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

Abstract

In the present study, the aim is to estimate the annual exergy yield of a hybrid system consisting of a photovoltaic/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 performance is the optimal ANN configuration. Based on the results, 74% of the investigated pairs have an error smaller than 0.05 %.