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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)

Abstract

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.

Keywords

Input Layer Neural Model Load Forecast Time Series Forecast Linear Activation Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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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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