Article: Modelling and drawing energy and exergy of solar radiation Journal: International Journal of Exergy (IJEX) 2016 Vol.19 No.4 pp.544 - 568 Abstract: Some models are proposed to study the exergy of incident solar radiation on the horizontal surface, which can be used to produce work. It is always important to know how much work can be obtained from solar radiation. In this study, an artificial neural network (ANN)-based model for prediction of solar energy was developed and compared with real data. The results show that the correlation coefficients between the predictions and actual global solar radiation intensities for training and testing datasets were higher than 97%, thus suggesting a high reliability of the model for evaluating solar radiation in locations where solar radiation data are not available. The results show that the total energy quality factor of extraterrestrial solar radiation by Petela expression is ~0.933. The predicted solar radiation energy and exergy values are illustrated in the form of maps that were made by ArcGIS. Inderscience Publishers - linking academia, business and industry through research

Title: Modelling and drawing energy and exergy of solar radiation

Authors: Saeed Edalati; Mehran Ameri; Masoud Iranmanesh; Hakimeh Tarmahi

Addresses: Department of Energy, Institute of Science and High Technology and Environmental Sciences, Graduate University of Advanced Technology, Kerman 76311-33131, Iran ' Faculty of Engineering, Department of Mechanical Engineering, Shahid Bahonar University of Kerman, Kerman 76189-18188, Iran ' Department of Energy, Institute of Science and High Technology and Environmental Sciences, Graduate University of Advanced Technology, Kerman 76311-33131, Iran ' Faculty of Engineering, Department of Mechanical Engineering, Shahid Bahonar University of Kerman, Kerman 76189-18188, Iran

Abstract: Some models are proposed to study the exergy of incident solar radiation on the horizontal surface, which can be used to produce work. It is always important to know how much work can be obtained from solar radiation. In this study, an artificial neural network (ANN)-based model for prediction of solar energy was developed and compared with real data. The results show that the correlation coefficients between the predictions and actual global solar radiation intensities for training and testing datasets were higher than 97%, thus suggesting a high reliability of the model for evaluating solar radiation in locations where solar radiation data are not available. The results show that the total energy quality factor of extraterrestrial solar radiation by Petela expression is ~0.933. The predicted solar radiation energy and exergy values are illustrated in the form of maps that were made by ArcGIS.

Keywords: solar radiation; energy values; exergy values; artificial neural networks; ANNs; ArcGIS; modelling; solar energy; solar power.

DOI: 10.1504/IJEX.2016.075890

International Journal of Exergy, 2016 Vol.19 No.4, pp.544 - 568

Received: 03 Sep 2014
Accepted: 12 May 2015
Published online: 12 Apr 2016 *

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Title: Modelling and drawing energy and exergy of solar radiation

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