Prediction of the exergy performance of a hybrid photovoltaic/thermal-thermal wheel system using an optimal artificial neural network

In the present study, the aim is to estimate the annual exergy yield of a hybrid system consisting of a photo-voltaic/thermal (PV/T) system and a thermal wheel. By using the hybrid system, the outside air can be cooled or heated and also, part of the electricity required by a building can be provided. The input parameters of the predictive model are the dimensions of the channel of PV/T system, the air mass flow rate and the thermal wheel length and diameter. For modeling the exergy of the studied hybrid system, a systematic method is proposed to find the optimal configuration of Artificial Neural Network (ANN). For this purpose, a novel method is proposed to discover the optimal architecture and training algorithm of the ANN by the Genetic Algorithm (GA) among 20,160 possible combinations. A dataset with 2000 input-target pairs is investigated using this method. The results indicate that a Feed-Forward (FF) network containing 19 and 10"tansig" neurons in the first and second hidden layers that is trained by Bayesian regularization backpropagation (trainbr) can reach the best perfor-mance is the optimal ANN configuration. Based on the results, 74% of the investigated pairs have an error smaller than 0.05 %.