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

The Korean Institute of Electrical Engineers (대한전기학회)
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A Study on the Demand Forecasting Control using A Composite Fuzzy Model

복합 퍼지모델을 이용한 디맨드 예측 제어에 관한 연구

  • Published : 2002.09.01
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Abstract

This paper presents an industrial peak load management system for the peak demand control. Kohonen neural network and wavelet transform based techniques are adopted for industrial peak load forecasting that will be used as input data of the peak demand control. Firstly, one year of historical load data of a steel company were sorted and clustered into several groups using Kohonen neural network and then wavelet transforms are applied with Biorthogonal 1.3 mother wavelet in order to forecast the peak load of one minute ahead. In addition, for the peak demand control, composite fuzzy model is proposed and implemented in this work. The results are compared with those of conventional model, fuzzy model and composite model, respectively. The outcome of the study clearly indicates that the composite fuzzy model approach can be used as an attractive and effective means of the peak demand control.

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References

  1. '1MW급 전력저장전지시스템 기술개발', 산업자원부, 1998
  2. 장정태, '에너지 절약기술 특집', 전기학회지, 47권, 7호, 1998
  3. Humberto Jorge, A. Martin, Alvaro Gomes, 'Maximum demand control: A Survey and comparative evaluation of different methods', IEEE Trans. on PS, Vol .8, No. 3, pp.1013-1019, 1993 https://doi.org/10.1109/59.260899
  4. G. Mbamulu, F. El-Hawary, M.E. El-Hawary, 'Decomposition approach to forecasting electric power system commercial load using an artificial neural network', Electric Machines and Power Systems, Vol. 25, pp. 875-883, 1997 https://doi.org/10.1080/07313569708955782
  5. K.Y. Lee, Y.T. Cha, J.H. Park 'Short-term for forecasting using an artificial neural network', IEEE Trans. on Power Delivery, Vol. 7, No. 1, pp. 124-131, 1992 https://doi.org/10.1109/59.141695
  6. D. Niebur, A. Germond, 'Power system static security assessment using the Kohonen neural network classifier', IEEE Trans. on Power Systems, Vol. 7, No. 2, pp. 865-872, 1992 https://doi.org/10.1109/59.141797
  7. T. Kohonen, E. Oja, O. Simula, A. Visa, J. Kangas, Engineering application of the self-organizing map', Proc. IEEE, Vol. 84, No. 10, pp. 1358-1384, 1996 https://doi.org/10.1109/5.537105
  8. A. Graps, 'An Introduction to Wavelets,' IEEE Computational Science & Engineering, Vol. 2, No. 2, pp. 50-61, 1995 https://doi.org/10.1109/99.388960
  9. Gilbert Strang, 'Wavelets,' American Scientist, Vol. 82, pp. 250-255, 1994
  10. I. K. Yu, Y. H. Song, 'Wavelet Analysis and Neural Network based Adaptive Single-pole autoreclosure Scheme for EHV Transmission Systems,' International Journal of Electric Power and Energy Systems, Vol. 20, No. 7, pp. 465-474, 1998 https://doi.org/10.1016/S0142-0615(98)00015-5