buzeraandClaude Opus 5.5
d2bd398a0f
During a first test the Status entity only said "learning" for the first half hour (the radiator-lag wait after a restart), and the attributes explaining why were hard to find. Add two sensors: - Learning Progress (%): for the least trained room, the mean of its three readiness requirements (200 samples, 30 ON, 30 OFF), each capped at 100 %; 100 % means ready. Attributes show the counts. - Last Sample: an enum of the last poll's learning outcome, with English and Romanian state names (e.g. "Waiting for radiators to settle"). Its history shows when learning happens. Polls blocked by missing shared inputs now report waiting_for_inputs. Readiness thresholds move to const.py. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
412 lines
22 KiB
Python
412 lines
22 KiB
Python
"""Read existing HA entities, learn thermal response, produce shadow MPC decisions."""
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from __future__ import annotations
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import logging
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from collections import deque
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from datetime import datetime, timedelta, timezone
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from functools import partial
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from math import exp, isfinite
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from typing import Any
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from homeassistant.core import Event, HomeAssistant, callback
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from homeassistant.config_entries import ConfigEntry
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from homeassistant.helpers.storage import Store
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from homeassistant.helpers.update_coordinator import DataUpdateCoordinator
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from .const import (
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CONF_ROOM_MAX_AGE, CONF_ROOM_STALE_KEEP, CONF_WEATHER_MAX_AGE, ROOM_STALE_KEEP_MAX_MINUTES,
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DOMAIN, CONF_CLIMATE, CONF_HEATER, CONF_ROOMS, CONF_WEIGHTS, WEATHER_SOURCES,
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CONF_HORIZON, CONF_MIN_ON, CONF_MIN_OFF, CONF_SWITCH_COST, CONF_HEAT_COST,
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CONF_BAND, DEFAULTS, READY_OFF_SAMPLES, READY_ON_SAMPLES, READY_SAMPLES,
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)
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from .data_quality import ReportAgeHistory, freshness
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from .evaluation import ForecastEvaluator
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from .physics import (
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LAG_LEARN_MAX_UNCERTAINTY, LAG_TAU_SECONDS, Learner, advance_lag,
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lag_from_time_in_state, optimize, relay_segments,
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)
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from .weather_sources import choose_weather_sources, sources_for, normalize
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_LOGGER = logging.getLogger(__name__)
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# Relay changes kept between polls; more than this in five minutes is chatter.
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MAX_RELAY_EVENTS = 256
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# Models of rooms removed in Options, kept in case a room is added back.
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MAX_RETIRED_ROOMS = 24
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class HeatingMPCCoordinator(DataUpdateCoordinator[dict[str, Any]]):
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"""The existing generic thermostat stays the only actuator owner."""
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def __init__(self, hass: HomeAssistant, entry: ConfigEntry) -> None:
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super().__init__(hass, _LOGGER, config_entry=entry, name=DOMAIN,
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update_interval=timedelta(minutes=5))
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self.entry = entry
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self.config = {**DEFAULTS, **entry.data, **entry.options}
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self.rooms: list[str] = self.config[CONF_ROOMS]
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self.learners: dict[str, Learner] = {room: Learner() for room in self.rooms}
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self._retired: dict[str, dict[str, Any]] = {}
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self._report_ages = ReportAgeHistory()
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self._evaluator = ForecastEvaluator(LAG_LEARN_MAX_UNCERTAINTY)
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# How each poll was handled since start, to explain slow learning.
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self._sample_results: dict[str, int] = {}
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self._store = Store(hass, 1, f"{DOMAIN}.{entry.entry_id}")
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self._previous: dict[str, Any] | None = None
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# Radiator-lag proxy at the last poll with a valid relay state. Tracked
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# independently of room sensors so lag survives room outages and transitions.
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self._lag: dict[str, Any] | None = None
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# (time, new state) of relay changes since the last poll, from a read-only listener.
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self._relay_events: deque[tuple[datetime, str]] = deque(maxlen=MAX_RELAY_EVENTS)
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self._relay_events_dropped = False
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self._on_samples = 0
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self._off_samples = 0
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async def async_restore_model(self) -> None:
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stored = await self._store.async_load() or {}
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if not isinstance(stored, dict) or stored.get("schema_version", 1) not in (1, 2):
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_LOGGER.warning("Ignoring unsupported Heating MPC model data")
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return
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# Replacing the actual heating system invalidates heating coefficients.
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# Switching among weather providers does not discard months of learning.
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if stored.get("heater_entity", self.config[CONF_HEATER]) != self.config[CONF_HEATER]:
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_LOGGER.warning("Heating MPC heater changed; starting a fresh thermal model")
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return
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self._evaluator.load(stored.get("evaluation"))
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# Models carry room identity, so changing the room list is safe. Models of
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# rooms no longer configured are retained, most recently removed last.
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for key in ("retired_learners", "learners"): # current learners take precedence
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saved = stored.get(key)
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if not isinstance(saved, dict):
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continue
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for room, data in saved.items():
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if not isinstance(room, str):
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continue
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learner = Learner.from_dict(data)
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if room in self.learners:
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self.learners[room] = learner
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elif learner.count:
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self._retired.pop(room, None)
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self._retired[room] = learner.as_dict()
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while len(self._retired) > MAX_RETIRED_ROOMS:
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self._retired.pop(next(iter(self._retired)))
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self._update_counts()
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def _update_counts(self) -> None:
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self._on_samples = min(m.on_count for m in self.learners.values())
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self._off_samples = min(m.off_count for m in self.learners.values())
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def _sensor_quality(self, entity_id: str, now: datetime, limit: float) -> dict:
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state = self.hass.states.get(entity_id)
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if state is None:
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return {"status": "missing", "age_seconds": None}
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if state.state in ("unknown", "unavailable"):
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return {"status": state.state, "age_seconds": None}
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# last_updated is a conservative compatibility fallback for older HA.
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quality = freshness(getattr(state, "last_reported", getattr(state, "last_updated", None)),
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now, limit * 60)
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if quality["status"] in ("valid", "stale"):
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quality["max_age_seconds_24h"] = round(
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self._report_ages.observe(entity_id, now, quality["age_seconds"]), 1)
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return quality
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@callback
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def async_relay_changed(self, event: Event) -> None:
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"""Record relay state changes as they happen; read-only state listener."""
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old, new = event.data.get("old_state"), event.data.get("new_state")
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if new is None:
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self.record_relay_state(event.time_fired, "removed")
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elif old is None or old.state != new.state:
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self.record_relay_state(new.last_changed, new.state)
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def record_relay_state(self, when: datetime, state: str) -> None:
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if len(self._relay_events) == self._relay_events.maxlen:
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self._relay_events_dropped = True
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self._relay_events.append((when, state))
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def _track_lag(self, now: datetime, heater_on: bool, last_changed: datetime) -> bool:
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"""Advance the lag proxy to `now`; return True if the relay held one state throughout.
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Relay changes since the last poll are integrated at their timestamps. Anything
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ambiguous (missed changes, an outage, clock jumps) restarts the estimate from
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the time spent in the current state, with an explicit error bound.
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"""
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tracker = self._lag
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steady = False
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lag = uncertainty = None
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if tracker is not None and now > tracker["time"]:
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start = tracker["time"]
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changes = [(when, state) for when, state in self._relay_events if start < when <= now]
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# The current state is authoritative; its change event may still be queued,
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# and without a listener it is the only change visible.
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if last_changed > start and (not changes or changes[-1][0] < last_changed):
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changes.append((last_changed, "on" if heater_on else "off"))
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segments = None if self._relay_events_dropped else relay_segments(
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tracker["heater_on"], start.timestamp(),
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[(when.timestamp(), state) for when, state in changes], now.timestamp())
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if segments is not None and segments[-1][0] == heater_on:
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lag = tracker["lag"]
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for command, seconds in segments:
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lag = advance_lag(lag, command, seconds)
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# The lag update is a contraction, so an initial error decays the same way.
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uncertainty = tracker["uncertainty"] * exp(
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-(now - start).total_seconds() / LAG_TAU_SECONDS)
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steady = len(segments) == 1
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if lag is None:
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lag, uncertainty = lag_from_time_in_state(
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heater_on, (now - last_changed).total_seconds())
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self._lag = {"time": now, "heater_on": heater_on, "lag": lag, "uncertainty": uncertainty}
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self._relay_events = deque((e for e in self._relay_events if e[0] > now),
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maxlen=MAX_RELAY_EVENTS)
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self._relay_events_dropped = False
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return steady
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def _count_result(self, status: dict[str, Any], result: str) -> None:
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"""Record how this poll was handled for learning, and the learning progress."""
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status["sample_result"] = result
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self._sample_results[result] = self._sample_results.get(result, 0) + 1
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status["sample_results_since_start"] = dict(self._sample_results)
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# Least trained room: mean of its three readiness requirements (100 = ready).
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status["learning_progress"] = round(100 * min(
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(min(1.0, m.count / READY_SAMPLES) + min(1.0, m.on_count / READY_ON_SAMPLES)
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+ min(1.0, m.off_count / READY_OFF_SAMPLES)) / 3
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for m in self.learners.values()), 1)
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def _prediction_inputs(self) -> tuple:
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"""Values the prediction depends on, used to detect changes during calculation."""
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heater = self.config[CONF_HEATER]
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entities = sorted(set(self.rooms) | set(sources_for(self.config, "temperature")) |
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{self.config[CONF_CLIMATE], heater})
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values = []
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for entity_id in entities:
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state = self.hass.states.get(entity_id)
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if state is None:
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values.append((entity_id, None))
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continue
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attrs = state.attributes
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values.append((entity_id, state.state, attrs.get("unit_of_measurement"),
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attrs.get("temperature"), attrs.get("temperature_unit"),
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state.last_changed if entity_id == heater else None))
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return tuple(values)
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async def _async_update_data(self) -> dict[str, Any]:
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now = datetime.now(timezone.utc)
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heater = self.hass.states.get(self.config[CONF_HEATER])
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climate = self.hass.states.get(self.config[CONF_CLIMATE])
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# Choose each weather variable independently from ordered sensor sources.
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# Sensor priorities are editable in Options without touching integration code.
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weather_entity_ids = {
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entity_id for metric in WEATHER_SOURCES
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for entity_id in sources_for(self.config, metric)
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}
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snapshot = {entity: self.hass.states.get(entity)
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for entity in set(self.rooms) | weather_entity_ids}
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inputs_before = self._prediction_inputs()
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room_quality = {}
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temps = {}
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keep_stale = self.config.get(CONF_ROOM_STALE_KEEP) is True
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for room in self.rooms:
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quality = self._sensor_quality(room, now, self.config[CONF_ROOM_MAX_AGE])
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value = None
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# Optionally trust sensors that report only on change, but never a
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# sensor silent for a day: it is most likely dead.
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if quality["status"] == "valid" or (
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keep_stale and quality["status"] == "stale"
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and quality["age_seconds"] <= ROOM_STALE_KEEP_MAX_MINUTES * 60):
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state = snapshot[room]
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value = normalize("temperature", state.state, state.attributes.get("unit_of_measurement"))
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if value is None or not 0 <= value <= 45:
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quality["status"] = "invalid_value_or_unit"
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value = None
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quality["used"] = value is not None
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room_quality[room] = quality
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temps[room] = value
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weather_states = {}
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weather_quality = {}
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for entity_id in weather_entity_ids:
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quality = self._sensor_quality(entity_id, now, self.config[CONF_WEATHER_MAX_AGE])
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weather_quality[entity_id] = quality
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if quality["status"] == "valid":
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state = snapshot[entity_id]
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weather_states[entity_id] = (state.state, state.attributes.get("unit_of_measurement"))
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weather, active_sources, weather_comparisons = choose_weather_sources(
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self.config, weather_states
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)
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metric_quality = {}
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for metric in WEATHER_SOURCES:
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metric_quality[metric] = {}
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for entity_id in sources_for(self.config, metric):
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quality = dict(weather_quality[entity_id])
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if quality["status"] == "valid" and entity_id not in weather_comparisons[metric]:
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quality["status"] = "invalid_value_or_unit"
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metric_quality[metric][entity_id] = quality
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outside = weather["temperature"]
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status: dict[str, Any] = {
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"input_quality": {"rooms": room_quality, "weather": metric_quality},
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"status": "waiting_for_sensors", "proposal": None, "prediction_60m": {},
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"temperatures": {r: t for r, t in temps.items() if t is not None},
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"on_samples": self._on_samples, "off_samples": self._off_samples,
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"samples": min(learner.count for learner in self.learners.values()),
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"mode": climate.state if climate else "unavailable",
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"weather": {k: v for k, v in weather.items() if v is not None},
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"weather_active_sources": {k: v for k, v in active_sources.items() if v},
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"weather_comparison": {k: v for k, v in weather_comparisons.items() if v},
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"weather_model_inputs": ["temperature"],
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"retired_room_models": sorted(self._retired),
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"excluded_rooms": [room for room in self.rooms if temps[room] is None],
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}
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heater_valid = heater is not None and heater.state in ("on", "off")
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heater_on = heater_valid and heater.state == "on"
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steady = False
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if heater_valid:
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steady = self._track_lag(now, heater_on, heater.last_changed)
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status["heat_lag"] = round(self._lag["lag"], 3)
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status["heat_lag_uncertainty"] = round(self._lag["uncertainty"], 3)
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else:
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self._lag = None
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self._relay_events.clear()
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self._relay_events_dropped = False
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# Rooms without a usable reading are left out and the others carry on;
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# only the shared inputs, or losing every room, pause the integration.
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vals: dict[str, float] = {room: float(t) for room, t in temps.items() if t is not None}
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if not heater_valid or climate is None or outside is None or not vals:
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self._previous = None
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self._evaluator.reset_window()
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status["forecast_accuracy"] = self._evaluator.summary(self.rooms)
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self._count_result(status, "waiting_for_inputs")
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return status
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# A room learns only across a poll pair where it was usable at both ends.
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pairs = [room for room in vals if self._previous and room in self._previous["temps"]]
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learned = False
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if not pairs:
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status["sample_result"] = "first_sample"
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else:
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dt = (now - self._previous["time"]).total_seconds()
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# Do not fit across a relay change, a provider/fallback switch or an
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# uncertain lag estimate (restart, relay outage, several changes).
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if not 270 <= dt <= 330:
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status["sample_result"] = "interval_out_of_range"
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elif not steady or heater_on != self._previous["heater_on"]:
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status["sample_result"] = "relay_changed"
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elif self._previous["outside_source"] != active_sources["temperature"]:
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status["sample_result"] = "outside_source_changed"
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elif self._lag["uncertainty"] > LAG_LEARN_MAX_UNCERTAINTY:
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status["sample_result"] = "lag_uncertain"
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else:
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accepted = [
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self.learners[room].learn(
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self._previous["temps"][room], vals[room],
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self._previous["outside"], self._lag["lag"], dt, heater_on)
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for room in pairs
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]
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status["sample_result"] = "learned" if all(accepted) else "partly_rejected"
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self._update_counts()
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learned = True
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self._count_result(status, status["sample_result"])
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self._previous = {
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"time": now, "temps": vals, "outside": outside, "heater_on": heater_on,
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"outside_source": active_sources["temperature"],
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}
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# Parameters after this poll's learning: forecasts from here use only data so far.
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self._evaluator.observe(now, vals, outside, self._lag["lag"], self._lag["uncertainty"],
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{room: tuple(self.learners[room].theta) for room in vals})
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status["forecast_accuracy"] = self._evaluator.summary(self.rooms)
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if learned:
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# Persist at each sample so restart does not erase hours of training.
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await self._store.async_save({
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"schema_version": 2,
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"learners": {room: learner.as_dict() for room, learner in self.learners.items()},
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# Optional keys; older 0.2.x readers ignore them and keep working.
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"retired_learners": self._retired,
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"evaluation": self._evaluator.as_dict(),
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"on_samples": self._on_samples, "off_samples": self._off_samples,
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"heater_entity": self.config[CONF_HEATER],
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"outside_source": active_sources["temperature"],
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})
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weights = self.config.get(CONF_WEIGHTS, {})
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try:
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weights = {r: float(weights.get(r, 1.0)) for r in self.rooms}
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if any(not isfinite(w) or w <= 0 for w in weights.values()) or not isfinite(sum(weights.values())):
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raise ValueError
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except (AttributeError, TypeError, ValueError, OverflowError):
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status["status"] = "invalid_weights"
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return status
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# Left-out rooms take their weight with them; the rest are re-normalized.
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used_weights = {room: weights[room] for room in vals}
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weighted_temp = sum(used_weights[r] * vals[r] for r in vals) / sum(used_weights.values())
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status.update({
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"weighted_temperature": round(weighted_temp, 3),
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"weight_coverage": round(sum(used_weights.values()) / sum(weights.values()), 3),
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"outside_temperature": outside,
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"reference_heat": heater_on,
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"on_samples": self._on_samples,
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"off_samples": self._off_samples,
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"samples": min(learner.count for learner in self.learners.values()),
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"models": {
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room: {"loss": round(model.theta[0], 6),
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"heat": round(model.theta[1], 6),
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"bias": round(model.theta[2], 6),
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"samples": model.count,
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"on_samples": model.on_count, "off_samples": model.off_count,
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"rejected_samples": model.rejected_count,
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"mae_5m": round(model.mae, 4) if model.mae is not None else None}
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for room, model in self.learners.items()
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},
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})
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if climate.state != "heat":
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status["status"] = "paused"
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return status
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try:
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target = normalize("temperature", climate.attributes["temperature"],
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climate.attributes.get("temperature_unit",
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self.hass.config.units.temperature_unit))
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if target is None or not 5 <= target <= 35:
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raise ValueError
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except (TypeError, KeyError, ValueError):
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status["status"] = "waiting_for_target"
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return status
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status["target"] = target
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# Do not act on a cold model: every room used needs ~17 hours of samples
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# plus both relay conditions.
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used_models = [self.learners[room] for room in vals]
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if (min(m.count for m in used_models) < READY_SAMPLES
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or min(m.on_count for m in used_models) < READY_ON_SAMPLES
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or min(m.off_count for m in used_models) < READY_OFF_SAMPLES):
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status["status"] = "learning"
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return status
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try:
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# 180-minute exhaustive search can take several seconds on RPi5;
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# never block Home Assistant's event loop with CPU work.
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job = partial(
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optimize,
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vals, outside, self._lag["lag"], target, used_weights,
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{room: self.learners[room] for room in vals},
|
|
heater_on, (now - heater.last_changed).total_seconds(),
|
|
horizon_minutes=int(self.config[CONF_HORIZON]),
|
|
minimum_on_minutes=int(self.config[CONF_MIN_ON]),
|
|
minimum_off_minutes=int(self.config[CONF_MIN_OFF]),
|
|
switching_cost=float(self.config[CONF_SWITCH_COST]),
|
|
heating_cost=float(self.config[CONF_HEAT_COST]),
|
|
comfort_band=float(self.config[CONF_BAND]),
|
|
)
|
|
# Discard a result if a value used by the prediction changed while
|
|
# saving or while the worker calculated the hypothetical plan. Optional
|
|
# diagnostic weather sensors are not prediction inputs.
|
|
output = await self.hass.async_add_executor_job(job)
|
|
if self._prediction_inputs() != inputs_before:
|
|
status["status"] = "inputs_changed"
|
|
return status
|
|
except (ValueError, RuntimeError, OverflowError) as err:
|
|
_LOGGER.warning("MPC prediction failed: %s", err)
|
|
status["status"] = "prediction_error"
|
|
return status
|
|
status.update({
|
|
"status": "ready", "proposal": output["proposal"],
|
|
"prediction_60m": output["predicted_60m"],
|
|
"prediction_end": output["predicted_end"],
|
|
"plan_15min": output["plan_15min"], "objective": output["cost"],
|
|
"prediction_trajectory": output["trajectory_15min"],
|
|
"model_quality_note": "5-minute in-sample MAE; not a validated 60-minute forecast error",
|
|
})
|
|
return status
|