NDLI: Parametric Analysis of Solar Collectors Through Sensitivity Factors Via Artificial Neural Networks

Content Provider	IEEE Xplore Digital Library
Author	Zarate, L.E. Pereira, E.M.D.
Copyright Year	2006
Description	Author affiliation: Pontifical Catholic Univ. of Minas Gerais, Minas Gerais (Zarate, L.E.)
Abstract	Since solar collectors have been presented as an alternative way of energy producing, many researches have been working with these systems. The solar collectors are greatly influenced by the operation parameters: ambient temperature (Tamb), input water temperature (Tin), solar irradiance (G) and mainly by manufacture process. Those parameters are important in order to know the quality and the efficiency of a specific solar collector. The efficiency of those systems can be influenced by manufacture process and this condition is not considered in mathematical models of collectors. On other hand, due its facility in solving nonlinear problems, experimental data based, in this paper, artificial neural networks (ANN) have been proposed as alternative to represent and to compare solar collectors. In the classification of solar collectors, is important to know how Tamb, Tin, G influence the output water temperature (Tout) (strongly associated to the system efficiency) for each collector considered. These influences may be obtained through the sensitivity analysis of the parameters in relation to Tout. So, through differentiation of a previously trained net, the sensitivity factors can be obtained. The sensitivity factors show how much the input variables influence the output variables. In this paper, the sensitivity analysis via ANN, to compare and classify solar collectors is applied and discussed.
Starting Page	2742
Ending Page	2748
File Size	252717
Page Count	7
File Format	PDF
ISBN	0780394909
DOI	10.1109/IJCNN.2006.247179
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-07-16
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Artificial neural networks Temperature sensors Tin Manufacturing processes Sensitivity analysis Water heating Mathematical model Solar energy Solar heating Virtual manufacturing Thermo-siphon system Artificial Neural Networks Sensitivity Analysis Solar Energy Solar Collector
Content Type	Text
Resource Type	Article

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