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

The Korean Institute of Electrical Engineers (대한전기학회)
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SVM Load Forecasting using Cross-Validation

교차검증을 이용한 SVM 전력수요예측

  • 조남훈 (숭실대 공대 전기공학부)
  • Published : 2006.11.01
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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

References

  1. D.G. Infield and D.C. Hill, 'Optimal smoothing for trend removal in short term electricity demand forecasting,' IEEE Trans. Power Systems, vol. 13, no. 3, pp. 1115-1120, 1998 https://doi.org/10.1109/59.709108
  2. J.H. Park, Y.M. Park, and K.Y. Lee, 'Composite modeling for adaptive short-term load forecasting,' IEEE Trans. Power Systems, vol. 6, no. 2, pp. 450-457, 1991 https://doi.org/10.1109/59.76686
  3. J.W. Taylor and S. Majithia, 'Using combined forecasts with changing weights for electricity demand profiling,' J. Oper, Res. Soc., vol. 51, no. 1, pp, 72-82, 2000 https://doi.org/10.2307/253949
  4. S.E. Papadakis, J.B. Theocharis, S.J. Kiartzis, and A.G. Bakirtzis, 'A novel approach to short-term load forecasting using fuzzy neural networks,' IEEE Trans. Power Systems, vol. 13, no. 2, pp. 480-492, 1998 https://doi.org/10.1109/59.667372
  5. Kyung-Bin Song, Young-Sik Baek, Dug Hun Hong, and lang, G., 'Short-term load forecasting for the holidays using fuzzy linear regression method,' IEEE Trans. Power Systems, vol. 20, no. 1, pp. 96-101, 2005 https://doi.org/10.1109/TPWRS.2004.835632
  6. H.S. Hippert, C.E. Pedriera, and R.C. Souza, 'Neural Networks for Short-term Load Forecasting: A Review and Evaluation,' IEEE Trans. Power Systems, vol. 16, no. 1, pp. 44-55, 2001 https://doi.org/10.1109/59.910780
  7. Reis, A.J.R. and da Silva, A.P.A, 'Feature extraction via multiresolution analysis for short-term load forecasting,' IEEE Trans. Power Systems, vol. 20, no. 1, pp. 189-198, 2005 https://doi.org/10.1109/TPWRS.2004.840380
  8. C.H.C. Burges, 'A tutorial on Support Vector Machines for Pattern Recognition,' Data Mining and Knowledge Discovery,' pp. 121-167, 1998 https://doi.org/10.1023/A:1009715923555
  9. N. Cristianini and J. Shawe-Taylor, 'An introduction to Support Vector Machines,' Cambridge University press, 2003
  10. 조남훈, 송경빈, 노영수, 강대승, '지원벡터머신을 이용한 단기전력 수요예측에 관한 연구,' 대한전기학회 논문지, Vol.55A, No. 7, pp. 306-312, Jun., 2005
  11. 하성관, 송경빈, 김홍래, '신경회로망과 하절기 온도 민감도를 이용한 단기 전력 수요 예측,' 대한전기학회 논문지, Vol. 54A, No. 6, pp. 259-266, Jun., 2005
  12. 지평식, 남상천, 임재윤, 김정훈, '분류된 부하패턴을 근거로 한 단기 전력 수요 예측,' 대한전기학회 논문지, Vol. 47, No. 3, pp. 269-275, March, 1998
  13. S. Haykin, Neural Networks, New Jersey: Prentic e-Hall, 1999