Exploration of the Bayesian Network framework for modelling window control behaviour. (December 2017) - Barthelmes, Verena M.; Heo, Yeonsook; Fabi, Valentina; Corgnati, Stefano P.
Abstract: Extended literature reviews confirm that the accurate evaluation of energy-related occupant behaviour is a key factor for bridging the gap between predicted and actual energy performance of buildings. One of the key energy-related human behaviours is window control behaviour that has been modelled by different probabilistic modelling approaches. In recent years, Bayesian Networks (BNs) have become a popular representation based on graphical models for modelling stochastic processes with consideration of uncertainty in various fields, from computational biology to complex engineering problems. This study investigates the potential applicability of BNs to capture underlying complicated relationships between various influencing factors and energy-related behavioural actions of occupants in buildings: in particular, window opening/closing behaviour of occupants in residential buildings is investigated. This study addresses five key research questions related to modelling window control behaviour: (A) variable selection for identifying key drivers impacting window control behaviour, (B) correlations between key variables for structuring a statistical model, (C) target definition for finding the most suitable target variable, (D) BN model with capabilities to treat mixed data, and (E) validation of a stochastic BN model. A case study on the basis of measured data in one residential apartment located in Copenhagen, Denmark provides key findings associated with the five research questions through the modelling process of developing the BN model. Highlights: The applicability of the Bayesian Network Framework to model window control behaviour is demonstrated. A procedure for developing a BN model with full exploitation of mixed data is presented. Key variables that mostly impact window control actions are highlighted. The window opening action is more suitable as a target variable to model window control behaviour.