关键词: 天然气长期负荷,    , SVM,    , BP神经网络,    ,  遗传算法,    ,  交叉验证法,    ,  预测,    ,  精度

Abstract:

      Long-term natural gas load forecasting can solve the problem of the imbalance between supply and demand of city gas and provide assistance for the city gas company's management and running. In order to improve the accuracy of predicting the longterm natural gas load，a forecasting model of natural gas longterm load was built based on SVM-GA(Support Vector MachinesGenetic Algorithm). The relevant factors influencing natural gas consumption was analyzed and determined. In order to improve prediction accuracy， the penalty factor c and the kernel parameter g of support vector machines were optimized using genetic algorithm and cross validation methods. Optimized parameters were inputted support vector machines model and long-term natural gas load forecasting was made. In a case study from a certain city，a comparative analysis was made of the forecasting results among SVM-GA，SVM and crossvalidation method combined prediction model and BP(Back Propagation) neural networks. The forecasting model based on SVM-GA was validated with a high prediction accuracy and the resulted relative mean square error，normalization mean square error，normalization absolute square error，normalization rootmean square error， maximum absolute error resulted from the SVM-GA were lower than those from SVM and crossvalidation method combined prediction model or BP neural networks by 0.58%，3.98%，2.99%，4.58%，8.64% and 6.13%，26.28%，19.71%，21.09%，31.48%. Therefore，the support vector machine and genetic algorithm combined model can accurately predict the long-term natural gas load.

Key words: Natural gas long-term load,    , SVM,    , BP neural networks,    , Genetic algorithm,    , Cross validation,    , Forecast,    , Accuracy