A model based on Gauss Distribution for predicting window behavior in building. (February 2019) - Pan, Song; Han, Yiye; Wei, Shen; Wei, Yixuan; Xia, Liang; Xie, Lang; Kong, Xiangrui; Yu, Wei
Abstract: Modeling of window behavior is a key component for building performance simulation, due to the significant impact of opening/closing windows on indoor environment and energy consumption. The predictions of existing models cannot well reflect actual window behavior, the prediction accuracy still needs to be improved. The Gauss distribution model is a new machine-learning technique which has achieved successful applications in many fields because of its special advantages (i.e. simple structure, strong operability and flexible nonparametric inference ability) compared to existing models. This paper presents results from a study using the Gauss distribution model to predict window behavior in office building. The data used in this study were from a real building located in Beijing, China, and covered two transitional seasons (from October 1 to November 15, 2014 and from March 15 to May 16, 2015), when natural ventilation was fully applied. When modeling, three types of input variables, i.e., indoor temperature, outdoor temperature and their combination were used. This work validates the importance of selecting suitable input variables when developing Gauss distribution model. This study also compared the prediction performance between the Gauss distribution modeling approach and the Logistic regression modeling approach, which is the most popular method used to model occupant window behavior in buildings. The results showed that Gauss distribution models could provide higher prediction accuracy, with 9.5% higher than Logistic regression model when using suitable inputs. This paper provided a novel modeling method that can be used to predict window states more accurately in office buildings. Highlights: A novel modeling method based on Gauss Distribution to model occupant window behavior is presented. The field data were split into two groups and used to train and validate the model developed using Gauss Distribution. The results showed that Gauss Distribution models provide better prediction results than Logistic regression modelling.