Investigation of occupant window opening behaviour during the summer period in a Beijing maternity hospital. (January 2022) - Niu, Baolian; Li, Dan; Yu, Haowei; Mahyuddin, Norhayati; Liu, Yiqiao; Wu, Jinshun; Wang, Xinru; Pan, Song
Abstract: Window opening behavior in maternity hospital wards is the result of collective actions to create a healthy and comfortable recovery area for the patients and it is a main strategy of natural ventilation. Since many rooms in the hospital are occupied 24 h a day, which could lead to different window operation characteristics from other building types. However, the global understanding of window opening behavior at maternity hospitals is very limited as little research has been undertaken. In order to fully understand the behavioral characteristic and improve the prediction accuracy, monitoring data from a maternity hospital in Beijing during summer were analyzed and modeled by statistical method and random forest (RF) algorithm respectively. The data included nine correlated factors of window operation in three wards and a doctors' office. The results indicated that the hourly window opening probability of the wards changed regularly with time from 10 to 65%, whereas the probability of the doctors' office window being opened remained at ∼ 70%, which was quite different from the characteristics of other building types in Beijing during summer. Among the correlated factors, the outdoor PM2.5 concentration was the most significant for two room types, which was a valuable finding for window status analysis in hospitals. Satisfied prediction accuracy of the RF model was obtained, which provide a reliable reference for the modeling of hospital buildings. This research could deepen the understanding of the behavior in hospital buildings and provide reference for future modeling research. Highlights: The window-opening behavior characteristics of wards and doctors' offices are not only different from other building types, but also different from each other. The random forest algorithm is used to model window status and the prediction accuracy can reach 91.6%. Outdoor PM2.5 concentration is the most significant correlated factor of window status for the monitored hospital.