Optimal control strategy for a cutting-edge hybrid ventilation system in classrooms: Comparative analysis based on air pollution levels across cities

Abstract

Natural ventilation has the potential to enhance indoor air quality in classrooms with elevated CO₂ levels, although it may introduce outdoor pollutants. This study introduces a novel controller for automatic windows that simultaneously monitors outdoor air pollution and temperature, synchronizing window openings with mechanical ventilation system to create a comfortable, healthy, and energy-efficient indoor environment. The practicality of the proposed controller is assessed for a classroom in Delhi, Warsaw, and Stockholm, each with contrasting climates and outdoor pollution levels, specifically PM2.5 and NO₂. The controller parameters are optimized for each city using a non-dominated sorting genetic algorithm (NSGA-II) to find the best trade-off between thermal comfort, CO₂ levels, and energy consumption. The results show that the controller successfully met the indoor air quality standards in all cities; however, its operation was significantly influenced by the climate and pollution levels. While natural ventilation was utilized for 44% and 31% of the year in Warsaw and Stockholm, respectively, it was used for only 11% of the year in Delhi, the most polluted city. The optimization process significantly reduced energy use across all cities while also successfully reducing indoor CO₂ concentrations. Although thermal comfort decreased slightly, it remained within acceptable thermal comfort conditions.