NDLI: Correlation Coefficient Method for Support Vector Machine Input Samples

Content Provider	IEEE Xplore Digital Library
Author	Kui-He Yang Gan-Lin Shan Ling-Ling Zhao
Copyright Year	2006
Description	Author affiliation: Dept. of Opt. & Electron Eng., Ordnance Eng. Coll., Shijiazhuang (Kui-He Yang; Gan-Lin Shan)
Abstract	When the support vector machine is used for load forecasting, the input samples of support vector machine have important effect to forecasting results. Support vector machine can study any non-linear relation, but if a group of non-distinct variables are selected as input variable set, the training time of support vector machine is lengthened and the errors become bigger. The non-linear relation of the load can be effectively explained only when a group of appropriate input variables are found. In this paper, the correlation coefficient idea is used to input variables selection of support vector machine short-term load forecasting model. The load values, which have bigger correlation coefficient with expectation output values, are chosen from effect factor sets as input variables. By mean of this method, a preferable input variables set can be gained, the correlation between the input variables and the forecasting points are bigger, and the forecasting results is more exact. The simulation results show that the method is effective
Sponsorship	IEEE Syst., Man and Cybernetics Hebei Univ.
Starting Page	2857
Ending Page	2861
File Size	177193
Page Count	5
File Format	PDF
ISBN	1424400619
DOI	10.1109/ICMLC.2006.259069
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-08-13
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Input variables Load forecasting Weather forecasting Load modeling Predictive models Educational institutions Power generation Electron optics Nonlinear optics short-term load forecasting Support vector machine input variables selection
Content Type	Text
Resource Type	Article

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