Skip to main content
Article thumbnail
Location of Repository

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

By Zhang Xing-ping and Gu Rui

Abstract

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:10.1.1.416.9086
Provided by: CiteSeerX
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • http://citeseerx.ist.psu.edu/v... (external link)
  • http://www.wseas.us/e-library/... (external link)
  • Suggested articles


    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.