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

The Korean Institute of Electrical Engineers (대한전기학회)
권호 표지

A System Marginal Price Forecasting Method Based on an Artificial Neural Network Using Time and Day Information

시간축 및 요일축 정보를 이용한 신경회로망 기반의 계통한계가격 예측

  • 이정규 (건국대학 공대 전기공학과) ;
  • 신중린 (건국대학 공대 전기공학과) ;
  • 박종배 (건국대학 공대 전기공학과)
  • Published : 2005.03.01
Download PDF
⟨ Previous Next ⟩

Abstract

This paper presents a forecasting technique of the short-term marginal price (SMP) using an Artificial Neural Network (ANN). The SW forecasting is a very important element in an electricity market for the optimal biddings of market participants as well as for market stabilization of regulatory bodies. Input data are organized in two different approaches, time-axis and day-axis approaches, and the resulting patterns are used to train the ANN. Performances of the two approaches are compared and the better estimate is selected by a composition rule to forecast the SMP. By combining the two approaches, the proposed composition technique reflects the characteristics of hourly, daily and seasonal variations, as well as the condition of sudden changes in the spot market, and thus improves the accuracy of forecasting. The proposed method is applied to the historical real-world data from the Korea Power Exchange (KPX) to verify the effectiveness of the technique.

Keywords

References

  1. B.R. Szkuta, L. A. Sanabria, and T. S. Dillon, 'Electricity price short-term forecasting using artificial neural networks', IEEE Transactions on Power Systems, Vol. 14, No.3, pp 851-857, Aug. 1999 https://doi.org/10.1109/59.780895
  2. C. P. Rodriguez, and G. J. Anders, 'Energy price forecasting in the Ontario competitive power system market', IEEE transactions on power systems, Vol 19, No.1, pp. 366-374, February 2004 https://doi.org/10.1109/TPWRS.2003.821470
  3. F. J. Nogales, A. J. Conejo, and R. Espinola, 'Forecasting next-day electricity prices by time series models.' IEEE Transaction on Power Systems, Vol. 17, pp. 342-348, May 2002 https://doi.org/10.1109/TPWRS.2002.1007902
  4. H. Liu, X. Wang, W. Zhang, and G. Xu, 'Market clearing price forecasting based on dynamic fuzzy system.' Proc. 2001 Power System Technology, pp. 890-896, 2001 https://doi.org/10.1109/ICPST.2002.1047528
  5. Dillon T. s. and D. Niebur, Neural Networks Applications in Power System, London: CRL Publishing, 1996
  6. L. Fausett, Fundamentals of Neural Networks, Englewood Cliffs, NJ:Prentice Hall, 1994
  7. F. Gao, X. R. Cao, and A. papalexopoulos, 'Forecasting power market clearing price and quantity using a neural network', Power Engineering Society Summer Meeting, IEEE Vol. 4, pp. 2183-2188, 2000 https://doi.org/10.1109/PESS.2000.866984
  8. J. M. Zurada, Introduction to Artificial Neural Systems, Singapore: West Publishing Company, 1992
  9. K. Y. Lee, Y. T. Cha, and J. H. Park, 'Short-term load forecasting using an artificial neural network,' IEEE Transactions on Power Systems, Vol. 7, No.1, pp. 124-132, February 1992 https://doi.org/10.1109/59.141695