A Hybrid Neural Model in Long-Term Electrical Load Forecasting

  • Otávio A. S. Carpinteiro
  • Isaías Lima
  • Rafael C. Leme
  • Antonio C. Zambroni de Souza
  • Edmilson M. Moreira
  • Carlos A. M. Pinheiro
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4132)


A novel hierarchical hybrid neural model to the problem of long-term electrical load forecasting is proposed in this paper. The neural model is made up of two self-organizing map nets — one on top of the other —, and a single-layer perceptron. It has application into domains which require time series analysis. The model is compared to a multilayer perceptron. Both the hierarchical and the multilayer perceptron models are endowed with time windows in their input layers. They are trained and assessed on load data extracted from a North-American electric utility. The models are required to predict once every week the electric peak-load and mean-load during the next two years. The results are presented and evaluated in the paper.


Input Layer Neural Model Load Forecast Time Series Forecast Linear Activation Function 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Otávio A. S. Carpinteiro
    • 1
  • Isaías Lima
    • 1
  • Rafael C. Leme
    • 1
  • Antonio C. Zambroni de Souza
    • 1
  • Edmilson M. Moreira
    • 1
  • Carlos A. M. Pinheiro
    • 1
  1. 1.Research Group on Computer Networks and Software EngineeringFederal University of ItajubáItajubáBrazil

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