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《Bulletin of Science and Technology》 2013-12
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Consumption Prediction of Natural Gas Based on Principal Component Analysis and Support Vector Machine

Jiang Min;Taizhou Teachers College;  
In order to improve the accuracy of the prediction of natural gas consumption,a model is presented to forecast natural gas consumption and support vector machine combined with a principal component analysis(PCA-SVM).The first principal component analysis was used to select factors that influence the consumption of natural gas,and then input to a nonlinear prediction ability of training support vector machine,genetic algorithm is used to optimize the parameters of SVM,the last natural gas consumption prediction model.The simulation results show that,PCA-SVM accelerate the learning speed prediction of natural gas,to improve the accuracy of the prediction of natural gas consumption.
【CateGory Index】: TP18;F426.22
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