• DocumentCode
    2844665
  • Title

    Artificial neural networks applied to long-term electricity demand forecasting

  • Author

    Mamun, M.A. ; Nagasaka, Ken

  • Author_Institution
    Dept. of Electr; & Electron. Eng., Tokyo Univ. of Agric. & Technol., Japan
  • fYear
    2004
  • fDate
    5-8 Dec. 2004
  • Firstpage
    204
  • Lastpage
    209
  • Abstract
    The electric power demand in Japan has steadily increased and the load factor of total power system has decreased. It is therefore very important to the utilities to have advance knowledge of their electrical load. One of the important points for forecasting the long-term load in Japan is to take into account the past and present economic situations and power demand. These points were considered in this study. The proposed artificial neural network (ANN) that is radial basis function network (RBFN) has also showed that the changes in loads are a reflection of economy. Here, prediction of peak loads in Japan up to year 2015 is discussed using the RBFN and the maximum demands for 2001 through 2015 are predicted to be elevated from 179.42 GW to 209.18 GW. The annual average rate of load growth seen per ten years until 2015 is about 1.39%.
  • Keywords
    electric current; load forecasting; power engineering computing; power system economics; radial basis function networks; total energy systems; RBFN; artificial neural networks; economic factors; electric load factor; electric power demand; long-term electricity demand forecasting; radial basis function network; total power system; Artificial neural networks; Demand forecasting; Economic forecasting; Load forecasting; Power demand; Power generation economics; Power system economics; Power systems; Radial basis function networks; Reflection; Economic Factors; Electric Load Demand; Long-term Load Fore-casting; Radial Basis;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Hybrid Intelligent Systems, 2004. HIS '04. Fourth International Conference on
  • Print_ISBN
    0-7695-2291-2
  • Type

    conf

  • DOI
    10.1109/ICHIS.2004.27
  • Filename
    1410005