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

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.