Modeling Occupancy Behavior for Energy Efficiency and Occupants Comfort Management in Intelligent Buildings

Abstract

We applied genetic programming algorithm to learn the behavior of an occupant in single person office based on motion sensor data. The learned rules predict the presence and absence of the occupant with 80\%–83\% accuracy on testing data from 5 different offices. The rules indicate that the following variables may influence occupancy behavior: 1) the day of week, 2) the time of day, 3) the length of time the occupant spent in the previous state, 4) the length of time the occupant spent in the state prior to the previous state, 5) the length of time the occupant has been in the office since the first arrival of the day. We evaluate the rules with various statistics, which confirm some of the previous findings by other researchers. We also provide new insights about occupancy behavior of these offices that have not been reported previously.