Research on personnel window opening behavior based on Logistic regression and average Bayesian network models

 Investigates the personnel window opening behaviors in the dormitories of a university in Tianjin during a whole heating season. Simplifies the input parameters of the traditional Logistic window prediction model, and presents a simplified Logistic regression model with higher prediction accuracy and practicability. Predicts the window opening behavior by the average Bayesian network model, and obtains a better prediction effect, with 82.22% of the prediction accuracy. The prediction accuracy of window opening of the average Bayesian network model is 14.16% higher than that of the Logistic regression model, which reflects the superiority of the average Bayesian network model in the prediction of window opening behaviors.