Towards a Real-time Occupancy Detection Approach for Smart Buildings

Context-awareness has been considered as a crucial fact for developing context-driven control approaches in which sensing, and actuation tasks are performed according to the contextual changes. This could be done by including the occupants’ presence, number, actions and behaviours in up-to-date context taking into account the complex interlinked elements, situations, processes, and their dynamics. Many recent studies have shown that occupants’ information is a major leading source of uncertainty when developing occupancy-driven control approaches for energy efficient buildings. Comprehensive and real-time fine-grained occupancy information has to be, therefore, integrated in order to improve the performance of these control approaches. The work presented in this paper is towards the development of a holistic platform that combines recent IoT and Big data technologies for real-time occupancy detection. We focus mainly on occupants’ presence by comparing static and dynamic machine learning techniques. Experiments have been conducted and results are presented to assess the usefulness of the platform and the effectiveness of real-time machine learning strategies for data streams processing.