Files
heating-mpc-ha/custom_components/heating_mpc/sensor.py
T
buzeraandClaude Opus 5.5 ed8f9fe2e2 Model windows, ventilation, wind exposure and recovery
Opening a window adds heat exchange that the envelope model mistook for
poor insulation or a constant cooling bias.

windows.py adds windows as config subentries ("Add window"): a contact
sensor (optionally inverted; unknown/unavailable stays unknown, never
"closed"), its zone, the direction its outside faces (default 345°,
editable) and an opening factor. Each poll computes the share of the
interval each window was open (one change located by its timestamp, a
round trip is unknown), the wind exposure with proper circular angles
(meteorological "from" direction vs the window's facing) and a recovery
weight after closing that decays towards 0, pulling the air back
towards its pre-ventilation temperature. A bounded event log records
opening/closing, duration, temperatures before/lowest/at close/30 min
after, weather and other open windows.

While a zone's window is open, unknown or recovering, the base and
extended models skip it; a separate ventilation learner fits the
closed-window model's residual with natural exchange, wind speed, a
directional term bounded by the wind term (a leeward window still
ventilates), recovery, and optional gust excess and cross-ventilation
terms. Forecasts assume an open window closes after a configurable time
(default 15 min), then recovers. New poll outcome "ventilation".

Weather gains optional wind direction (degrees, 360 -> 0) and wind gust
sources with the same priorities and fallback; knots and ft/s accepted.

On a synthetic week with ventilation events every four hours and a
daily outdoor swing, the configured zone keeps loss/heat/bias close to
the truth and recovers the combined natural+wind effect within 15 %,
recovery within 15 % and direction within 30 %. The same data without
the window configured produces a -0.013 °C/5 min cooling bias and an
11 % inflated boiler effect.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
2026-10-27 02:47:40 +02:00

217 lines
8.6 KiB
Python

"""Diagnostics and predicted temperatures exposed as HA sensors."""
from __future__ import annotations
from homeassistant.components.sensor import SensorDeviceClass, SensorEntity, SensorStateClass
from homeassistant.const import PERCENTAGE, UnitOfTemperature
from homeassistant.helpers.update_coordinator import CoordinatorEntity
from .const import (
DOMAIN, READY_OFF_SAMPLES, READY_ON_SAMPLES, READY_SAMPLES, SAMPLE_RESULTS,
)
from .evaluation import HORIZONS, WINDOW_DAYS
async def async_setup_entry(hass, entry, async_add_entities) -> None:
coordinator = hass.data[DOMAIN][entry.entry_id]
entities = [MPCStatus(coordinator, entry.entry_id), MPCWeightedTemp(coordinator, entry.entry_id), MPCOutdoorTemperature(coordinator, entry.entry_id),
MPCLearningProgress(coordinator, entry.entry_id), MPCLastSample(coordinator, entry.entry_id)]
entities += [MPCForecastError(coordinator, entry.entry_id, horizon) for horizon in HORIZONS]
# Zone forecasts belong to their zone subentry, so deleting a zone removes them.
linked = {}
for zone in coordinator.zones.values():
entity = MPCRoomPrediction(coordinator, entry.entry_id, zone.key)
if zone.subentry_id:
linked.setdefault(zone.subentry_id, []).append(entity)
else:
entities.append(entity)
async_add_entities(entities)
for subentry_id, zone_entities in linked.items():
async_add_entities(zone_entities, config_subentry_id=subentry_id)
class MPCStatus(CoordinatorEntity, SensorEntity):
_attr_has_entity_name = True
_attr_name = "Heating MPC Status"
_attr_icon = "mdi:brain"
# Per-source report ages change every poll; recording them adds a new
# attribute row to the database every five minutes. Current values stay visible.
_unrecorded_attributes = frozenset({"input_quality", "weather_comparison", "heat_sources", "windows",
"ventilation", "ventilation_events"})
def __init__(self, coordinator, entry_id):
super().__init__(coordinator)
self._attr_unique_id = f"{entry_id}_status"
@property
def native_value(self):
return self.coordinator.data.get("status")
@property
def extra_state_attributes(self):
data = self.coordinator.data
# Forecast details live on their own entities.
return {key: value for key, value in data.items() if key not in (
"status", "prediction_60m", "prediction_end", "prediction_trajectory", "forecast_accuracy")}
class MPCWeightedTemp(CoordinatorEntity, SensorEntity):
_attr_has_entity_name = True
_attr_name = "Heating MPC Weighted Temperature"
_attr_device_class = SensorDeviceClass.TEMPERATURE
_attr_native_unit_of_measurement = UnitOfTemperature.CELSIUS
def __init__(self, coordinator, entry_id):
super().__init__(coordinator)
self._attr_unique_id = f"{entry_id}_weighted_temp"
@property
def native_value(self):
return self.coordinator.data.get("weighted_temperature")
class MPCRoomPrediction(CoordinatorEntity, SensorEntity):
_attr_has_entity_name = True
_attr_device_class = SensorDeviceClass.TEMPERATURE
_attr_native_unit_of_measurement = UnitOfTemperature.CELSIUS
_attr_icon = "mdi:chart-line"
def __init__(self, coordinator, entry_id, room):
super().__init__(coordinator)
self.room = room
self._attr_unique_id = f"{entry_id}_{room}_predicted_60min"
self._attr_name = f"MPC {coordinator.zones[room].name} 60min Prediction"
# Change every poll; the 60-minute value itself is recorded.
_unrecorded_attributes = frozenset({"forecast_15min", "plan_15min"})
@property
def native_value(self):
return self.coordinator.data.get("prediction_60m", {}).get(self.room)
@property
def extra_state_attributes(self):
data = self.coordinator.data
return {
"forecast_15min": data.get("prediction_trajectory", {}).get(self.room),
"plan_15min": data.get("plan_15min"),
"basis": "proposed MPC plan; counterfactual while Generic Thermostat controls the relay",
}
class MPCOutdoorTemperature(CoordinatorEntity, SensorEntity):
"""Display chosen outdoor temperature and all currently valid alternatives."""
_attr_has_entity_name = True
_attr_name = "MPC Outdoor Temperature"
_attr_device_class = SensorDeviceClass.TEMPERATURE
_attr_native_unit_of_measurement = UnitOfTemperature.CELSIUS
_attr_icon = "mdi:thermometer"
def __init__(self, coordinator, entry_id):
super().__init__(coordinator)
self._attr_unique_id = f"{entry_id}_outside_temperature"
@property
def native_value(self):
return self.coordinator.data.get("weather", {}).get("temperature")
@property
def extra_state_attributes(self):
data = self.coordinator.data
return {
"active_source": data.get("weather_active_sources", {}).get("temperature"),
"alternatives": data.get("weather_comparison", {}).get("temperature", {}),
}
class MPCForecastError(CoordinatorEntity, SensorEntity):
"""Mean absolute error of the thermal model at one horizon, replayed against reality."""
_attr_has_entity_name = True
_attr_device_class = SensorDeviceClass.TEMPERATURE_DELTA
_attr_native_unit_of_measurement = UnitOfTemperature.CELSIUS
_attr_state_class = SensorStateClass.MEASUREMENT
_attr_suggested_display_precision = 2
_attr_icon = "mdi:target"
_unrecorded_attributes = frozenset({"zones"})
def __init__(self, coordinator, entry_id, horizon):
super().__init__(coordinator)
self.horizon = horizon
self._attr_unique_id = f"{entry_id}_forecast_error_{horizon}min"
self._attr_name = f"Heating MPC Forecast Error {horizon} min"
@property
def _accuracy(self):
return self.coordinator.data.get("forecast_accuracy", {}).get(self.horizon, {})
@property
def native_value(self):
return self._accuracy.get("mae")
@property
def extra_state_attributes(self):
accuracy = self._accuracy
return {
"samples": accuracy.get("samples", 0),
"bias": accuracy.get("bias"),
"persistence_mae": accuracy.get("persistence_mae"),
"skill": accuracy.get("skill"),
# Per zone, by name.
"zones": {self.coordinator.zones[key].name if key in self.coordinator.zones else key: stats
for key, stats in accuracy.get("rooms", {}).items()},
"window_days": WINDOW_DAYS,
"unit_of_attributes": "°C",
"basis": ("thermal model replayed with the actual relay history; outdoor "
"temperature held at the start value"),
}
class MPCLearningProgress(CoordinatorEntity, SensorEntity):
"""How close the least trained room is to being used for forecasts."""
_attr_has_entity_name = True
_attr_name = "Heating MPC Learning Progress"
_attr_native_unit_of_measurement = PERCENTAGE
_attr_state_class = SensorStateClass.MEASUREMENT
_attr_suggested_display_precision = 0
_attr_icon = "mdi:school-outline"
def __init__(self, coordinator, entry_id):
super().__init__(coordinator)
self._attr_unique_id = f"{entry_id}_learning_progress"
@property
def native_value(self):
return self.coordinator.data.get("learning_progress")
@property
def extra_state_attributes(self):
data = self.coordinator.data
return {
"samples": data.get("samples"),
"on_samples": data.get("on_samples"),
"off_samples": data.get("off_samples"),
"required": {"samples": READY_SAMPLES, "on_samples": READY_ON_SAMPLES,
"off_samples": READY_OFF_SAMPLES},
}
class MPCLastSample(CoordinatorEntity, SensorEntity):
"""What the last poll did for learning; its history shows when learning happens."""
_attr_has_entity_name = True
_attr_name = "Heating MPC Last Sample"
_attr_translation_key = "last_sample" # translated state names
_attr_device_class = SensorDeviceClass.ENUM
_attr_options = list(SAMPLE_RESULTS)
_attr_icon = "mdi:clipboard-pulse-outline"
_unrecorded_attributes = frozenset({"counts_since_start"})
def __init__(self, coordinator, entry_id):
super().__init__(coordinator)
self._attr_unique_id = f"{entry_id}_last_sample"
@property
def native_value(self):
return self.coordinator.data.get("sample_result")
@property
def extra_state_attributes(self):
return {"counts_since_start": self.coordinator.data.get("sample_results_since_start", {})}