MODES: Multi-sensor Occupancy Data-driven Estimation System for Smart Buildings

Buildings account for more than 40% of the energy US primary energy consumption. Of all the building services, heating, ventilation, and air-conditioning (HVAC) account for almost 50% of that energy use. Despite all the resources used, many users are not satisfied with the comfort conditions in buildings. The main problems for this lack of balance between energy use and quality of comfort are the lack of occupancy information, real user comfort feedback, and easily built zone thermodynamic models available to the Building Management Systems (BMS). In our work, we focus on occupancy sensing. While occupancy sensing is very important and there are multiple different sensing technologies used to address this issue, a precise and reliable measurement of occupancy remains elusive. In this paper, we propose MODES, a Multi-sensor Occupancy Data-driven Estimation System for Smart Buildings. Leveraging on two different state-of-the-art sensing techniques available in the literature (vibration and thermal sensors), both being capable of counting the number of occupants in any particular zone. The two occupancy estimations are then fused using a data-driven optimization process for sensor fusion to create an improved estimate. This newly updated estimate is further used together with a data-driven occupancy model as input of a particle filter to provide an even more accurate estimate. We tested the system in a commercial building under realistic conditions using real experimental occupancy data traces with users doing their daily routines. We showed that MODES can improve occupancy estimation by 40% from vibration sensors, 19% from thermal sensors, and 30% from state-of-the-art sensor fusion schemes. Moreover, we show that this is possible with minimum data training requirements, needing 7 days of training data to train the fusion system. We also run several EnergyPlus simulations using an occupancy-driven HVAC controller under different occupancy errors to show the impact that more accurate occupancy sensing schemes can have on the overall energy usage and quality of comfort and air ventilation. Our study shows that MODES can save up to 77% of energy use in a building while improving the quality of comfort by 10%. (1)