Validation: the walk-forward evaluator now replays all zones together
with the full model (electrical heat, neighbour exchange, ventilation
and recovery) using the parameters frozen at the forecast start and the
electrical heat and window openings that actually happened, and scores
the base 3-parameter model on the same spans. Forecast Error sensors
add base_model_mae and improvement_vs_base (share of the base model's
error the new features remove; 0 = no gain, negative = worse). Saved
0.3 statistics load with their own error as the base column.
On synthetic apartments, 60-minute error full vs base: gaming office
0.031 vs 0.182 °C (83 % better), bedroom with ventilation 0.026 vs
0.218 °C (88 %), two coupled rooms 0.024 vs 0.037 °C (34 %; gone by
120 min, where the constant-outdoor assumption dominates). The same
ventilation week without window sensors shows no improvement, as it
should. These check the method, not real-world accuracy.
Performance: every term of a zone's 5-minute change is affine in the
zone temperatures and the radiator lag, and which layer applies depends
only on which inputs exist. compile_step_model reads the coefficients
off the real model per zone and step, so the plan search does a few
multiply-adds per step. It matches the direct model to 1e-12 and makes
a 180-minute, 5-zone search 4.5x faster (0.73 s here, close to the old
base-only search).
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
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>
Electrical equipment heats its room, and a gaming session can add
hundreds of watts, which the 3-parameter model attributed to the boiler
or the bias term.
heat_sources.py adds heat sources as config subentries ("Add heat
source"): an active-power sensor (W/kW; VA and % rejected), the zone,
the share that becomes heat, an automatic baseline (20th percentile of
24 h, so the persistent UPS load is not double counted with the bias
term) or a manual one, an optional "included in" parent (the UPS total
minus a separately measured PC, one level deep) and a first-order
thermal response time. The heat during each interval is the power held
from its start, like the radiator lag. After startup or an outage a
source's response must run two time constants before it counts as
settled.
zone_model.py layers each zone: the base model is kept unchanged as the
fallback and comparison, and an extended FeatureLearner adds "internal"
(kW above baseline after the response) and neighbour coupling for
adjacent zones. It learns only intervals where all its inputs are
known, and is used for forecasts once as trained as the base. The
optimizer accepts a multi-zone step model; forecasts let extra power
fade to baseline with a configurable half-life (default 30 min).
On a synthetic office with thermostat cycling and gaming sessions the
extended model recovers the electrical, boiler and loss coefficients
within 10 %. Status lists each source's power, effective power,
baseline, variable and responding heat; models show the extended
coefficients with confidence. Saved models gain optional zone_models
and heat_sources keys.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
buzeraandClaude Opus 5.5