Comparison of different window behavior modeling approaches during transition season in Beijing, China

Window operation is an important occupant behavior, and has significant impacts on building energy consumption. Recently, various stochastic and non-stochastic models have been proposed, aiming to describe occupant window behavior based on several influencing factors. However, most of the employed methods are logit regression and Markov chain techniques, and the application of machine learning to model occupants' window behavior is rarely investigated. In addition, most published studies referring to occupants' window behavior have been carried out within European countries, where the influence of outdoor air quality is rarely considered. This study compares different models of occupants' window behavior, including models based on logistic regression, Markov processes, and an artificial neural network. An artificial neural network model is proposed to explore the application and optimization of an artificial neural network algorithm under a condition of having less samples. Moreover, the outdoor fine inhalable particles (PM2.5) concentration is considered as an influencing factor for building a window opening model for office buildings during the transition season in China. From this work, it is generally concluded that the PM2.5 concentration and outdoor humidity should be considered in the modeling of occupant window behavior in Beijing, China. In addition, more true estimations can be obtained from artificial neural network models than from logistic regression models and Markov models. This result demonstrates that the proposed artificial neural network yields a prediction model of office window states with higher accuracy and better interpretability of highly correlated factors as compared to logistic regression models and Markov models. The proposed approaches provide a new and detailed way for engineers and building operators to better understand occupant window behaviors and their impacts on energy use in office buildings.