Comparative Analyses of Computational Intelligence Models for Load Forecasting: A Case Study in the Brazilian Amazon Power Suppliers

  • Authors:

  • Liviane P. Rego;Ádamo L. Santana;Guilherme Conde;Marcelino S. Silva;Carlos R. Francês;Cláudio A. Rocha

  • Affiliations:

  • Laboratory of High Performance Networks Planning, Federal University of Pará-R. Augusto Correa, Belém, Brazil 66075-110;Laboratory of High Performance Networks Planning, Federal University of Pará-R. Augusto Correa, Belém, Brazil 66075-110;Laboratory of High Performance Networks Planning, Federal University of Pará-R. Augusto Correa, Belém, Brazil 66075-110;Laboratory of High Performance Networks Planning, Federal University of Pará-R. Augusto Correa, Belém, Brazil 66075-110;Laboratory of High Performance Networks Planning, Federal University of Pará-R. Augusto Correa, Belém, Brazil 66075-110;Laboratory of High Performance Networks Planning, Federal University of Pará-R. Augusto Correa, Belém, Brazil 66075-110

  • Venue:
  • ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
  • Year:
  • 2009

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Abstract

One of the most desired aspects for power suppliers is the acquisition/sale of energy for a future demand. However, power consumption forecast is characterized not only by the variable of the power system itself, but also related to socio-economic and climatic factors. Hence, it is imperative for the power suppliers to design and correlate these parameters. This paper presents a study of power load forecast for power suppliers, comparing application of techniques of wavelets, time series analysis methods and neural networks, considering long term forecasts; thus defining the future power consumption of a given region. The results obtained proved to be much more effective when compared to those projected by the power suppliers based on specialist information, thus contributing to the decision making for acquisition/sale of energy at a future demand.