Abstract

We introduce a Bayesian Markov chain Monte Carlo approach for occupancy estimation in a room with an immeasurable ventilation rate, with the objective of investigating the effects of ventilation estimation and uncertainty in CO₂ data on the occupancy estimation. Measured CO₂ concentrations are used as inputs for the Bayesian estimation and ventilation calculation. The ventilation rate is obtained quantitatively by the concentration decay and the sum-up methods. The ventilation rate is determined by the decay rates at night with no occupancy and by sum up the average concentration level during a day with known occupancy. The Bayesian calculation uses a mathematical model based on the dynamic CO₂ mass-balance equation in space. The result shows that the accuracy of occupancy estimation depends upon the estimate of ventilation rate, as well as the uncertainty in CO₂ measurements.