Neural Representation of a Solar Collector with Statistical Optimization of the Training Set

  • Luis E. Zárate
  • Elizabeth Marques Duarte Pereira
  • João Paulo D. Silva
  • Renato Vimeiro
  • Antônia Sônia Cardoso Diniz
Conference paper

DOI: 10.1007/978-3-540-24677-0_10

Part of the Lecture Notes in Computer Science book series (LNCS, volume 3029)
Cite this paper as:
Zárate L.E., Pereira E.M.D., Silva J.P.D., Vimeiro R., Diniz A.S.C. (2004) Neural Representation of a Solar Collector with Statistical Optimization of the Training Set. In: Orchard B., Yang C., Ali M. (eds) Innovations in Applied Artificial Intelligence. IEA/AIE 2004. Lecture Notes in Computer Science, vol 3029. Springer, Berlin, Heidelberg

Abstract

Alternative ways of energy producing are essential in a reality where natural resources have been scarce and solar collectors are one of these ways. However the mathematical modeling of solar collectors involves parameters that may lead to nonlinear equations. Due to their facility of solving nonlinear problems, ANN (i.e. Artificial Neural Networks) are presented here, as an alternative to represent these solar collectors with several advantages on other techniques of modeling, like linear regression. Techniques for selecting representative training sets are also discussed and presented in this paper.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Luis E. Zárate
    • 1
  • Elizabeth Marques Duarte Pereira
    • 2
  • João Paulo D. Silva
    • 1
  • Renato Vimeiro
    • 1
  • Antônia Sônia Cardoso Diniz
    • 3
  1. 1.Applied Computational Intelligence Laboratory (LICAP) 
  2. 2.Energy Researches Group (GREEN) 
  3. 3.Energy Company of Minas Gerais (CEMIG) Pontifical CatholicUniversity of Minas Gerais (PUC)Belo HorizonteBrazil

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