A Hierarchical Self-Organizing Map Model in Short-Term Load Forecasting

  • Authors:

  • Otá/vio A. S. Carpinteiro;Alexandre P. Alves Da Silva

  • Affiliations:

  • Instituto de Engenharia Elé/trica, Escola Federal de Engenharia de Itajubá/, Av. BPS 1303, Itajubá/, MG, 37500-903, Brazil/ e-mail: otavio@iee.efei.br;Instituto de Engenharia Elé/trica, Escola Federal de Engenharia de Itajubá/, Av. BPS 1303, Itajubá/, MG, 37500-903, Brazil/ e-mail: alex@iee.efei.br

  • Venue:
  • Journal of Intelligent and Robotic Systems
  • Year:
  • 2001

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Abstract

This paper proposes a novel neural model to the problem of short-term load forecasting. The neural model is made up of two self-organizing map nets – one on top of the other. It has been successfully applied to domains in which the context information given by former events plays a primary role. The model was trained and assessed on load data extracted from a Brazilian electric utility. It was required to predict once every hour the electric load during the next 24 hours. The paper presents the results, and evaluates them.