A Distributed PIR-based Approach for Estimating People Count in Office Environments

Office buildings are key energy consumers and thus require attention to achieve efficient operation. While individual office spaces are dynamically used, current building automation does not receive information on utilisation that could be used to adaptively adjust energy consumption. In this work, we propose an approach to estimate people count per office space using distributed strategically placed PIR sensors and algorithms that can process the distributed sensor information. We detail our sensing node and evaluate its performance in an office installation. A sensor model was subsequently used in a floor-wide simulation of realistic occupant behaviours to investigate two algorithms to estimate people count per office space. The occupant behaviour simulations confirmed that our estimation algorithms can accurately predict people count in different office use scenarios. The errors introduced by the PIR masking time after a detection can be partially compensated when using distributed sensor information. Our approach can be used for dynamic, occupancy-dependent lighting, climate, and appliances control of office spaces.