Publication:
Artificial neural network model with different backpropagation algorithms and meteorological data for solar radiation prediction

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dc.citedby5
dc.contributor.authorHeng S.Y.en_US
dc.contributor.authorRidwan W.M.en_US
dc.contributor.authorKumar P.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorFai C.M.en_US
dc.contributor.authorBirima A.H.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid57751621900en_US
dc.contributor.authorid57218502036en_US
dc.contributor.authorid57206939156en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid57214146115en_US
dc.contributor.authorid23466519000en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2023-05-29T09:36:04Z
dc.date.available2023-05-29T09:36:04Z
dc.date.issued2022
dc.descriptionarticle; artificial neural network; back propagation; conjugate; controlled study; Malaysia; prediction; relative humidity; solar radiation; wind speed; algorithm; Bayes theorem; meteorology; solar energy; Algorithms; Bayes Theorem; Meteorology; Neural Networks, Computer; Solar Energyen_US
dc.description.abstractSolar energy serves as a great alternative to fossil fuels as they are clean and renewable energy. Accurate solar radiation (SR) prediction can substantially lower down the impact cost pertaining to the development of solar energy. Lately, many SR forecasting system has been developed such as support vector machine, autoregressive moving average and artificial neural network (ANN). This paper presents a comprehensive study on the meteorological data and types of backpropagation (BP) algorithms used to train and develop the best SR predicting ANN model. The meteorological data, which includes temperature, relative humidity and wind speed are collected from a meteorological station from Kuala Terrenganu, Malaysia. Three different BP algorithms are employed into training the model i.e., Levenberg�Marquardt, Scaled Conjugate Gradient and Bayesian Regularization (BR). This paper presents a comparison study to select the best combination of meteorological data and BP algorithm which can develop the ANN model with the best predictive ability. The findings from this study shows that temperature and relative humidity both have high correlation with SR whereas wind temperature has little influence over SR. The results also showed that BR algorithm trained ANN models with maximum R of 0.8113 and minimum RMSE of 0.2581, outperform other algorithm trained models, as indicated by the performance score of the respective models. � 2022, The Author(s).en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo10457
dc.identifier.doi10.1038/s41598-022-13532-3
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85132267438
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85132267438&doi=10.1038%2fs41598-022-13532-3&partnerID=40&md5=0f965de70db10867c6cbf896013d1c18
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26656
dc.identifier.volume12
dc.publisherNature Researchen_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleScientific Reports
dc.titleArtificial neural network model with different backpropagation algorithms and meteorological data for solar radiation predictionen_US
dc.typeArticleen_US
dspace.entity.typePublication
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