• DocumentCode
    2224764
  • Title

    Adaptive power signal prediction by non-fixed neural network model with modified fuzzy back-propagation learning algorithm

  • Author

    Hwang, Rey-Chue ; Huang, Huang-Chu ; Chen, Yu-Ju ; Jer-Guang Hsich ; Hsing Chao

  • Author_Institution
    Dept. of Electr. Eng., Kaohsiung Polytech. Inst., Taiwan
  • fYear
    1997
  • fDate
    9-12 Sep 1997
  • Firstpage
    689
  • Abstract
    The authors present a nonfixed artificial neural network (ANN) model with a modified fuzzy back-propagation (BP) learning rule for power signal prediction. This model is designed to avoid the ill-learning of ANN training caused by improper information. Taipower 1990-1993 loads and relevant weather data are implemented. The experiments of next day peak load forecasting and one-day-ahead hourly load forecasting are made in this study. Some experiments using conventional BP ANN approach are also performed as a comparison with the proposed model
  • Keywords
    backpropagation; fuzzy neural nets; load forecasting; power system analysis computing; Taipower; adaptive power signal prediction; modified fuzzy backpropagation learning rule; next day peak load forecasting; nonfixed artificial neural network; one-day-ahead hourly load forecasting; Artificial neural networks; Fuzzy logic; Fuzzy neural networks; Load forecasting; Neural networks; Power system modeling; Power system planning; Predictive models; Temperature; Testing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information, Communications and Signal Processing, 1997. ICICS., Proceedings of 1997 International Conference on
  • Print_ISBN
    0-7803-3676-3
  • Type

    conf

  • DOI
    10.1109/ICICS.1997.652065
  • Filename
    652065