Research on natural gas load forecasting based on support vector regression

Machine learning techniques are finding more and more applications in the field of load forecasting. In this paper a novel regression technique, called Support Vector Machines (SVM), based on the statistical learning theory is explored. SVM is hased on the principle of Structure Risk Minimization as opposed to the principle of Empirical Risk Minimization supported by conventional regression techniques. The natural gas load data in Xi'an city in 2001 and 2002 are used in this study to demonstrate the forecasting capabilities of SVM. The result is compared with that of neural network based model for 7-lead day forecusting. The prediction result shows that prediction accuracy of SVM is better than that of neural network. Thus, SVM appears to he a very promising prediction tool. The software package NGPSLF based on support vector regression (SVR) also has been gone into practical business application.