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This paper addresses the question of identifying the time-window inshort-term past from which the information regarding the future occupant'swindow opening actions and resulting window states in buildings can bepredicted. The addressed sequence duration was in the range between 30 and 240time-steps of indoor climate data, where the applied temporal discretizationwas one minute. For that purpose, a deep neural network is trained to predictthe window states, where the input sequence duration is handled as anadditional hyperparameter. Eventually, the relationship between the predictionaccuracy and the time-lag of the predicted window state in future is analyzed.The results pointed out, that the optimal predictive performance was achievedfor the case where 60 time-steps of the indoor climate data were used as input.Additionally, the results showed that very long sequences (120-240 time-steps)could be addressed efficiently, given the right hyperprameters. Hence, the useof the memory over previous hours of high-resolution indoor climate data didnot improve the predictive performance, when compared to the case where 30/60minutes indoor sequences were used. The analysis of the prediction accuracy inthe form of F1 score for the different time-lag of future window states droppedfrom 0.51 to 0.27, when shifting the prediction target from 10 to 60 minutes infuture.

Learning short-term past as predictor of human behavior in commercial buildings