• DocumentCode
    160390
  • Title

    Application of BP Neural Networks based on genetic simulated annealing algorithm for shortterm electricity price forecasting

  • Author

    Jun Chen ; Li He ; Yi Quan ; Wang Jiang

  • Author_Institution
    Sch. of Electr. & Electron. Eng., Hubei Univ. of Technol., Wuhan, China
  • fYear
    2014
  • fDate
    9-11 Jan. 2014
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    BP Neural Network can forecast short-term electricity price, while it is necessary to explore technique to tune the back propagation learning algorithm either for better generalization, or for faster training. The paper proposed enhanced BP Neural Network to forecast electricity price, in which we replaced back propagation algorithm of BP Network with genetic simulated annealing algorithm (GSAA). It integrated GA´s search performance and SA´s strong local search performance, and has a better performance in terms of solution accuracy and convergence speed. Finally, a case study of New South Wales in Australia illustrates the feasibility and effectiveness of the proposed method.
  • Keywords
    backpropagation; genetic algorithms; load forecasting; power engineering computing; pricing; simulated annealing; Australia; BP neural network; GSAA; New South Wales; back propagation learning algorithm; genetic simulated annealing algorithm; short-term electricity price forecasting; Electricity; Forecasting; Genetics; Neural networks; Predictive models; Simulated annealing; Sociology; BP Neural Network; Genetic simulated annealing algorithm (GSAA); Price forecasting; Weight adjustment;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Advances in Electrical Engineering (ICAEE), 2014 International Conference on
  • Conference_Location
    Vellore
  • Type

    conf

  • DOI
    10.1109/ICAEE.2014.6838562
  • Filename
    6838562