ORCID Profile
0000-0003-0024-4497
Current Organisations
Iran University of Science and Technology
,
University of Adelaide
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Publisher: Elsevier BV
Date: 08-2015
Publisher: Elsevier BV
Date: 08-2016
Publisher: Springer Science and Business Media LLC
Date: 03-07-2020
Publisher: Elsevier BV
Date: 11-2016
Publisher: Springer Science and Business Media LLC
Date: 02-09-2020
Publisher: Elsevier BV
Date: 2021
Publisher: Elsevier BV
Date: 05-2016
Publisher: Elsevier BV
Date: 06-2016
Publisher: Elsevier BV
Date: 05-2017
Publisher: Elsevier BV
Date: 2017
Publisher: Springer Science and Business Media LLC
Date: 03-05-2021
Publisher: Springer Science and Business Media LLC
Date: 18-11-2023
DOI: 10.1007/S11356-022-24044-Y
Abstract: Over the past few decades, the popularity of solar thermal collectors has increased dramatically because of many significant advantages like being a free, natural, environmentally friendly and permanent energy source. Today, developing and optimising different solar thermal energy systems are more important than before. Thus, there are various methods for investigating the performance of these systems, such as experimental, numerical and mathematical methods. One of the cutting-edge methods is artificial intelligence, which can predict key and effective parameters in solar collector efficiency. This review identified recent machine learning modelling, including multilayer perceptron artificial neural network (MLP-ANN), group method of data handling (GMDH), radial basis function (RBF), artificial neuro-fuzzy inference system (ANFIS), support vector machine (SVM) and studies regarding different types of solar thermal collectors, namely non-concentration and concentration. Furthermore, it investigated the effect of various essential factors on the accuracy, potential issues and challenges facing the application of artificial intelligence in these systems. Finally, it will also be recommended opportunities for future research.
Publisher: Elsevier BV
Date: 03-2017
Publisher: Springer Science and Business Media LLC
Date: 27-02-2017
Publisher: Elsevier BV
Date: 08-2016
Location: Iran (Islamic Republic of)
No related grants have been discovered for Masoud Vakili.