Since the coronavirus disease 2019, the extended time indoors makes people more concerned about indoor air quality, while the increased ventilation in seeks of reducing infection probability has increased the energy usage from heating, ventilation, and air-conditioning systems. In this study, to represent the dynamics of indoor temperature and air quality, a coupled grey-box model is developed. The model is identified and validated using a data-driven approach and real-time measured data of a campus office. To manage building energy usage and indoor air quality, a model predictive control strategy is proposed and developed. The simulation study demonstrated 18.92% energy saving while maintaining good indoor air quality at the testing site. Two nationwide simulation studies assessed the overall energy saving potential and the impact on the infection probability of the proposed strategy in different climate zones. The results showed 20%-40% energy saving in general while maintaining a predetermined indoor air quality setpoint. Although the infection risk is increased due to the reduced ventilation rate, it is still less than the suggested threshold (2%) in general.
Electronic supplementary material esm: The Appendix is available in the online version of this article at 10.1007/s12273-022-0936-6.
Keywords: energy-saving; indoor air quality; infection risk; large-scale simulation; model predictive control.
© Tsinghua University Press 2022.