Optimal HVAC building control with occupancy prediction

Buildings account for about 41% of primary energy consumption and 75% of the electricity. Space heating, space cooling, and ventilation are the dominant end uses, accounting for 41% of all energy consumed in the buildings sector. Growing interest in sustainability has resulted in research efforts to reduce energy consumption while providing adequate comfort to users. In this work, we present a Model Predictive Control (MPC) framework for optimal HVAC control that minimizes energy consumption while staying within the comfort bounds of the occupants. The novelty of our approach lies in the use of prediction occupancy models derived from data traces and incorporating those models within the MPC framework. We use a Blended Markov Chain (BMC) occupancy prediction model in order to predict thermal load and occupancy of each zone in the building. We test our approach in simulation and compare it with occupancy schedules and control rules currently use in our university buildings. Our preliminary results show that 15.5% savings in cooling in the summer, and 9.4% savings in heating in the winter are achievable when conditioning the building using our MPC/BMC control framework.