Innovations in Applied Artificial Intelligence

Volume 3029 of the series Lecture Notes in Computer Science pp 87-96

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

  • Luis E. ZárateAffiliated withApplied Computational Intelligence Laboratory (LICAP)
  • , Elizabeth Marques Duarte PereiraAffiliated withEnergy Researches Group (GREEN)
  • , João Paulo D. SilvaAffiliated withApplied Computational Intelligence Laboratory (LICAP)
  • , Renato VimeiroAffiliated withApplied Computational Intelligence Laboratory (LICAP)
  • , Antônia Sônia Cardoso DinizAffiliated withEnergy Company of Minas Gerais (CEMIG) Pontifical Catholic, University of Minas Gerais (PUC)

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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.