  Occupant behavior (OB) and in particular window openings need to be
considered in building performance simulation (BPS), in order to realistically
model the indoor climate and energy consumption for heating ventilation and air
conditioning (HVAC). However, the proposed OB window opening models are often
biased towards the over-represented class where windows remained closed. In
addition, they require tuning for each occupant which can not be efficiently
scaled to the increased number of occupants. This paper presents a window
opening model for commercial buildings using deep learning methods. The model
is trained using data from occupants from an office building in Germany. In
total the model is evaluated using almost 20 mio. data points from 3
independent buildings, located in Aachen, Frankfurt and Philadelphia.
Eventually, the results of 3100 core hours of model development are summarized,
which makes this study the largest of its kind in window states modeling.
Additionally, the practical potential of the proposed model was tested by
incorporating it in the Modelica-based thermal building simulation. The
resulting evaluation accuracy and F1 scores on the office buildings ranged
between 86-89 % and 0.53-0.65 respectively. The performance dropped around 15 %
points in case of sparse input data, while the F1 score remained high.
