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Improving solar energy prediction in complex topography using artificial neural networks: Case study Peninsular Malaysia

This research assesses the feasibility of using artificial neural networks (ANN) to predict and improve the spatial distribution of solar radiation data, using Peninsular Malaysia as a case study. This peninsula has seas to the east and west that control cloud formation and rain throughout the year....

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Main Authors:	Al-Fatlawi, A.W.A., Rahim, Nasrudin Abd, Saidur, R., Ward, T.A.
Format:	Article
Published:	Wiley 2015
Subjects:	TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery
Online Access:	http://eprints.um.edu.my/19488/ http://dx.doi.org/10.1002/ep.12130
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id	my.um.eprints.19488
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spelling	my.um.eprints.194882019-10-25T06:31:27Z http://eprints.um.edu.my/19488/ Improving solar energy prediction in complex topography using artificial neural networks: Case study Peninsular Malaysia Al-Fatlawi, A.W.A. Rahim, Nasrudin Abd Saidur, R. Ward, T.A. TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery This research assesses the feasibility of using artificial neural networks (ANN) to predict and improve the spatial distribution of solar radiation data, using Peninsular Malaysia as a case study. This peninsula has seas to the east and west that control cloud formation and rain throughout the year. A rugged mountain range bisects the length of the peninsula creating a complex topography. These features make it difficult to develop effective empirical solar radiation models to cover large areas in Peninsular Malaysia. In this article, several different solar radiation prediction models were designed using the ANN tool in MATLAB. Geographical and meteorological data from 24 solar energy stations were used to predict the solar radiation in 341 cities. Standard multilayer, feed-forward, and back-propagation neural networks were used for the 12 solar radiation models with different numbers of neurons, training functions and activation functions. Predicted solar radiation results were actively used to develop monthly solar radiation maps. The results show that the mean absolute percentage error is less than 6.07% for both the training and testing datasets. This shows that the models are highly reliable predictors of solar radiation values, even in the selected locations that have deficient or unavailable solar radiation databases. The maps show that Peninsular Malaysia receives a monthly average daily solar radiation of between 3.82 and 5.23 kWh/m2-day, and that the extreme northern region in Peninsular Malaysia has the highest solar radiation intensity throughout the year. Wiley 2015 Article PeerReviewed Al-Fatlawi, A.W.A. and Rahim, Nasrudin Abd and Saidur, R. and Ward, T.A. (2015) Improving solar energy prediction in complex topography using artificial neural networks: Case study Peninsular Malaysia. Environmental Progress & Sustainable Energy, 34 (5). pp. 1528-1535. ISSN 1944-7442 http://dx.doi.org/10.1002/ep.12130 doi:10.1002/ep.12130
institution	Universiti Malaya
building	UM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaya
content_source	UM Research Repository
url_provider	http://eprints.um.edu.my/
topic	TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery
spellingShingle	TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery Al-Fatlawi, A.W.A. Rahim, Nasrudin Abd Saidur, R. Ward, T.A. Improving solar energy prediction in complex topography using artificial neural networks: Case study Peninsular Malaysia
description	This research assesses the feasibility of using artificial neural networks (ANN) to predict and improve the spatial distribution of solar radiation data, using Peninsular Malaysia as a case study. This peninsula has seas to the east and west that control cloud formation and rain throughout the year. A rugged mountain range bisects the length of the peninsula creating a complex topography. These features make it difficult to develop effective empirical solar radiation models to cover large areas in Peninsular Malaysia. In this article, several different solar radiation prediction models were designed using the ANN tool in MATLAB. Geographical and meteorological data from 24 solar energy stations were used to predict the solar radiation in 341 cities. Standard multilayer, feed-forward, and back-propagation neural networks were used for the 12 solar radiation models with different numbers of neurons, training functions and activation functions. Predicted solar radiation results were actively used to develop monthly solar radiation maps. The results show that the mean absolute percentage error is less than 6.07% for both the training and testing datasets. This shows that the models are highly reliable predictors of solar radiation values, even in the selected locations that have deficient or unavailable solar radiation databases. The maps show that Peninsular Malaysia receives a monthly average daily solar radiation of between 3.82 and 5.23 kWh/m2-day, and that the extreme northern region in Peninsular Malaysia has the highest solar radiation intensity throughout the year.
format	Article
author	Al-Fatlawi, A.W.A. Rahim, Nasrudin Abd Saidur, R. Ward, T.A.
author_facet	Al-Fatlawi, A.W.A. Rahim, Nasrudin Abd Saidur, R. Ward, T.A.
author_sort	Al-Fatlawi, A.W.A.
title	Improving solar energy prediction in complex topography using artificial neural networks: Case study Peninsular Malaysia
title_short	Improving solar energy prediction in complex topography using artificial neural networks: Case study Peninsular Malaysia
title_full	Improving solar energy prediction in complex topography using artificial neural networks: Case study Peninsular Malaysia
title_fullStr	Improving solar energy prediction in complex topography using artificial neural networks: Case study Peninsular Malaysia
title_full_unstemmed	Improving solar energy prediction in complex topography using artificial neural networks: Case study Peninsular Malaysia
title_sort	improving solar energy prediction in complex topography using artificial neural networks: case study peninsular malaysia
publisher	Wiley
publishDate	2015
url	http://eprints.um.edu.my/19488/ http://dx.doi.org/10.1002/ep.12130
_version_	1648736161132183552
score	13.211869

Improving solar energy prediction in complex topography using artificial neural networks: Case study Peninsular Malaysia

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