A hierarchical hybrid neural model with time integrators in long-term load forecasting

  • Otávio A. S. Carpinteiro
  • Isaías Lima
  • Edmilson M. Moreira
  • Carlos A. M. Pinheiro
  • Enzo Seraphim
  • J. Vantuil L. Pinto
Original Article

Abstract

A novel hierarchical hybrid neural model to the problem of long-term 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 mutilated architecture of it, and to a multilayer perceptron. The hierarchical, the mutilated, and the multilayer perceptron models are trained and assessed on load data extracted from a North-American electric utility. They are required to predict either once every week or once every month the electric peak-load during the next two years. The results from the experiments show that the performance of HHNM on long-term load forecasts is better than that of the mutilated model, and much better than that of the MLP model.

Keywords

Neural networks Time-series forecasting Long-term electrical load forecasting 

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Copyright information

© Springer-Verlag London Limited 2009

Authors and Affiliations

  • Otávio A. S. Carpinteiro
    • 1
  • Isaías Lima
    • 1
  • Edmilson M. Moreira
    • 1
  • Carlos A. M. Pinheiro
    • 1
  • Enzo Seraphim
    • 1
  • J. Vantuil L. Pinto
    • 1
  1. 1.Research Group on Systems and Computer EngineeringFederal University of ItajubáItajubáBrazil

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