Heating MPC 0.3.0 — 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.

This project targets Home Assistant OS on a Raspberry Pi 5, with one existing ON/OFF central-heating relay, generic_thermostat, and individual indoor temperature sensors.

Install / upgrade

1. Create a Home Assistant backup.
2. Copy the entire folder custom_components/heating_mpc into /config/custom_components/heating_mpc (replace its contents when upgrading; read the upgrade notes in CHANGELOG.md first).
3. Restart Home Assistant Core.
4. If new: Settings → Devices & services → Add integration → Heating MPC. If installed already: select Heating MPC → Configure (Options).
5. Choose the existing thermostat and physical heating switch (read-only), then individual room sensor entities.
6. Choose ordered lists of outdoor sensor entity IDs. First valid sensor is active; if missing, unavailable, nonnumeric or outside reasonable bounds, the next valid sensor is used automatically.
7. Assign a relative comfort weight (0.1–10) to each selected room.

On Home Assistant 2026.3 or newer the integration shows its own icon (a radiator with a forecast curve) in Devices & services; older versions show a generic placeholder.

No entity IDs are hard-coded. Every room and outdoor measurement can be changed later via Configure, without editing YAML or Python.

Reconfigurable weather inputs

Measurement	Required?	Used in the thermal model?
Outdoor temperature	Yes (1–10 prioritized sensors)	Yes: estimates thermal losses; constant over the 2 h forecast window
Wind speed	No	No, diagnostics/source comparison only
Cloud cover	No	No, diagnostics/source comparison only
Humidity	No	No, diagnostics/source comparison only
Feels-like temperature	No	No, diagnostics/source comparison only
Atmospheric pressure	No	No, diagnostics/source comparison only
Visibility	No	No, diagnostics/source comparison only
Rain / snow	No	No, diagnostics/source comparison only

Choose multiple sensors for a single variable, e.g. OpenWeatherMap then another provider, in priority order. Switch order in Configure to run A/B comparisons on successive days, or simply let the second sensor act as fallback. The active source and all current alternative readings are visible in the Heating MPC Status entity attributes (weather, weather_active_sources, weather_comparison). The MPC Outdoor Temperature sensor also has an active_source attribute and an alternatives map.

Measurements are normalized where possible: outdoor temperatures to °C; wind to m/s; pressure to hPa; visibility to meters. Unknown units or values outside plausibility bounds are rejected in favor of the next selected sensor. Clouds and humidity are expected in %, rain/snow in mm or mm/h as represented by their upstream sensor. A fallback source may have a different sampling delay; configurable report-age limits now reject stale inputs (see below).

Switching only the weather-provider sensors does not reset learned thermal parameters. The update is applied after the integration reloads, and it skips learning one transition when the primary temperature source changes, avoiding a jump in the training series. Nevertheless, two sensors with a persistent offset can bias predictions; compare their readings and validate the model again before relying on them. Replacing the physical heating switch discards incompatible model parameters. Existing room-specific parameters are retained if room entity IDs remain unchanged.

Model, sampling and output

• Every 5 minutes: reads HA sensor states and observed relay state; learns a per-room linear thermal model dT = a*(T_out-T_room) + b*effective_heating + c.
• Learns only from valid 5-minute transitions; uses a smoothed heating-demand proxy to approximate radiator lag. Relay changes are timestamped by a read-only state listener, so the lag estimate follows every switch between polls.
• After at least 200 transitions and samples with both heating states, runs an ON/OFF MPC search for the next 60–180 minutes (default 120) in 15-minute command blocks. Protects against rapid simulated toggling.
• Uses room comfort weights and the existing Generic Thermostat's target and heat/off state. The simulated ON/OFF demand never operates hardware.
• Persists model parameters using HA's .storage so HA restarts do not discard the trained data. Models of rooms removed in Configure are kept (up to 24) and restored if the room is added back.

Entities (entity IDs may vary):

• sensor.heating_mpc_status — learning/ready/paused status and diagnostics, including outside weather, active source, all valid alternative weather values, model coefficients, proposed 15-minute plan.
• sensor.heating_mpc_weighted_temperature — own room-weighted average, informative only.
• sensor.mpc_outdoor_temperature — selected external temp and alternatives.
• Per-room predicted temperatures after 60 minutes. Attributes forecast_15min (the temperature every 15 minutes over the horizon) and plan_15min (the proposed ON/OFF plan). These assume the MPC's plan is executed; while Generic Thermostat controls the relay they are counterfactual.
• binary_sensor.heating_mpc_proposed_heat — hypothetical heating demand (unavailable until ready).
• Heating MPC Learning Progress — how close the least trained room is to being used for forecasts (100 % = ready); attributes show the sample counts and what is required.
• Heating MPC Last Sample — what the last poll did for learning (for example Learned, Relay switched, Waiting for radiators to settle); its history shows when learning happens.
• Heating MPC Forecast Error 15/30/60/120 min — how far the thermal model's forecasts were from what actually happened (see Forecast accuracy).

Known limitations / safety

• Do not connect the proposed binary sensor directly to the heating switch or build an automation from it. A production controller would need watchdog, manual override, safety handling, and an exclusive relay owner with Generic Thermostat fallback.
• The other meteorological variables besides temperature are intentionally captured but not yet fitted into the thermal model. This allows source tests without falsely treating weak inputs as validated improvements. Later versions can test whether wind or solar proxy reduces held-out forecast error.
• All rooms share one heating actuator. The model does not command individual radiators.
• No weather forecast: assumes outdoor temperature remains constant during simulated horizon.
• No Recorder/InfluxDB training backfill. It learns only from samples collected after installation.
• The demand switch is not a burner-flame or real gas consumption measurement.
• Not yet tested on HAOS or with real devices. Automated tests run the integration in a Home Assistant Core test harness (HA 2025.12.0 and 2026.2.3) with simulated entities.
• Home Assistant Core APIs and config-flow selectors can change; take a backup before trying this experimental custom integration.

Diagnostics

Start with Learning Progress and Last Sample. For details, open the Heating MPC Status entity's attributes. In recent Home Assistant versions Developer Tools is at the bottom of Settings (or press Cmd/Ctrl+K and type "developer"); its States tab lists every attribute.

Why learning starts about 30 minutes after a restart. The model learns how much the radiators warm each room, so it needs to know how warm the radiators are. It estimates this from when the relay last switched (20-minute time constant). After a Home Assistant restart that time is unknown, so learning waits until the estimate is within 10 % (Waiting for radiators to settle), about 32 minutes. Forecasts continue meanwhile. Reloading the integration usually does not cause this wait, because the relay's last change is still known. Ready then needs 200 accepted samples (about 17 hours) per room, including 30 with the relay ON and 30 with it OFF, which can take a few days depending on how often the boiler runs.

The Heating MPC Status entity's state is one of waiting_for_sensors, learning, paused (thermostat not in heat), waiting_for_target, invalid_weights, ready, inputs_changed or prediction_error. Proposed heat is available only while it is ready. Useful attributes:

• input_quality — for each room and each weather source (grouped by metric): status (valid, missing, unknown, unavailable, stale, invalid_timestamp, future_timestamp or invalid_value_or_unit), age_seconds (time since the sensor last reported to HA) and max_age_seconds_24h (largest age seen at polls in the last 24 hours; resets on reload and can read up to one poll, 5 minutes, low). Rooms also have used: whether that room's reading was used in this poll.
• excluded_rooms and weight_coverage — rooms left out of this poll, and the share of the total comfort weight still covered by the rooms used (1.0 = all).
• heat_lag and heat_lag_uncertainty — the radiator-lag estimate (0 cold, 1 fully warm) and its error bound. Learning waits while the bound is above 0.1, for example for about 30 minutes after an HA restart.
• sample_result — why the last poll was or was not learned: first_sample, learned, partly_rejected (an outlier in some room), interval_out_of_range, relay_changed, outside_source_changed or lag_uncertain.
• models — per-room coefficients, accepted sample counts and the in-sample 5-minute error (mae_5m), which is not a forecast accuracy.
• retired_room_models — removed rooms whose learned models are kept.

input_quality and weather_comparison change every poll, so they are not written to the Recorder; the other attributes are.

Choosing freshness limits

Room and weather report-age limits are set during setup and in Configure (5–1440 minutes; defaults 30 for rooms and 120 for weather).

A room sensor that is older than its limit, unavailable, missing or invalid is left out: the weighted temperature, learning and forecasts continue with the other rooms, whose weights are re-normalized. Status shows it in excluded_rooms. Learning and forecasts pause only when no room is usable, or when the relay, thermostat or outdoor temperature is missing. A room that reports again learns from its second poll onwards, so nothing is learned across its gap.

The checkbox Keep using the last value of a room sensor that stopped reporting changes this for stale sensors: their last value is still used, for up to 24 hours without a report. Use it for sensors that report only when the value changes. Unavailable or invalid sensors are always left out, and so is any sensor silent for more than 24 hours, since it is most likely dead.

A stale outdoor source falls back to the next one in its list; optional weather metrics never block.

Within the limit (or with the checkbox on), the last reading counts as current, so a frozen value can be learned for a while. A too-tight limit (with the checkbox off) drops a room whenever its sensor is quietly between reports. Freshness uses HA's last_reported: the time a sensor last reported to HA, even with an unchanged value. It is not the age of the physical measurement, so a provider re-publishing cached data looks fresh, and sensors that report only on change can look stale in a steady room.

After a day of running, set each limit comfortably above (about 1.5–2×) the largest max_age_seconds_24h among the sensors it covers. The relay and thermostat have no age limit.

Forecast accuracy

The four Forecast Error sensors measure the thermal model against reality, continuously and out of sample. At every poll the integration records each room's reading, the outdoor temperature, the radiator-lag estimate and the model's parameters. 15, 30, 60 and 120 minutes later it replays that room from the earlier reading using the parameters it had then, the outdoor temperature held at its starting value, and the radiator heat that actually followed from the relay's real ON/OFF history. The result is compared with the current reading.

This tests the thermal model, not the MPC plan, because the plan is never executed. Each sensor's state is the mean absolute error (°C) over the last 7 days, across all rooms. Attributes, all in °C except counts:

• samples — number of scored forecasts. Consecutive forecasts overlap, so they are not independent; treat small counts as indicative only.
• bias — mean error: negative means the model predicts too cold.
• persistence_mae — error of simply assuming no change; the baseline to beat.
• skill — 1 - mae / persistence_mae: 1 is perfect, 0 is no better than assuming no change, negative is worse. Empty while the temperature did not change.
• rooms — the same numbers per room (not written to the Recorder).

Forecasts are scored only where the inputs were known: polls 4.5–5.5 minutes apart, a usable reading at the start and the end, and a well-known radiator-lag estimate throughout. A missing relay, outdoor temperature or all rooms restarts the replay window, so scores resume 15–120 minutes later. Statistics are kept with the model and survive restarts; deleting the model resets them.

What it does not cover: outdoor temperature changes during the horizon (held constant, as in the MPC), and periods when the model was still learning (included, so early numbers are pessimistic). The roadmap's working target is about 0.3–0.4 °C at 60 minutes with clearly positive skill, before any control is considered.

Release notes

See CHANGELOG.md for changes and upgrade notes per version, and AUDIT.md for audit findings and test evidence.

Developer checks and Forgejo/Gitea Actions

The HA Core test suite runs against Home Assistant 2025.12.0 (the minimum in hacs.json, requirements-test.txt) and 2026.2.3 (newest published test fixtures, requirements-test-latest.txt), both on Python 3.13. Use a dedicated development virtual environment; these test dependencies are not installed with the integration and do not change its local, standard-library model inference.

python3.13 -m venv .venv
. .venv/bin/activate
python -m pip install -r requirements-test.txt   # or requirements-test-latest.txt
sh scripts/check.sh

The check script runs dependency consistency checks, the offline tests, the HA Core tests, compilation, package metadata, translation and changelog checks, and diff whitespace checks. The 2025.12.0 requirements pin a compatible DNS library because pycares 5 breaks that release's dependency. The script also disables a transitive HA test dependency's optional metadata download. The offline tests also run without Home Assistant installed:

python -m unittest discover -s tests -v

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 and 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 source. HA Core runs locally with simulated input entities and mocked storage I/O; this does not validate HAOS, physical devices, disk failures or full process restarts.

.gitea/workflows/tests.yml runs the same checks for both HA versions on pushes and pull requests, on the dedicated bs-node Actions runner label (bs-node:docker://node:22-bookworm). Repository Actions must be enabled, and the runner must support the checkout and setup-uv actions. CI uses pinned uv 0.9.6 to create a Python 3.13 virtual environment from standalone Python builds; setup-python cannot provision this version in the Debian Bookworm job image. Action caching is disabled to avoid requiring GitHub cache services on Gitea. Dependencies, Python builds and action downloads need internet access in CI; model inference does not. Remote CI execution has not yet been verified; local validation used macOS.