  Energy consumed in buildings takes significant portions of the total global
energy 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 HVAC
based on simple rule-based control (RBC) technologies. The ability to design
systems that can efficiently manage HVAC can reduce energy usage and greenhouse
gas emissions, and, all in all, it can help us to mitigate climate change. This
paper 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 on
the prediction and considering the time for cooling or heating, HVAC can be
turned 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 off
earlier, and it can help us increase the efficiency of HVAC while not
decreasing comfort. We demonstrate our approach's capabilities using real-world
energy data collected from multiple rooms of a university building. We show
that LSTM's room occupancy prediction based HVAC control could save energy
usage by 50% compared to conventional RBC based control.
