Environmental Sensors-Based Occupancy Estimation in Buildings via IHMM-MLR

Occupancy estimation in buildings can benefit various applications such as heating, ventilation, and air-conditioning control, space monitoring, and emergency evacuation. Due to the consideration of temporal dependency in occupancy data, hidden Markov model (HMM) has been shown to be effective in occupancy estimation. However, the conventional HMM that assumes invariant temporal dependency of occupancy dynamics for different time instances is unrealistic. Moreover, the performance of the conventional HMM that utilizes mixture of Gaussian for emission probability in terms of continuous observations can be easily affected by the noise in sensory data. To address these problems, in this paper, we propose a new architecture, i.e., inhomogeneous hidden Markov model with multinomial logistic regression (IHMM-MLR), for building occupancy estimation using nonintrusive environmental sensors. Instead of using the time-invariant transition probability matrix, we apply a time-dependent (inhomogeneous) transition probability matrix which can capture the temporal dependency for different time instances. Meanwhile, we employ an efficient probabilistic model, i.e., MLR, for emission probability. Online and offline occupancy estimation schemes are presented for real-time and accurate long-term applications respectively. Real experiments have indicated the effectiveness of our proposed approach.