Abstract
In this paper, we present the results obtained by modelling the users' behaviours in a mixed mode office building in a tropical climate, more exactly in La Réunion. Few specific research studies on comfort in tropical climates have been published, and there is little feedback on the users' behaviour in these buildings.In order to improve users' assumptions in the design phase, users' actions on ceiling fans and windows have been measured and analysed. These data have then been modelled by machine learning methods, according to hygrothermal comfort and occupancy. The F1 scores eventually obtained for predicting fan use by random forests, decision trees and Bayesian networks are 99%, 98% and 95% respectively. For windows use, the F1 scores obtained are 92%, 91% and 70%, which demonstrates the ability of the models tested to predict the users' behaviours.
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