Estimation method of actual heating heat index based on elastic network regression model
In order to meet the demand of estimating regional heat load for cogeneration enterprises,an estimation method using elastic network regression model is proposed.Firstly,the influencing factors of the actual heating heat index are analyzed to determine the input parameters of the model.Then,based on the actual operation data of 123 residential areas in Xi'an in the heating season from 2022 to 2023,the estimation model is established,and it is proved that the accuracy of the model is higher than that of Lasso regression and ridge regression models.Finally,part of the communities in Xi'an are selected to form a verification set to verify the elastic network regression model.The verification results show that,the elastic network regression model combines the advantages of Lasso regression and ridge regression,and has higher prediction accuracy than the conventional machine learning model.The MAE and goodness of fit of the model are 1.150 and 0.953,respectively,indicating that the method can accurately estimate the actual heating heat index with different parameters,and can meet the actual engineering needs of cogeneration enterprises.
万方数据
维普
