Study and Analysis of Socio-behavioural Dynamics for Decision Support Systems in Smart Buildings

This thesis deals with the energy saving in smart building with focus on the impact of the user behaviour on the energy consumption. The problem of human behaviour modelling has been widely studied in the state of the art, but it is still an open problem in the field of smart building since the stochastic nature of the behaviour is difficult to be accurately represented by numerical tools. An interdisciplinary approach is proposed in order to identify the suitable user features from the psychological and social point of view and to integrate such a representation into a DSS for appliance scheduling and energy cost reduction. The proposed method has exploited location-based features of the users in order to represent their habits and needs and to compute the schedules that maximize the user acceptance toward an “energy-aware” behaviour. The obtained results point out a reduction of the peak-to-average ratio higher than 40% also considering the user constraints imposed by their presence into the building.