The Transactions of the Korean Institute of Electrical Engineers D (대한전기학회논문지:시스템및제어부문D)

The Korean Institute of Electrical Engineers (대한전기학회)
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An Improvement Algorithm of the Daily Peak Load Forecasting for Korean Thanksgiving Day and the Lunar New Year's Day

추석과 설날 연휴에 대한 전력수요예측 알고리즘 개선

  • Published : 2002.10.01
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Abstract

This paper proposes an improved algorithm of the daily peak load forecasting for Korean Thanksgiving Day and the Lunar New Year's day. So far, many studies on the short-term load forecasting have been made to improve the accuracy of the load forecasting. However, the large errors of the load forecasting occur i case of Korean Thanksgiving Day and the Lunar New Year's Day. In order to reduce the errors of the load forecasting, the fuzzy linear regression method is introduced and a good selection method of the past load pattern is presented. Test results show that the proposed algorithm improves the accuracy of the load forecasting.

Keywords

References

  1. 윤용범 외 3인, 전력수급계획 및 운용해석 종합시스템 개발에 관한 연구, 한국전력공사 전력연구원, Technical Report, TR.94YJ15.J1998.89, 1998년 12월
  2. K. H. Kim, J. K. Park, K. J. Hwang, and S. H. Kim, 'Implementation of hybrid short-term load forecasting in an autonomous power system using artificial neural networks,' IEEE Trans. Power Systems, vol. 10, no. 3, pp. 1534-1539, 1995 https://doi.org/10.1109/59.466492
  3. 김광호, 황갑주, 박종근, 김성학, 단기전력 수요예측 전문가 시스템의 개발, 전기학회 논문지 47권, 제3호, pp.284-290, 1998년 3월
  4. S. Rahman, and R. Bhatnagar, An Expert System Based Algorithm for Short-Term Load Forecast, IEEE Transactions on Power Systems, Vol. 3, No. 1,pp. 50-55, 1987 https://doi.org/10.1109/59.192889
  5. T. M. Peng, N. F. Hubele and G. G. Karady, An Adaptive Neural Network approach to One-Week ahead Forecasting, IEEE Transactions on Power System, Vol. 8, pp. 1195-1203, 1993 https://doi.org/10.1109/59.260877
  6. A. G. Bakirtzis, V.Petridis, S. J. Kiartzis, M. C. Alexiadis, and A. H. Maissis, 'A neural network short term load forecasting model for the Greek power system,' IEEE Trans. Power Systems, vol. 11, no. 2, pp. 858-863, 1996 https://doi.org/10.1109/59.496166
  7. R. Lamedica, A. Prudenzi, M. Sfoma, M. Caciotta, and V. O. Cencelli, 'A neural network based technique for short-term forecasting of anomalous load periods,' IEEE Trans. Power Systems, vol. 11, no. 4, pp. 1749-1756, 1996 https://doi.org/10.1109/59.544638
  8. Hiroyuki Mori, Hidenori Kobayashi, Optimal Fuzzy Inference for Short-Term Load Forecasting. IEEE Transactions on Power Systems, Vol. 11, No. 1, February 1996
  9. 조현호, 백영식, 송경빈, 홍덕헌, 퍼지 선형회귀분석 알고리즘을 이용한 특수일 전력수요예측, 대한전기학회하계학술대회 논문집, pp. 298-300, 2000년 7월
  10. 송경빈, 구본석, 백영식, 특수일의 최대 전력수요예측 알고리즘 개선, 전기학회 논문지 51권, 3호, pp.109-117, 2002년 3월
  11. D.H. Hong, S.H. Lee, H.Y. Do, 'Fuzzy Linear Regression Analysis for Fuzzy Input-output Data Using Shape Preserving Operations,' Fuzzy Sets and Systems 122, pp.513-526, September 2001 https://doi.org/10.1016/S0165-0114(00)00003-8
  12. D.H. Hong and H.Y. Do, Fuzzy Systems Reliability Analysis By The Use of Tw(the weakest t-norm) on Fuzzy Number Arithmetic Operations, Fuzzy Sets and Systems Vol. 90, pp. 307-316, September 1997 https://doi.org/10.1016/S0165-0114(96)00125-X
  13. 김광호, 특수일 전력수요예측을 위한 퍼지 전문가시스템의 개발, 전기학회 논문지 47권, 제7호, pp. 886-891, 1998년 7월
  14. K.H. Kim, Short-Term Load Forecasting for Special Days in Anomalous Load Conditions Using Neural Networks and Fuzzy Inference Method, IEEE Transactions on Power Systems, Vol. 15, No. 2, pp. 559-565, May 2000 https://doi.org/10.1109/59.867141
  15. 구본석, 백영식, 송경빈, 연휴에 대한 전력수요예측, 대한 전기학회 추계학술대회 논문집, pp.256-258, 2001년 11월