Smart control of windows for intermittent ventilation in public housing in Hong Kong based on deep neural network models - Shi, Yifu; Dai, Ho Kam (Frontiers in Built Environment, 2025)
Climate change has led to an increase in the frequency and intensity of heatwaves, making Hong Kong particularly hot during summer months. As a result, residents in Hong Kong’s public housing buildings heavily rely on air conditioning, leading to poor ventilation when used for extended periods. To achieve proper ventilation, people often resort to intermittent ventilation, opening windows for short periods to allow fresh air to circulate. However, there is currently no specific guideline or approach tailored for public housing in Hong Kong. To address this issue, the study proposed a smart control strategy for windows to achieve effective intermittent ventilation with the shortest window opening duration for public housing in Hong Kong. First, deep neural network (DNN) models were developed to predict the ventilation rate for each unit of a public housing building in Hong Kong, with the database obtained from computational fluid dynamics (CFD) and multi-zone airflow models. Based on the trained DNN models, a smart window control strategy was proposed to minimize the window opening period for intermittent ventilation. The results show that, for the 12 studied cases, on average, the proposed algorithm minimized the window opening duration for intermittent ventilation to 9.5 min, which was 68% shorter than the 30-min guideline, while maintaining the same intermittent ventilation effectiveness. The proposed smart control strategy for intermittent ventilation can minimize the window opening period so that thermal discomfort and exposure to heat could be minimized, especially for the elderly, in public housing during hot seasons in Hong Kong.