Sensitivity Analysis Obtained Through Artificial Neural Networks – Application in Solar Energy Systems

L.E. Zárate, E.M.D. Pereira, R. Vimieiro, D. Alencar Soares, and A.S.C. Diniz (Brazil)


Neural Networks, Sensitivity Analysis, Solar Energy


Since solar collectors have been presented as an alterna tive way of energy producing, many researches have been working with these systems. Due its facility in solving non linear problems, Artificial Neural Networks(ANN) have been proposed, as a powerful tool, to represent solar energy systems, and specially solar collectors. Solar Energy sys tems are greatly influenced by the operation parameters ambient temperature (Tamb), input water temperature (Tin) and solar irradiance (G) and by devices installation pa rameters 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 Tamb, Tin, G influence the output water temperature (Tout)(strongly associated to the system efficiency). These influence 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 of the main parameters of so lar 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.

Important Links:

Go Back