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Development of Daily Peak Power Demand Forecasting Algorithm using ELM

ELM을 이용한 일별 최대 전력 수요 예측 알고리즘 개발

  • Received : 2013.10.29
  • Accepted : 2013.11.27
  • Published : 2013.12.01

Abstract

Due to the increase of power consumption, it is difficult to construct an accurate prediction model for daily peak power demand. It is very important work to know power demand in next day to manage and control power system. In this research, we develop a daily peak power demand prediction method based on Extreme Learning Machine(ELM) with fast learning procedure. Using data sets between 2006 and 2010 in Korea, the proposed method has been intensively tested. As the prediction results, we confirm that the proposed method makes it possible to effective estimate daily peak power demand than conventional methods.

Keywords

References

  1. 남봉우, 송경빈, 김규호, 차준민, "다중회귀분석법을 이용한 지역전력수요예측 알고리즘", 조명.전기설비학회 논문제,Vol. 22, No. 2, pp. 63-70, 2008, https://doi.org/10.5207/JIEIE.2008.22.2.063
  2. R. Ramanathan, R. Engle, C. W. J. Granger, F. VahidAraghi, C. Brace, "Short-term forecasts of electricity loads and peaks," International Journal of Forecasting, Vol. 13, pp. 161-174, 1997. https://doi.org/10.1016/S0169-2070(97)00015-0
  3. J. W. Taylor, "Short-term electricity demand forecasting using double seasonal exponential smoothing," Journal of the Operational Research Society, Vol. 54, pp. 799-805, 2003. https://doi.org/10.1057/palgrave.jors.2601589
  4. J. W. Taylor, "Triple seasonal methods for short-term electricity demand forecasting," European Journal of Operational Research, Vol. 204, pp. 139-152, 2010. https://doi.org/10.1016/j.ejor.2009.10.003
  5. R. Weron, "Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach," Wiley, 2006.
  6. 이형로, 신현정, "Support Vector Regression에 기반한 전력 수요 예측," IE Interfaces, Vol. 24, No. 4, pp. 351-361, 2011. https://doi.org/10.7232/IEIF.2011.24.4.351
  7. 박영진, 왕보현, "뉴로-퍼지 모델 기반 전력 수요예측 시스템 : 시간, 일간, 주간 단위 예측," 퍼지및지능시스템학회 논문지, Vol. 14, No. 5, pp. 533-538, 2004. https://doi.org/10.5391/JKIIS.2004.14.5.533
  8. A. S. pandy, D. Singh, S. K. Sinha, "Intelligent Hybrid Wavelet Models for Short-Term Load Forecasting," IEEE Trans. on Powe systems, Vol. 25, No. 3, pp. 1266-1273, 2010. https://doi.org/10.1109/TPWRS.2010.2042471
  9. C. Guan, P. B. Luh, L. D. Michel, Y. Wang, P. B. Friedland, "Very Short-Term Load Forecasting: Wavelet Neural Networks With Data Pre-Filtering," IEEE Trans. on Power systems, Vol. 28, No. 1, pp. pp.30-41, 2013. https://doi.org/10.1109/TPWRS.2012.2197639
  10. M. Hanmandlu, B. K. Chauhan, "Load Forecasting Using Hybrid Models," IEEE Trans. on Power systems, Vol. 26, No. 1, pp. 20-29, 2011.
  11. G. B. Huang, Q. Y. Zhu, and C. K. Siew, "Extreme learning machine: a new learning scheme of feedforward neural networks," in Proc. 2004 IEEE Int. Conf. Neural Networks, Vol. 2, pp. 985-990, 2004.
  12. G. B. Huang, Q. Y. Zhu, and C. K. Siew, "Extreme learning machine: theory and applications," Neurocomputing, Vol. 70, No. 1-3, pp. 489-501, 2006. https://doi.org/10.1016/j.neucom.2005.12.126
  13. D. Serre, Matrices : Theory and Application, New York, Springer-Verlag, 2002.