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SVM Load Forecasting using Cross-Validation  

Jo, Nam-Hoon (숭실대 공대 전기공학부)
Publication Information
The Transactions of the Korean Institute of Electrical Engineers A / v.55, no.11, 2006 , pp. 485-491 More about this Journal
Abstract
In this paper, we study the problem of model selection for Support Vector Machine(SVM) predictor for short-term load forecasting. The model selection amounts to tuning SVM parameters, such as the cost coefficient C and kernel parameters and so on, in order to maximize the prediction performance of SVM. We propose that Cross-Validation method can be used as a model selection algorithm for SVM-based load forecasting technique. Through the various experiments on several data sets, we found that the difference between the prediction error of SVM using Cross-Validation and that of ideal SVM is less than 5%. This shows that SVM parameters for load forecasting can be efficiently tuned by using Cross-Validation.
Keywords
Load Forecasting; Support Vector Machine; Model Selection; Cross-Validation;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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