Real-time ventilation control based on a Bayesian estimation of occupancy
occupancy estimation, Bayesian MCMC, carbon dioxide, ventilation control
Demand-controlled ventilation (DCV) is commonly implemented to provide variable amounts of outdoor air according to an internal ventilation demand. The objective of the present study is to investigate the applicability and the performance of occupancy-based DCV schemes in comparison with time-based and CO2-based DCV schemes. To do this, we apply the occupancy estimation method by the Bayes theorem to control the ventilation rate of an office building in real-time. We investigated six cases in total (two cases for each control scheme). Experiments were conducted in a small office room with controllable ventilation equipment and relevant sensors. The observed results indicated that the occupancy-based schemes relying on Bayes theorem could be applied successfully to perform continuous control of ventilation rates without causing recursive problems. Additionally, we discussed the time delays associated with the control procedure, including dispersion time, sensor-response time, and data processing time. Finally, we compared the performance of the proposed approach in six DCV cases in terms of a resultant indoor CO2 level and the total ventilation-air volume. We concluded that DCV control based on both occupancy and floor area provided the best conformity to the ASHRAE standard among the analyzed schemes.
Tsinghua University Press
Haolia Rahman, Hwataik Han. Real-time ventilation control based on a Bayesian estimation of occupancy. Building Simulation 2021, 14(5): 1487-1497.