Non-intrusive Indoor Occupancy Detection Methods Based on Machine Learning Techniques

The Heating, Ventilation and Air-Conditioning (HVAC) systems in public buildings typically operate on fixed schedules without considering changes in actual indoor occupancy. Significant energy savings can be achieved by adaptively adjusting HVAC operating conditions to meet indoor cooling demands. Obtaining indoor occupancy through conventional devices, such as video cameras and personal bracelets, may impose negative impacts on human privacy. To address such challenge, this study proposes a non-intrusive method to accurately predict indoor occupancy using environmental data and machine learning techniques. Two-month experiment has been designed to obtain high-frequency data on environmental conditions and indoor occupancy of a conference room. A variety of environmental data, including temperature, relative humidity, CO₂ concentration, light intensity and noise level, have been collected and used as modeling inputs. Four state-of-the-art machine learning techniques, together with over-sampling and under-sampling techniques, have been used for indoor occupancy detection and occupant number prediction. The model performance has been carefully analyzed to investigate the potential of the non-intrusive method proposed. The research results validate the usefulness of environmental data in predicting indoor occupancy. The research outcomes are helpful for devising occupancy-centric measures for building energy conservations.