningfully (inputs_changed, with changed_inputs). An older run finishing late never overwrites a newer result, nothing is published after unload, and saving and deleting the model can no longer overlap.

Changed

• Model accuracy and forecast accuracy are reported separately. Model Error replays the model with the inputs that actually happened (relay, electrical heat, windows); Forecast Error uses only what was known when the forecast was published, and scores it only when the relay followed the forecast's plan (in the planned state for at least 12 minutes of every 15-minute block). Both report MAE, RMSE, bias, the no-change baseline, the improvement over it, sample counts, effective (roughly independent) sample counts and a quality label.
• Base-model coefficients (loss, heat, bias) report a confidence from their posterior variance, like the other layers: it reflects whether the data varied their inputs, not how many samples there are.
• The zone form's sensor weights are numbered fields with the sensors listed above them (English and Romanian), and the zone form explains the two kinds of weight.

Persisted data

• schema_version stays 2. New optional keys: fingerprint_version, fingerprints, model_changes (last 20), operational_evaluation (Forecast Error statistics, 7 days) and per-source baseline fingerprints in heat_sources. Retired zone models keep their fingerprint.
• Model Error statistics gain RMSE columns; 0.3 and 0.4.0 statistics load (their RMSE stays empty until new forecasts are scored).
• Pending published forecasts are kept in memory only; a restart drops those not yet due.

0.4.0 — 2026-10-27

Thermal zones, electrical heat sources, windows with ventilation and wind, and heat exchange between zones (Milestone 2, first part). Still observation-only: no new 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 yet on HAOS.

Upgrade notes

• Back up Home Assistant first. The config entry moves to version 3: each room sensor becomes a one-sensor zone with its comfort weight, and the sensor's entity ID stays the zone's key, so learned models, forecast entities and accuracy statistics carry over unchanged. No model reset is needed.
• Downgrading needs the backup. Older versions refuse a version 3 entry.
• Configure no longer asks for room sensors or weights; manage zones with Add zone and each zone's edit button on the integration page.
• Status attributes renamed: input_quality.rooms → input_quality.zones (each zone lists its sensors), excluded_rooms → excluded_zones, retired_room_models → retired_zone_models. The Forecast Error attribute rooms is now zones, keyed by zone name. The invalid_weights status is gone (invalid stored weights fall back to 1.0). Update templates that used these.
• New layers start with no assumed effect and need data: the electrical term needs gaming sessions (or other load changes), the ventilation terms need window openings. Watch improvement_vs_base on the Forecast Error sensors before relying on them; switch off optional terms that do not help.

Added

• Zones (config subentries): name, several sensors with sensor weights, comfort weight, target offset and adjacent zones. Sensor and comfort weights never change learned thermal coefficients.
• Heat sources: active power (W/kW; VA and % rejected), zone, heat fraction, automatic 24-hour baseline or manual value, "included in" another source (no double counting), thermal response time, enable/disable.
• Windows: contact sensor (optional inverted polarity; unknown stays unknown), zone, facing direction (default 345°), opening factor, enable/disable; a bounded log of ventilation events.
• Wind direction and wind gust weather sources with priorities and fallback; knots and ft/s accepted.
• Model options: electrical-heat forecast half-life, assumed open-window time, recovery time constant, and wind / gust / cross-ventilation terms.
• Per-zone diagnostics on each zone's forecast entity: current temperature, +15/+30/+60/+120-minute forecast, contributions in °C/h, coefficients with confidence; Status lists heat sources, windows, ventilation and recent events.
• Forecast Error sensors: base_model_mae and improvement_vs_base.
• Poll outcomes sensors_changed and ventilation, with English and Romanian names.

Changed

• Each zone's model is layered: the unchanged base model (fallback and comparison), an extended layer with electrical heat and neighbours, and a separate ventilation layer. Zones with an open, unknown or recovering window are kept out of base and extended learning.
• Forecasts simulate all zones together, with electrical heat fading to baseline and open windows closing after the configured time; the plan search uses an exact compiled step and stays fast at 180 minutes.
• Accuracy scoring replays all zones with the actual relay, electrical heat and window history, and scores the base model on the same spans.

Persisted data

• Model payload schema_version stays 2 with optional new keys: zone_models, ventilation_models, heat_sources (baseline samples) and ventilation (events). Forecast statistics gain a base-model column; 0.3 statistics load.

Development

• New pure modules: learning.py (named-feature RLS), zones.py, heat_sources.py, windows.py, zone_model.py; docs/MODEL.md documents the model.
• Synthetic-apartment tests for gaming heat, ventilation with wind and recovery, and coupled rooms, each checking recovered coefficients and out-of-sample improvement over the base model.

0.3.0 — 2026-10-26

First part of Milestone 1: the integration now measures how accurate its thermal model is. Still observation-only; no new options. Upgrading from an earlier version: also read the notes of each version in between.

Added

• Four sensors, Heating MPC Forecast Error 15/30/60/120 min: the thermal model's mean absolute error over the last 7 days, scored out of sample against what actually happened, using the relay's real ON/OFF history. Attributes show the bias, the error of a no-change forecast (persistence_mae), skill and per-room details. See README, "Forecast accuracy".
• Heating MPC Learning Progress (% towards forecasts being used) and Heating MPC Last Sample (what the last poll did for learning, with English and Romanian state names), so learning activity is visible without opening attributes.
• Room forecast entities: forecast_15min (temperature every 15 minutes over the horizon) and plan_15min (the proposed ON/OFF plan), labelled counterfactual.
• Status: rejected_samples per room in models, sample_results_since_start counting how each poll was handled, and sample_result: waiting_for_inputs when the relay, thermostat, outdoor temperature or every room is missing.

Changed

• The model is saved after each poll's forecasts are scored, with the forecast statistics (about 8 KB for five rooms, at most 18 KB).

Upgrade notes

• Six new entities appear (four forecast-error sensors, learning progress, last sample). The error sensors stay unknown until forecasts can be scored: 15 minutes after start for the 15-minute sensor, 2 hours for the 120-minute one. Early numbers include the learning phase and are pessimistic.
• Rollback to 0.2.x is safe: older versions ignore the new saved keys, and the six new sensors become unavailable (remove them in the entity settings if you roll back permanently).

Development

• Bug-finding lint (ruff: pyflakes, bugbear) in the checks and CI.
• Tests for restoring a model saved by 0.2.1 and for 0.2.1 being able to read today's saved model.
• .DS_Store files are ignored and rejected by the package check.

0.2.3 — 2026-10-26

A stale or failed room sensor no longer pauses the whole integration. Still observation-only. Upgrading from 0.2.1 (0.2.2 was not deployed): follow the 0.2.2 upgrade notes below as well.

Changed

• A room sensor that is stale, unavailable, missing or invalid is now left out instead of pausing everything: the weighted temperature, learning and forecasts continue with the other rooms, whose comfort weights are re-normalized. Learning and forecasts pause only if no room is usable, or if the relay, thermostat or outdoor temperature is missing. Readiness considers only the rooms used.

Added

• Checkbox Keep using the last value of a room sensor that stopped reporting (setup and Configure, default off), for sensors that report only when the value changes. A stale sensor's last value is then still used, for up to 24 hours without a report. Unavailable or invalid sensors are always left out.
• Status attributes excluded_rooms and weight_coverage (share of the total comfort weight covered by the rooms used), and a per-room used flag in input_quality.
• Integration icon (a radiator with a forecast curve) in Settings → Devices & services, with a dark-theme variant. Needs Home Assistant 2026.3 or newer; older versions show the generic placeholder.

Upgrade notes

• The new checkbox appears in Configure; existing installations start with it off. If a room is often listed in excluded_rooms while its temperature is steady, either raise that room's report-age limit or turn the checkbox on.
• Rollback to 0.2.2 or 0.2.1 is safe; older versions ignore the new setting.

0.2.2 — 2026-10-26 (not deployed)

Reliability release (Milestone 0). Still observation-only: the integration never calls the relay, the thermostat or any other service. No new options or UI text; entity IDs and settings carry over. Tested in a Home Assistant Core test harness with HA 2025.12.0 and 2026.2.3; not yet tested on HAOS or with real devices.

Upgrade notes

• Back up Home Assistant, replace custom_components/heating_mpc, restart Core.
• Saved models load, but coefficients learned by 0.2.1 were fitted with a broken radiator-lag estimate (see Fixed). Either let them re-converge over a few days of heating, or reset the model: disable the integration, delete /config/.storage/heating_mpc.<entry_id>, restart Core, enable it again. Deleting the file while the integration runs does not work; it saves the model again. Deleting and re-adding the integration also resets it, but loses the settings.
• After each HA restart, learning pauses for about 30 minutes (sample_result: lag_uncertain) because the radiator state is unknown. Forecasts continue.
• After a day of running, compare each sensor's max_age_seconds_24h in Status with the freshness limits in Configure (see README, "Choosing freshness limits").
• Rollback: restore the previous folder (or the backup) and restart. 0.2.1 reads models saved by 0.2.2 and ignores the new keys.

Fixed

• The radiator-lag estimate jumped to fully hot or fully cold at every relay change. In simulation this learned the heating gain about a third too low, and forecasts made just after a boiler cycle started from the wrong state.
• The learner's covariance grew without limit during long OFF periods (for example a summer), which could destabilize learning when heating resumed and eventually make a restart discard the saved model.
• Optional weather sources could not be removed in Configure.
• A finished forecast was discarded, making proposed heat unavailable for five minutes, when only a diagnostic weather sensor or the thermostat's current temperature changed during the calculation.
• Config entries from 0.1 were not migrated.
• Forecast entities of removed rooms stayed in the entity registry.
• Malformed or physically implausible saved models could crash setup or be used.
• ON/OFF readiness counts included rejected samples and other rooms' history.
• Polls 4–7 minutes apart, or containing a relay change, were learned as normal 5-minute samples.
• Thermostat targets were read as Celsius on Fahrenheit installations.
• Invalid room weights or optimizer settings could cause crashes.
• 30/45-minute horizons reported their end point as a 60-minute forecast.

Added

• Room and weather report-age limits in setup and Configure (5–1440 minutes, defaults 30 and 120). Stale outdoor sources fall back to the next one; a stale room pauses learning and forecasts until it reports again.
• A read-only listener on the relay, so all relay changes between polls are timestamped and integrated.
• Models of rooms removed in Configure are kept (up to 24) and restored if the room is added back.
• Status attributes: input_quality (per-source state and report age, including max_age_seconds_24h), heat_lag, heat_lag_uncertainty, sample_result, retired_room_models, and per-room accepted ON/OFF counts in models.
• Statuses invalid_weights and inputs_changed.

Changed

• Readiness now also requires 30 accepted ON and 30 accepted OFF samples per room.
• Learning skips intervals with a relay change, an outdoor-source switch or an uncertain lag estimate, and polls not 270–330 seconds apart.
• input_quality and weather_comparison are no longer written to the Recorder; they change every poll. Other attributes are still recorded.

Persisted data

• Model payload schema_version 2 (0.2.1 wrote no version). Optional keys: per-room on_count/off_count and retired_learners. Older readers ignore them.

Development

• Home Assistant Core test harness for HA 2025.12.0 and 2026.2.3, a Gitea Actions matrix over both, package and translation validation, and a static test that fails if actuator service calls are added.

0.2.1

• Corrects the manifest classification from helper to service. Existing config entries move from Helpers to Settings → Devices & services → Integrations after replacing the files and restarting Home Assistant Core.
• Deleting the config entry removes its entities and the learned model (.storage/heating_mpc.<entry_id>). The component folder stays until removed manually.
• MPC behavior otherwise unchanged; read-only / shadow-only.