An Improved Algorithm of the Daily Peak Load Forecasting fair the Holidays

특수일의 최대 전력수요예측 알고리즘 개선

  • Published : 2002.03.01

Abstract

High accuracy of the load forecasting for power systems improves the security of the power system and generation cost. However, the forecasting problem is difficult to handle due to the nonlinear and the random-like behavior of system loads as well as weather conditions and variation of economical environments. So far. many studies on the problem have been made to improve the prediction accuracy using deterministic, stochastic, knowledge based and artificial neural net(ANN) method. In the conventional load forecasting method, the load forecasting maximum error occurred for the holidays on Saturday and Monday. In order to reduce the load forecasting error of the daily peak load for the holidays on Saturday and Monday, fuzzy concept and linear regression theory have been adopted into the load forecasting problem. The proposed algorithm shows its good accuracy that the average percentage errors are 2.11% in 1996 and 2.84% in 1997.

Keywords

References

  1. 윤용범외 3인, '전력수급계획 및 운용해석 종합시스템개발에 관한 연구', 한국전력공사 전력연구원, Technical Report, TR. 94YJ15. J1998.89, 1998년 12월
  2. K.H. Kim, J.K. Park, J. Hwang and S.H. Kim, 'Implementation of Hybrid Short-term Load Forecasting System Using Artificial Neural Networks and Fuzzy Expert Systems' IEEE Transactions on Power Systems, Vol.10, No.3, pp. 1534-1539, August 1995 https://doi.org/10.1109/59.466492
  3. 조승우, 황갑주, 김성학, '코호넨 신경망을 이용한 단기 전력수요 예측', 전기학회 논문지 46권, 3호, 1997년 3월
  4. 김광호, 황갑주, 박종근, 김성학, '단기전력 수요예측전문가 시스템의 개발', 전기학회 논문지 47권, 3호, 1998년 3월
  5. 황갑주, 김광호, 김성학, '주간수요예측 전문가 시스템개발', 전기학회 논문지, 48권, 4호, 1998년 4월
  6. 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
  7. T. M. Peng, N. F. Hubele and G. G. Karady, 'An Adaptive Neural Network approach to One-Week Ahead Forecasting,' IEEE Transactions on Power Systems, Vol. 8, pp. 1195-1203, 1993 https://doi.org/10.1109/59.260877
  8. 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 Transactions on Power Systems, Vol. 11, No. 2, pp. 858-863, May 1996 https://doi.org/10.1109/59.496166
  9. R. Lamedica, A. Prudenzi, M. S, M. Caciotta, and V. Orsolini Cencelli, 'A Neural Network GBased Technique For Short Term Forecasting of Anomalous Load Periods', IEEE Transactions on Power Systems, Vol. 11, No. 4, pp. 1749-1756, November 1996 https://doi.org/10.1109/59.544638
  10. D. Srinivasan, C. S. Chang, and A. C. Liew, 'Demand forecasting Using fuzzy Neural Computation, With Special Emphasis On Weekend And Public Holiday Forecasting ', IEEE Transactions on Power Systems, Vol. 10, No. 4. pp. 1897-1903, November 1995 https://doi.org/10.1109/59.476055
  11. R. Campo and P. Ruiz, 'Adaptive Weather-Sensitive Short-Term Load Forecast', IEEE Transactions on Power System, Vol. 2, No. 3, pp. 592-600, August 1987 https://doi.org/10.1109/TPWRS.1987.4335174
  12. Hiroyuki Mori, Hidenori Kobayashi, 'Optimal Fuzzy Inference for Short-Term Load Forecasting', IEEE Transactions on Power Systems, Vol. 11, No. l, February 1996 https://doi.org/10.1109/59.486123
  13. 조현호, 백영식, 송경빈, 홍덕헌, '퍼지 선형회귀분석 알고리즘을 이용한 특수일 전력수요예측', 대한전기학회 하계학술대회 논문집, pp. 298-300, 2000년 7월
  14. Dug Hun Hong, Sungho Lee and Hae Young Do, 'Fuzzy linear regression Regression analysis for fuzzyinput-output data using shape-preserving operations', Fuzzy Sets and Systems 122, pp513-526, 2001 https://doi.org/10.1016/S0165-0114(00)00003-8
  15. 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 90, pp. 307-316, 1997 https://doi.org/10.1016/S0165-0114(96)00125-X
  16. 김광호, '특수일 전력수료예측을 위한 퍼지 전문가시스템의 개발', 전기학회 논문지 47권, 제7호, pp. 886-891, 1998년 7월
  17. 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