Bayesian estimation of occupancy distribution in a multi-room office building based on CO₂ concentrations

Occupancy information in an office building is an important asset for determining energy-efficient operations and emergency evacuation of a building. In this study, we developed a method to estimate the occupancy distribution in a multi-room office building using Bayesian inference. The Markov chain Monte Carlo algorithm was used to estimate the real-time occupancy in individual rooms based on indoor carbon dioxide concentrations. The office building was made-up of five rooms with different physical configurations and dimensions, and the rooms were air-conditioned and ventilated by a central air handling unit. The carbon dioxide concentration data were generated by the simulation software CONTAMW according to a given schedule of occupancy and the supply airflow rates in each room. The objective of the present paper is to investigate the effects of various parameters of Bayesian inference on the accuracy of estimation results. The parameters include the probability of prior information, the uncertainty level of CO₂ data, and the time interval for monitoring CO₂.