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Applications of extension grey prediction model for power system forecasting

Author

Listed:
  • Wei Niu

    (Northwestern Polytechnical University)

  • Juan Cheng

    (CNGC the 205 Institute)

  • Guoqing Wang

    (Northwestern Polytechnical University
    Chinese Aeronautical Radio Electronics Research Institute)

Abstract

Although the grey forecasting model has been successfully adopted in various fields and demonstrated promising results, the literatures show its performance could be further improved. For this purpose, this paper proves that the growth rate of the simulated value of the grey model GM(1,1) is a fixed value. If the growth rates of the primary sequence are equate, the fitted value deriving from GM(1,1) is the same as the primary sequence, otherwise greater error would occur. In order to overcome shortcoming of the fixed growth rates, extend the traditional GM(1,1) model by introducing linear time-varying terms, which can predict more accurately on non geometric sequences, termed EGM(1,1). Finally, compared with the other improved grey model and ARIMA model, experimental results indicate that the proposed model obviously can improve the prediction accuracy.

Suggested Citation

  • Wei Niu & Juan Cheng & Guoqing Wang, 2013. "Applications of extension grey prediction model for power system forecasting," Journal of Combinatorial Optimization, Springer, vol. 26(3), pages 555-567, October.
    • Handle: RePEc:spr:jcomop:v:26:y:2013:i:3:d:10.1007_s10878-012-9477-8
    DOI: 10.1007/s10878-012-9477-8
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    Cited by:

    1. Zhiguo Wang & Lufei Huang & Cici Xiao He, 2021. "A multi-objective and multi-period optimization model for urban healthcare waste’s reverse logistics network design," Journal of Combinatorial Optimization, Springer, vol. 42(4), pages 785-812, November.
    2. Hao Hao & Ji Zhang & Qian Zhang & Li Yao & Yichen Sun, 2021. "Improved gray neural network model for healthcare waste recycling forecasting," Journal of Combinatorial Optimization, Springer, vol. 42(4), pages 813-830, November.
    3. Zhiguo Wang & Lufei Huang & Cici Xiao He, 0. "A multi-objective and multi-period optimization model for urban healthcare waste’s reverse logistics network design," Journal of Combinatorial Optimization, Springer, vol. 0, pages 1-28.
    4. Hao Hao & Ji Zhang & Qian Zhang & Li Yao & Yichen Sun, 0. "Improved gray neural network model for healthcare waste recycling forecasting," Journal of Combinatorial Optimization, Springer, vol. 0, pages 1-18.

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