Comparison of Neural Network Models in the Estimation of the Performance of Solar Collectors

Three artificial neural network models [feedforward, Elman, and nonlinear autoregressive exogenous (NARX)] were used to find the performance of two flat-plate collectors operating under Jordanian climate. One collector used water as a working fluid, while the other used fuel oil as a working fluid. Previously obtained experimental data on the performance of solar collectors were used to train the neural network. Density of fluid, input temperature, output temperature, ambient temperature, and solar radiation were used as input parameters in the input layer of the network while the efficiency of the flat-plate solar collector was in the output layer. It was found that the artificial neural network technique may be used to estimate the efficiency of the flat-plate collector with excellent accuracy. The obtained results showed that the multilayer feedforward model with five inputs has the best ability to estimate the required performance, while the other models, the feedforward, NARX, and Elman networks, have the lowest ability to estimate it. Furthermore, using the sensitivity analysis, it was found that the NARX and Elman models have the least ability to estimate the solar collector’s thermal efficiency, while the feedforward model with the input parameters of density, $T_{\text{out}}$, $T_{\text{in}}$, $T_{\text{amb}}$, and $G_t$, has the best performance in both training and validation period.