基于Logistic回归和平均贝叶斯网络的人员开窗行为研究

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

对天津某高校宿舍内的人员开窗行为进行了一个完整供暖季的监测。对传统Logistic开窗预测模型的输入参数进行了简化,提出了预测准确度较高且更具实用价值的简化Logistic回归模型。并将平均贝叶斯网络模型引入开窗行为的预测中,取得了较好的预测效果,模型预测准确率为82.22%,其中开窗的预测准确率比Logistic回归模型提高14.16%,体现了平均贝叶斯网络模型在开窗行为预测中的优越性。

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.