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Electric Power Demand Forecasting Based on Cointegration Analysis and a Support Vector Machine

By Zhang Xing-ping and Gu Rui


Abstract: In the process of cointegration analysis, electricity consumption is chosen as the explained variable, and GDP per capita, heavy industry share, and efficiency improvement are chosen as the explanatory variables; then a cointegration model is put forward, which shows that there is a cointegration relationship between the explained variable and explanatory variables. The explained and explanatory variables are input into a support vector machine (SVM), and a Gaussian radial basis function is taken as the kernel function. So an electricity demand forecasting model based on multivariate SVM is established. The example provides evidence for the validity of the forecasting model

Topics: Key words, Support vector machine, Multivariate time series, Unit root test, Cointegration analysis
Year: 2014
OAI identifier: oai:CiteSeerX.psu:
Provided by: CiteSeerX
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