Sensitivity analysis obtained through artificial neural networks application in solar energy systems

Since solar collectors have been presented as an alternative way of energy producing, many researches have been working with these systems. Due its facility in solving nonlinear problems, Artificial Neural Networks(ANN) have been proposed, as a powerful tool, to represent solar energy systems, and specially solar collectors. Solar Energy systems are greatly influenced by the operation parameters ambient temperature (T-amb), input water temperature (T-in) and solar irradiance (G)- and by devices installation parameters like tank height, solar collector inclination, and others. The operation parameters are important in order to know the different efficiency values of solar collector. Then it is important to know how T-amb, T-in, G influence the output water temperature (T-amb)(strongly associated to the system efficiency). These influence may be obtained through the sensitivity analysis of the parameters in relation to T-out. So,through differentiation of a previously trained net, the sensitivity factors of the main parameters of solar collectors is calculated and discussed. The sensitivity factors show how much the input variables influence the output variables. In this paper, the sensitivity analysis for solar collectors main parameters is applied and discussed.