Representation of a Solar Collector via Artificial Neural Networks

L.E. Zárate, E.M. Duarte Pereira, J.P. Silva, R. Vimieiro, S. Pires, and A.S. Cardoso Diniz (Brazil)


Artificial Neural Networks, Computational Intelligence, Thermosiphon, Solar Energy.


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 of the process. Due to their facility of solving nonlinear problems, Artificial Neural Networks are presented here, as an alternative to represent these solar collectors with several advantages on other techniques of this kind of modeling as linear regression.

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