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

The Korean Institute of Electrical Engineers (대한전기학회)
권호 표지

Load Forecasting for Holidays Using a Fuzzy Least Squares Linear Regression Algorithm

퍼지 최소 자승 선형회귀분석 알고리즘을 이용한 특수일 전력수요예측

  • Published : 2003.04.01
Download PDF
⟨ Previous Next ⟩

Abstract

An accurate load forecasting is essential for economics and stability power system operation. Due to high relationship between the electric power load and the electric power price, the participants of the competitive power market are very interested in load forecasting. The percentage errors of load forecasting for holidays is relatively large. In order to improve the accuarcy of load forecasting for holidays, this paper proposed load forecasting method for holidays using a fuzzy least squares linear regression algorithm. The proposed algorithm is tested for load forecasting for holidays in 1996, 1997, and 2000. The test results show that the proposed algorithm is better than the algorithm using fuzzy linear regression.

Keywords

References

  1. K.H. Kim, J.K. Park, K.J. Hwang and S.H. Kim,'Implementation of Hybrid Short-term Load Forecasting Systme 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
  2. 김광호, 황갑주, 박종근, 김성학, '단기전력 수요예측 전문가 시스템의 개발', 전기학회 논문지 47권, 3호, pp. 284-290, 1998년 3월
  3. S. Rahman, 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
  4. 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, August 1993 https://doi.org/10.1109/59.260877
  5. 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 Greck Power System', IEEE Transactions on Power Systems, Vol. 11, No. 2, pp. 858-863, May 1996 https://doi.org/10.1109/59.496166
  6. R. Lamedica, A. Prudenzi, M. S, M. Caciotta, and V. Orsolini Cencelli,' A Neural Network GBased Techinique For Short-Term Forecasting of Anomalous Load Periods', IEEE Transactions on Power systems, Vol. 11, No. 4, pp. 1749-1756, Novemer 1996 https://doi.org/10.1109/59.544638
  7. Hiroyuki Mori, Hidenori Kobayashi,' Optimal Fuzzy Inference for Short-Term Load Forecasting', IEEE Transactions on Power Systems, Vol. 11, No. 1, February 1996 https://doi.org/10.1109/59.486123
  8. 조영호, 백영식, 송경빈, 홍덕헌, '퍼지 선형회귀분석 알고리즘을 이용한 특수일 전력수요예측', 대한 전기학회 하계학술대회 논문집, pp. 298-300, 2000년 7월
  9. D.H. Hong, S.H. Lee and H.Y. Do, 'Fuzzy Linear Regression Data Using Shape Preserving Operations', Fuzzy Sets and Systems, Vol 122, pp. 513-526, September 2001 https://doi.org/10.1016/S0165-0114(00)00003-8
  10. 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
  11. D.H. Hong and H.Y. Do and J.K. Song, 'Fuzzy least-squares linear regression analysis using shape preserving operations', Fuzzy Sets and System Vol. 90, pp. 307-316, september 1997 https://doi.org/10.1016/S0020-0255(01)00125-6
  12. 김광호, '특수일 전력수요예측을 위한 퍼지 전문가시스템의 개발', 전기학회 논문지 47권, 제7호, pp. 886-891, 1998년 7월
  13. 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
  14. 구본석, 백영식, 송경빈, '퍼지 최소자승 선형회귀분석 알고리즘을 이용한 특수일의 전력수요예측', 대한전기학회 추계학술대회 논문집, pp. 51-53, 2001년 11월