Modeling of solar energy systems using artificial neural network: A comprehensive review

• Published in: Solar energy Vol. 180; pp. 622 - 639
• Main Authors: Elsheikh, Ammar H., Sharshir, Swellam W., Abd Elaziz, Mohamed, Kabeel, A.E., Guilan, Wang, Haiou, Zhang
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 01-03-2019; Pergamon Press Inc
• Subjects: Artificial neural networks; Basis functions; Cookers; Driers; Energy demand; Heat exchangers; Heat pumps; Mathematical models; Modelling; Multilayer perceptrons; Neural networks; Optimization; Photovoltaic cells; Photovoltaic/thermal systems; Photovoltaics; Radial basis function; Solar assisted heat pumps; Solar cells; Solar collectors; Solar cookers; Solar dryers; Solar energy; Solar stills; Water heaters; Wavelet transforms; Solar assisted heat pumps; Solar stills; Solar dryers; Artificial neural networks; Solar energy; Photovoltaic/thermal systems; Solar collectors
• ISSN: 0038-092X; 1471-1257
• DOI: 10.1016/j.solener.2019.01.037

The development of different solar energy (SE) systems becomes one of the most important solutions to the problem of the rapid increase in energy demand. This may be achieved by optimizing the performance of solar-based devices under some operating conditions. Intelligent system-based techniques are used to optimize the performance of such systems. In present review, an attempt has been made to scrutinize the applications of artificial neural network (ANN) as an intelligent system-based method for optimizing and the prediction of different SE devices’ performance, like solar collectors, solar assisted heat pumps, solar air and water heaters, photovoltaic/thermal (PV/T) systems, solar stills, solar cookers, and solar dryers. The commonly used artificial neural network types and architectures in literature, such as multilayer perceptron neural network, a neural network using wavelet transform, Elman neural network, and radial basis function, are also briefly discussed. Different statistical criteria that used to assess the performance of artificial neural network in modeling SE systems have been introduced. Previous studies have reported that artificial neural network is a useful technique to predict and optimize the performance of different solar energy devices. Important conclusions and suggestions for future research are also presented.