Predicting the performance of thermal, electrical and overall efficiencies of a nanofluid-based photovoltaic/thermal system using Elman recurrent neural network methodology

Abstract

In this research, the Elman recurrent neural network methodology is used to develop a relationship to predict the performance of a photovoltaic/thermal device with sheet-and-serpentine tube collector. In this system, PV panels are cooled using the water-magnetite nanofluid. The thermal (ηTH), electrical (ηEL) and overall (ηOV) efficiencies of the PV/T device are utilized as objective functions, and the mass flow rate (m˙) and concentration (φ) of nanofluid are considered as input parameters. The range considered for the m˙ and φ is 20-80 kg/hr and 0-2% respectively. To measure the accuracy of the models developed, the mean absolute percentage error (MAPE), root mean square error (RMSE), and correlation coefficient (R) were used. It was found that the best accuracy is obtained for the ηTH in terms of (Training: R=0.9978 and RMSE=0.2045; Testing: R=0.9973 and RMSE=0.1973) followed by ηOV in terms of (Testing: R=0.9959 and RMSE=0.1797), and ηEL in terms of (Testing: R=0.9825 and RMSE=0.0037), respectively.