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Temperature-based estimation of global solar radiation using soft computing methodologies

Precise knowledge of solar radiation is indeed essential in different technological and scientific applications of solar energy. Temperature-based estimation of global solar radiation would be appealing owing to broad availability of measured air temperatures. In this study, the potentials of soft c...

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Main Authors:	Mohammadi, K., Shamshirband, S., Danesh, A. S., Abdullah, M. S., Zamani, M.
Format:	Article
Published:	Springer-Verlag Wien 2016
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://eprints.utm.my/id/eprint/71599/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-84929120395&doi=10.1007%2fs00704-015-1487-x&partnerID=40&md5=e8f2bfc95fdf9c627b46ba08a1963c13
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spelling	my.utm.715992017-11-20T08:28:24Z http://eprints.utm.my/id/eprint/71599/ Temperature-based estimation of global solar radiation using soft computing methodologies Mohammadi, K. Shamshirband, S. Danesh, A. S. Abdullah, M. S. Zamani, M. QA75 Electronic computers. Computer science Precise knowledge of solar radiation is indeed essential in different technological and scientific applications of solar energy. Temperature-based estimation of global solar radiation would be appealing owing to broad availability of measured air temperatures. In this study, the potentials of soft computing techniques are evaluated to estimate daily horizontal global solar radiation (DHGSR) from measured maximum, minimum, and average air temperatures (Tmax, Tmin, and Tavg) in an Iranian city. For this purpose, a comparative evaluation between three methodologies of adaptive neuro-fuzzy inference system (ANFIS), radial basis function support vector regression (SVR-rbf), and polynomial basis function support vector regression (SVR-poly) is performed. Five combinations of Tmax, Tmin, and Tavg are served as inputs to develop ANFIS, SVR-rbf, and SVR-poly models. The attained results show that all ANFIS, SVR-rbf, and SVR-poly models provide favorable accuracy. Based upon all techniques, the higher accuracies are achieved by models (5) using Tmax–Tmin and Tmax as inputs. According to the statistical results, SVR-rbf outperforms SVR-poly and ANFIS. For SVR-rbf (5), the mean absolute bias error, root mean square error, and correlation coefficient are 1.1931 MJ/m2, 2.0716 MJ/m2, and 0.9380, respectively. The survey results approve that SVR-rbf can be used efficiently to estimate DHGSR from air temperatures. Springer-Verlag Wien 2016 Article PeerReviewed Mohammadi, K. and Shamshirband, S. and Danesh, A. S. and Abdullah, M. S. and Zamani, M. (2016) Temperature-based estimation of global solar radiation using soft computing methodologies. Theoretical and Applied Climatology, 125 (1-2). pp. 101-112. ISSN 0177-798X https://www.scopus.com/inward/record.uri?eid=2-s2.0-84929120395&doi=10.1007%2fs00704-015-1487-x&partnerID=40&md5=e8f2bfc95fdf9c627b46ba08a1963c13
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
topic	QA75 Electronic computers. Computer science
spellingShingle	QA75 Electronic computers. Computer science Mohammadi, K. Shamshirband, S. Danesh, A. S. Abdullah, M. S. Zamani, M. Temperature-based estimation of global solar radiation using soft computing methodologies
description	Precise knowledge of solar radiation is indeed essential in different technological and scientific applications of solar energy. Temperature-based estimation of global solar radiation would be appealing owing to broad availability of measured air temperatures. In this study, the potentials of soft computing techniques are evaluated to estimate daily horizontal global solar radiation (DHGSR) from measured maximum, minimum, and average air temperatures (Tmax, Tmin, and Tavg) in an Iranian city. For this purpose, a comparative evaluation between three methodologies of adaptive neuro-fuzzy inference system (ANFIS), radial basis function support vector regression (SVR-rbf), and polynomial basis function support vector regression (SVR-poly) is performed. Five combinations of Tmax, Tmin, and Tavg are served as inputs to develop ANFIS, SVR-rbf, and SVR-poly models. The attained results show that all ANFIS, SVR-rbf, and SVR-poly models provide favorable accuracy. Based upon all techniques, the higher accuracies are achieved by models (5) using Tmax–Tmin and Tmax as inputs. According to the statistical results, SVR-rbf outperforms SVR-poly and ANFIS. For SVR-rbf (5), the mean absolute bias error, root mean square error, and correlation coefficient are 1.1931 MJ/m2, 2.0716 MJ/m2, and 0.9380, respectively. The survey results approve that SVR-rbf can be used efficiently to estimate DHGSR from air temperatures.
format	Article
author	Mohammadi, K. Shamshirband, S. Danesh, A. S. Abdullah, M. S. Zamani, M.
author_facet	Mohammadi, K. Shamshirband, S. Danesh, A. S. Abdullah, M. S. Zamani, M.
author_sort	Mohammadi, K.
title	Temperature-based estimation of global solar radiation using soft computing methodologies
title_short	Temperature-based estimation of global solar radiation using soft computing methodologies
title_full	Temperature-based estimation of global solar radiation using soft computing methodologies
title_fullStr	Temperature-based estimation of global solar radiation using soft computing methodologies
title_full_unstemmed	Temperature-based estimation of global solar radiation using soft computing methodologies
title_sort	temperature-based estimation of global solar radiation using soft computing methodologies
publisher	Springer-Verlag Wien
publishDate	2016
url	http://eprints.utm.my/id/eprint/71599/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-84929120395&doi=10.1007%2fs00704-015-1487-x&partnerID=40&md5=e8f2bfc95fdf9c627b46ba08a1963c13
_version_	1643656227532570624
score	13.211869

Temperature-based estimation of global solar radiation using soft computing methodologies

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