TY - JOUR
T1 - Optimal control strategy for a cutting-edge hybrid ventilation system in classrooms
T2 - Comparative analysis based on air pollution levels across cities
AU - Nateghi, Seyedkeivan
AU - Behzadi, Amirmohammad
AU - Kaczmarczyk, Jan
AU - Wargocki, Pawel
AU - Sadrizadeh, Sasan
PY - 2025
Y1 - 2025
N2 - 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.
AB - 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.
KW - Smart controllers
KW - Hybrid ventilation
KW - Air quality
KW - EnergyPlus
KW - Multi-objective optimization
KW - Window opening
U2 - 10.1016/j.buildenv.2024.112295
DO - 10.1016/j.buildenv.2024.112295
M3 - Journal article
SN - 0360-1323
VL - 267
JO - Building and Environment
JF - Building and Environment
M1 - 112295
ER -