Developing a Hidden Markov Model for occupancy prediction in high-density higher education buildings

Alfalah, B., Shahrestani, M. ORCID: https://orcid.org/0000-0002-8741-0912 and Shao, L. ORCID: https://orcid.org/0000-0002-1544-7548 (2023) Developing a Hidden Markov Model for occupancy prediction in high-density higher education buildings. Journal of Building Engineering, 73. 106795. ISSN 2352-7102 doi: 10.1016/j.jobe.2023.106795

Abstract/Summary

The number of occupants in a building as well as the occupancy patterns and profiles have a significant impact on the building energy consumption associated with heating, cooling, ventilation, and lighting systems. A range of approaches has been used in previous studies for building occupancy prediction. However, there were limitations associated with the methods used for data collection. For instance, the prediction of occupancy based on the concentration of CO2 level adds an additional level of uncertainty into the estimation of the building occupancy level. To avoid such uncertainties, high-resolution passive infrared video camera sensors were used to capture occupancy data for an extended period of one year. The collected occupancy data have then been used to develop a Hidden Markov Model (HMM), to predict the occupancy levels in a high-density higher education case study building. The model is trained under different scenarios to find the most appropriate set of attributes associated with occupancy data that can be used to develop the data driven HMM. The occupancy attributes considered include day, week, month, and term. Moreover, the collected dataset was optimally split into 70% for training and 30% for validation using cross-validation, which yields high prediction accuracy. The results of the prediction model under different scenarios were evaluated using root mean square error and Kullback Leibler (KL) divergence.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/111922
Identification Number/DOI	10.1016/j.jobe.2023.106795
Refereed	Yes
Divisions	Science > School of the Built Environment > Construction Management and Engineering
Publisher	Elsevier
Download/View statistics	View download statistics for this item