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

The Korean Institute of Electrical Engineers (대한전기학회)
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A Study on the Short-term Load Forecasting using Support Vector Machine

지원벡터머신을 이용한 단기전력 수요예측에 관한 연구

  • 조남훈 (숭실대 공대 전기공학부) ;
  • 송경빈 (숭실대 공대 전기공학부) ;
  • 노영수 (숭실대 공대 전기공학부) ;
  • 강대승 (숭실대 공대 전기공학부)
  • Published : 2006.07.01
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Abstract

Support Vector Machine(SVM), of which the foundations have been developed by Vapnik (1995), is gaining popularity thanks to many attractive features and promising empirical performance. In this paper, we propose a new short-term load forecasting technique based on SVM. We discuss the input vector selection of SVM for load forecasting and analyze the prediction performance for various SVM parameters such as kernel function, cost coefficient C, and $\varepsilon$ (the width of 8 $\varepsilon-tube$). The computer simulation shows that the prediction performance of the proposed method is superior to that of the conventional neural networks.

Keywords

References

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