Modeling of solar energy systems using artificial neural network: A comprehensive review. (1st March 2019)

• Record Type: Journal Article
• Authors: Elsheikh, Ammar H.; Sharshir, Swellam W.; Abd Elaziz, Mohamed; Kabeel, A.E.; Guilan, Wang; Haiou, Zhang
• Is Part Of: Solar energy. Volume 180(2019)
• Page Start: 622
• Page End: 639
• Publication Date: 2019-03-01
• Subjects: Artificial neural networks -- Solar energy -- Solar collectors -- Solar assisted heat pumps -- Solar dryers -- Solar stills -- Photovoltaic/thermal systems; Solar energy -- Periodicals; Solar engines -- Periodicals; 621.47
• DOI: 10.1016/j.solener.2019.01.037
• Languages: English
• ISSNs: 0038-092X

Abstract

Abstract: 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.