摘要
Natural gas load forecasting is a key process to the efficient operation of pipeline network. An accurate forecast is required to guarantee a balanced network operation and ensure safe gas supply at a minimum cost.Machine learning techniques have been increasingly applied to load forecasting. A novel regression technique based on the statistical learning theory, support vector machines (SVM), is investigated in this paper for natural gas shortterm load forecasting. SVM is based on the principle of structure risk minimization as opposed to the principle of empirical risk minimization in conventional regression techniques. Using a data set with 2 years load values we developed prediction model using SVM to obtain 31 days load predictions. The results on city natural gas short-term load forecasting show that SVM provides better prediction accuracy than neural network. The software package natural gas pipeline networks simulation and load forecasting (NGPNSLF) based on support vector regression prediction has been developed, which has also been applied in practice.
Natural gas load forecasting is a key process to the efficient operation ofpipeline network. An accurate forecast is required to guarantee a balanced network operation andensure safe gas supply at a minimum cost. Machine learning techniques have been increasingly appliedto load forecasting. A novel regression technique based on the statistical learning theory, supportvector machines (SVM), is investigated in this paper for natural gas short-term load forecasting.SVM is based on the principle of structure risk minimization as opposed to the principle ofempirical risk minimization in conventional regression techniques. Using a data set with 2 yearsload values we developed prediction model using SVM to obtain 31 days load predictions. The resultson city natural gas short-term load forecasting show that SVM provides better prediction accuracythan neural network. The software package natural gas pipeline networks simulation and loadforecasting (NGPNSLF) based on support vector regression prediction has been developed, which hasalso been applied in practice.
