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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References
Morrison, G.L., Ranatunga, D.B.J.: Transient response of thermosiphon solar collectors. Solar Energy 24, 191 (1980)
Huang, B.J.: Similarity theory of solar water heater with natural circulation. Solar Energy 25, 105 (1984)
Kudish, A.I., Santaura, P., Beaufort, P.: Direct measurement and analysis of thermosiphon flow. Solar Energy 35(2), 167–173
Kalogirou, S.A.: Thermosiphon solar domestic water heating systems: long term performance prediction using ANN. Solar Energy 69(2), 167–174 (2000)
Kalogirou, S.A., Panteliou, S., Dentsoras, A.: Modeling solar domestic water heating systems using ANN. Solar Energy 68(6), 335–342 (1999)
Duffie, J.A., Beckman, W.A.: Solar Engineering of Thermal Processes, 2a edn. John Wiley & Sons, Inc., USA (1999)
Kovács, Z.L.: Redes Neurais Artificiais. Edição acadêmica São Paulo, Cap 5, São Paulo. Brasil, pp. 75–76 (1996)
Bittencout, F.R., Zárate, L.E.: Controle da Laminação em Redes Neurais, com Capacidade de Generalização, e Lógica Nebulosa via Fatores de Sensibilidade, CBA, Natal, RN (2002)
Ashrae 93-86 Ra 91. Methods of Testing to Determine the Thermal Performance of Solar Collectors, American Society of Heating, Refrigeration, and Air-Conditioning Engineers, Inc., Atlanta (1986)
Zárate, L.E., Pereira, E.M., Silva, J.P., Vimieiro, R., Diniz, A.S., Pires, S.: Representation of a solar collector via artificial neural networks. In: Hamza, M.H. (ed.) International Conference On Artificial Intelligence And Applications, Benalmádena, Spain, September 8-11, pp. 517–522. ACTA Press, IASTED (2003a)
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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. https://doi.org/10.1007/978-3-540-24677-0_10
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DOI: https://doi.org/10.1007/978-3-540-24677-0_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22007-7
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