Neural networks for inflow forecasting using precipitation information

  • Authors:

  • Karla Figueiredo;Carlos R. Hall Barbosa;André V. A. Da Cruz;Marley Vellasco;Marco Aurélio C. Pacheco;Roxana J. Conteras

  • Affiliations:

  • Electrical and Telecommunications Engineering Department, UERJ, Rio de Janeiro, RJ, Brazil;Applied Computational Intelligence Laboratory, Pontifícia Universidade Católica of Rio de Janeiro, Rio de Janeiro, RJ, Brazil;Applied Computational Intelligence Laboratory, Department of Electrical Engineering, Pontifícia Universidade Católica of Rio de Janeiro, Rio de Janeiro, RJ, Brazil;Applied Computational Intelligence Laboratory, Department of Electrical Engineering, Pontifícia Universidade Católica of Rio de Janeiro, Rio de Janeiro, RJ, Brazil;Applied Computational Intelligence Laboratory, Department of Electrical Engineering, Pontifícia Universidade Católica of Rio de Janeiro, Rio de Janeiro, RJ, Brazil;Applied Computational Intelligence Laboratory, Department of Electrical Engineering, Pontifícia Universidade Católica of Rio de Janeiro, Rio de Janeiro, RJ, Brazil

  • Venue:
  • IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems
  • Year:
  • 2007

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Abstract

This work presents forecast models for the natural inflow in the Basin of Iguaçu River, incorporating rainfall information, based on artificial neural networks. Two types of rainfall data are available: measurements taken from stations distributed along the basin and ten-day rainfall forecasts using the ETA model developed by CPTEC (Brazilian Weather Forecating Center). The neural nework model also employs observed inflows measured by stations along the Iguaçu River, as well as historical data of the natural inflows to be predicted. Initially, we applied preprocessing methods on the various series, filling missing data and correcting outliers. This was followed by methods for selecting the most relevant variables for the forecast model. The results obtained demonstrate the potential of using artificial neural networks in this problem, which is highly non-linear and very complex, providing forecasts with good accuracy that can be used in planning the hydroelectrical operation of the Basin.