Window open detection is better called “temperature drop detection” (TDD)
Temperature drops from tilted windows are definitely harder to detect than fully opened windows. Seasonal transition times with out-side temperature >10°C but active heating are also harder to detect than lower outside temperatures.
Detection values have been defined as relative gradient in Kelvin per unit time and:
1,5K / 10 Minutes
Die Fensteröffnungserkennung wird besser als „Temperaturabfallerkennung“ bezeichnet (TDD).
Temperaturabfälle durch gekippte Fenster sind definitiv schwerer zu erkennen als vollständig geöffnete Fenster. Jahreszeitliche Übergangszeiten mit Außentemperatur >10°C aber aktiver Heizung sind ebenfalls schwerer zu erkennen als niedrigere Außentemperaturen.
Detektionswerte wurden als relativer Gradient in Kelvin pro Zeiteinheit definiert und:
1,5K / 10 Minuten
Measurement results product (in this case MLR003):
The window-open condition seems to be the event with the steepest gradient for any of the relevant sensors (103, 104, 105).
Turning off the radiator (100 -> 0 %) seems to be second.
Having a sensor outside the housing would be nice (less mass, faster reaction to quick changes), but a sensor inside the housing still sees a difference between window-open and other events.
The fastest part of the event is the first 10 … 15 minutes. When we allow this much time for detection, we are most certain that this is a window-open event.