Exposure-based smart ventilation and occupancy control for optimizing ventilation energy consumption and long-range airborne transmission of COVID-19 in school environments

Abstract Mechanical ventilation is an effective measure to control indoor long-range airborne transmission of COVID-19, but it often leads to substantial energy expenditure. This study introduces a novel exposure-based smart ventilation and occupancy control strategy to reduce infection risk and save energy in school environments that are typically characterized by fixed occupants and long exposure time. This exposure-based approach allows the quanta concentration to vary over time rather than keeping it constantly below certain thresholds. This enables us to: (1) adjust ventilation and occupant schedule to facilitate passive cooling/heating potential in response to outdoor weather conditions; (2) consider the interaction between ventilation and occupant schedule to maximize their benefits in reducing infection risk and energy consumption. Taking a typical classroom as a base case, ventilation and occupant schedule are optimized individually and jointly through Genetic Algorithm, to control infection risk, minimize energy consumption, maintain thermal comfort, and promise sufficient schooling time. Our results show that the most energy-efficient strategy is the concurrent optimization of both occupant schedule and ventilation, achieving an energy reduction of up to ∼60% compared to traditional constant ventilation methods. Solely optimizing occupant schedule is the least energy-efficient strategy, yielding an energy reduction ratio (over base case) of only half of the most efficient strategy. Our study reveals the possibility of optimizing occupant schedule and ventilation to balance building energy consumption and transmission control. The viability of these control strategies has been proven across various climate zones and seasons in China, highlighting their broad applicability.