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

• Published in: IEEE transactions on industrial informatics Vol. 13; no. 5; pp. 2184 - 2193
• Main Authors: Zhenghua Chen, Qingchang Zhu, Masood, Mustafa Khalid, Yeng Chai Soh
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.10.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Air conditioners; Air conditioning; Air monitoring; Building occupancy estimation; Buildings; Emission; Environmental monitoring; environmental sensors; Estimation; Feature extraction; Gaussian distribution; Hidden Markov models; Humidity; Informatics; inhomogeneous hidden Markov model; Invariants; Markov analysis; Markov chains; Mathematical models; multinomial logistic regression; Probabilistic models; Sensors; Statistical analysis; Temperature sensors; Ventilation
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2017.2668444

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.