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.
Highlights
Occupancy information is useful for building management to operate lighting and ventilation systems with a view to reduce operational cost [1,2]
We investigate the effect of both the ventilation rate methods upon the estimated number of occupants
Occupancy estimation based on the measured CO2 concentration has been successfully conducted using the Bayesian MCMC approach
Summary
Occupancy information is useful for building management to operate lighting and ventilation systems with a view to reduce operational cost [1,2]. Real-time occupancy monitoring can assume paramount importance during emergency evacuation [3]. Additional equipment and procedures are required to obtain occupancy information in a given space; such equipment may include video cameras, radio-frequency identification tags, and passive infrared sensors. These devices can potentially disturb privacy and are limited only to observing certain areas. Carbon dioxide sensors used for indoor environmental monitoring purposes may have better performance in eliminating the aforementioned drawbacks they have high uncertainty and slow response characteristics
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