Research on primary side flow prediction model of hospital central heating systems based on RF-GA-SVM

 The primary side flow of the hospital central heating systems is affected by many uncertain factors. In order to reduce the dimension of input space, save the operation cost and improve the prediction accuracy, this paper proposes a prediction model based on the random forest （RF） feature importance evaluation-genetic algorithm (GA) optimization support vector machine (SVM) parameter algorithm. Firstly, the RF algorithm is used to evaluate the importance of characteristic variables, and the cross validation method is used to filter the characteristic variables to build an index system of influencing factors of the heating system. Secondly, the genetic algorithm is used to optimize the parameters of the support vector machine to establish a regression prediction model (RF-GA-SVM). Finally, an example is analysed based on the data of a hospital’s central heating system and compared with the RF prediction model and GA-SVM prediction model. The prediction error analysis shows that the prediction model (RF-GA-SVM) proposed in this paper reduces the dimension of input space, avoids local optimization and improves the prediction accuracy.