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>
buzeraandClaude Opus 5.5
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