Verification of stochastic models of window opening behaviour for residential buildings

Based on the analyses of data from two distinct measurement campaigns conducted in residential indoor environments in Japan and Switzerland, we identify the specificities of occupants' behaviour with respect to their interactions with windows, including the choice of opening angles for axial openings. As a first step, each dataset is analysed to develop separate predictive models which account for the specificities of window usage in the residential context. The predictive accuracy of these models is then challenged by validation on external data: using models inferred from data obtained from one survey, actions on windows are simulated for the other survey and the predictions are compared with observations. Dynamic models developed using data from office buildings as well as previously published models are also compared using this verification procedure. In the case of the Swiss dataset, these analyses demonstrate the ability of carefully formulated behavioural models developed from office environment data to reliably predict window usage in a residential context and vice-versa. However, we observe that the same models perform less satisfactorily in the prediction of window usage in Japan. From these results it seems that such models require specific calibration in the case of buildings equipped with an air-conditioning unit as was the case for the hot and humid summer climate of Japan.