• DocumentCode
    3108259
  • Title

    Study of Power System Short-term Load Forecast Based on Artificial Neural Network and Genetic Algorithm

  • Author

    Du Xin-hui ; Feng, Tian ; Shao-Qiong, Tan

  • Author_Institution
    Coll. of Electr. & Power Eng., Taiyuan Univ. of Technol., Taiyuan, China
  • fYear
    2010
  • fDate
    26-28 Sept. 2010
  • Firstpage
    725
  • Lastpage
    728
  • Abstract
    The correct schedule, planning and operation of power system has a tight correlation between accurate load forecast. Aims at the variant feature of power system short-term load, the author took a widely study and discussion on the method of artificial neural network applied on power system short-term load forecasting. At the base of three layered BP neural network, the author studied the meteorological factor effect on short-term load forecasting precision, present the short-term load forecasting model made of BP neural network combine with genetic algorithm. According to the load data of area grid and relevant meteorological data, the author forecasted the short-term load with method of three layered BP neural network, four layered BP neural network and four layered BP neural network combine with genetic algorithm. The result shows that four layered BP neural network combine with genetic algorithm have the advantage of fast calculating time and high precision, have value for engineering application and significance for popularization.
  • Keywords
    backpropagation; genetic algorithms; load forecasting; power engineering computing; power system planning; BP neural network; artificial neural network; genetic algorithm; meteorological factor effect; power system operation; power system planning; power system scheduling; power system short-term load forecasting; Artificial neural networks; Biological cells; Load forecasting; Neurons; Weather forecasting; Artificial neural network; BP algorithm; Genetic algorithm; Short-term load forecasting;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Aspects of Social Networks (CASoN), 2010 International Conference on
  • Conference_Location
    Taiyuan
  • Print_ISBN
    978-1-4244-8785-1
  • Type

    conf

  • DOI
    10.1109/CASoN.2010.166
  • Filename
    5636945