Controlling and Limiting Infection Risk, Thermal Discomfort, and Low Indoor Air Quality in a Classroom through Natural Ventilation Controlled by Smart Windows

Abstract

In this study, a controller method for window opening was developed to naturally ventilate a classroom with 30 occupants. The aim was to improve indoor environment quality and limit the probability of COVID infection risk simultaneously. The study was based on a building performance simulation using combined EnergyPlus, CONTAM, and Python programs. Seven cases with automatically opening windows were considered. Opening window parameters were optimized by genetic algorithms. It was shown that the optimized controller with indoor environment functions improved classroom ventilation and considerably decreased CO2 concentration compared to a reference case where the windows were opened only during breaks, and the controller also improved occupants’ thermal comfort. However, there was a noticeable increase in energy demand, caused by the increased air change rate. Introducing the probability of infection risk function to the controller did not reduce the transmission risk substantially, and the probability of infection transmission was high for 80% of the classroom occupancy time. The risk of infection changed only when additional actions were taken, such as introducing face masks, indoor air cleaners, or reducing the number of students present in the classroom. In these cases, it was possible to prevent the infection transmission for more than 90% of the lecture time (R0 < 1).