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Energy consumed in buildings takes significant portions of the total globalenergy usage. A large amount of building energy is used for heating, cooling,ventilation, and air-conditioning (HVAC). However, compared to its importance,building energy management systems nowadays are limited in controlling HVACbased on simple rule-based control (RBC) technologies. The ability to designsystems that can efficiently manage HVAC can reduce energy usage and greenhousegas emissions, and, all in all, it can help us to mitigate climate change. Thispaper proposes predictive time-series models of occupancy patterns using LSTM.Prediction signal for future room occupancy status on the next time span (e.g.,next 30 minutes) can be directly used to operate HVAC. For example, based onthe prediction and considering the time for cooling or heating, HVAC can beturned on before the room is being used (e.g., turn on 10 minutes earlier).Also, based on the next room empty prediction timing, HVAC can be turned offearlier, and it can help us increase the efficiency of HVAC while notdecreasing comfort. We demonstrate our approach's capabilities using real-worldenergy data collected from multiple rooms of a university building. We showthat LSTM's room occupancy prediction based HVAC control could save energyusage by 50% compared to conventional RBC based control.

LSTM-based Space Occupancy Prediction towards Efficient Building Energy Management