Abstract

Demand controlled ventilation is an energy saving approach used to regulate outdoor air supply to a space according to its demand. The occupancy within a space is a useful parameter reflecting the ventilation requirement. The objective of the present study is to develop a method for estimating the occupancy in a subway station based on CO₂ and PM₁₀ concentration data using the Indoor Air Quality Tele-Monitoring System located in the station. A feasibility study has been conducted to investigate the monitoring system can provide occupancy information with satisfactory accuracy for ventilation control purposes. Bayesian inference is used in estimating the occupancy at a platform based on unknown information such as ventilation rate and CO₂ generation rate per person using various assumptions. The posterior distribution of the occupancy was simulated using the Markov Chain Monte Carlo sampling method. The results indicate that the dynamic model reduces the effect of the time delay and improves the uncertainty bands in the occupancy inference more than the static model. The inferred occupancy results are within the uncertainty ranges of the actual occupancy in the station. Additional use of the PM₁₀ concentration data improves the accuracy of the inference further.