대한전기학회논문지:전력기술부문A (The Transactions of the Korean Institute of Electrical Engineers A)

대한전기학회 (The Korean Institute of Electrical Engineers)
권호 표지

교차검증을 이용한 SVM 전력수요예측

SVM Load Forecasting using Cross-Validation

  • 조남훈 (숭실대 공대 전기공학부)
  • 발행 : 2006.11.01
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초록

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.

키워드

참고문헌

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