Smart building energy and comfort management based on sensor activity recognition and prediction

hanks to Building Energy and Comfort Managements (BECM) systems, it is possible monitor and control buildings with the aim to ease appliance management and at the same time ensuring efficient use of them from the energetic point of view. To develop such kind of systems, it is necessary to monitor users’ habits, learning their preferences and predicting their sequences of performed activities and appliance usage during the day. To this aim, in this paper a system capable of controlling home appliances according to user preferences and trying to reduce energy consumption is proposed. The main objective of the system is to learn users’ daily behaviour and to be able to predict their future activities basing on statistical data about the activities they usually perform. The system can then execute a scheduling algorithm of the appliances based on the expected energy consumption and user annoyance related with shifting the appliance starting time from their preferred one. Experimental results demonstrate that thanks to the scheduling algorithm energy cost can be reduced of 50.43% and 49.2% depending on different tariffs, just by shifting the use of the appliance to time periods of non-peak hours. Scheduling based on probability evaluation of preferred time of usage of the appliances allows to still obtain evident energy savings even considering the errors on predicted activities.