• DocumentCode
    2962506
  • Title

    A hybrid intelligent system clonart for short and mid-term forecasting for the Brazilian Energy Distribution System

  • Author

    Alexandrino, José Lima ; Zanchettin, Cleber ; Filho, Edson Costa de Barros Carvalho

  • Author_Institution
    Centro de Inf., Fed. Univ. of Pernambuco, Recife
  • fYear
    2008
  • fDate
    1-8 June 2008
  • Firstpage
    3486
  • Lastpage
    3492
  • Abstract
    The present work describes an application of Clonart (Clonal Adaptive Resonance Theory) for forecasting of amount of precipitation for the Brazilian Energy Distribution System. The effectiveness of the Brazilian electricity system directly depends on the difference between hydroelectric energy production and consumer use. Production depends upon the volume of water stored in the reservoirs. A forecasting system for the amount of rainfall throughout the year contributes significantly to the analysis. The plasticity of the Clonart ensures that a new piece of knowledge does not overshadow previous knowledge. This is especially important for forecast problems because this type of problem needs constants training.
  • Keywords
    ART neural nets; distribution networks; hydroelectric power; load forecasting; power engineering computing; Brazilian electricity system; Brazilian energy distribution system; clonal adaptive resonance theory; consumer use; hybrid intelligent system Clonart; hydroelectric energy production; mid-term forecasting; short-term forecasting; water reservoir; Autoregressive processes; Energy consumption; Hybrid intelligent systems; Load forecasting; Production systems; Reservoirs; Resonance; Silicon compounds; Water resources; Water storage;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on
  • Conference_Location
    Hong Kong
  • ISSN
    1098-7576
  • Print_ISBN
    978-1-4244-1820-6
  • Electronic_ISBN
    1098-7576
  • Type

    conf

  • DOI
    10.1109/IJCNN.2008.4634295
  • Filename
    4634295