Publication:
Solar radiation prediction using boosted decision tree regression model: A case study in Malaysia

Simple item page
dc.citedby33
dc.contributor.authorJumin E.en_US
dc.contributor.authorBasaruddin F.B.en_US
dc.contributor.authorYusoff Y.B.M.en_US
dc.contributor.authorLatif S.D.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorid57216831084en_US
dc.contributor.authorid15073693400en_US
dc.contributor.authorid57221716993en_US
dc.contributor.authorid57216081524en_US
dc.contributor.authorid57214837520en_US
dc.date.accessioned2023-05-29T09:07:31Z
dc.date.available2023-05-29T09:07:31Z
dc.date.issued2021
dc.descriptionartificial intelligence; artificial neural network; numerical model; prediction; regression analysis; solar power; solar radiation; Malaysia; algorithm; artificial intelligence; decision tree; Malaysia; solar energy; Algorithms; Artificial Intelligence; Decision Trees; Malaysia; Neural Networks, Computer; Solar Energyen_US
dc.description.abstractReliable and accurate prediction model capturing the changes in solar radiation is essential in the power generation and renewable carbon-free energy industry. Malaysia has immense potential to develop such an industry due to its location in the equatorial zone and its climatic characteristics with high solar energy resources. However, solar energy accounts for only 2�4.6% of total energy utilization. Recently, in developed countries, various prediction models based on artificial intelligence (AI) techniques have been applied to predict solar radiation. In this study, one of the most recent AI algorithms, namely, boosted decision tree regression (BDTR) model, was applied to predict the changes in solar radiation based on collected data in Malaysia. The proposed model then compared with other conventional regression algorithms, such as linear regression and neural network. Two different normalization techniques (Gaussian normalizer binning normalizer), splitting size, and different input parameters were investigated to enhance the accuracy of the models. Sensitivity analysis and uncertainty analysis were introduced to validate the accuracy of the proposed model. The results revealed that BDTR outperformed other algorithms with a high level of accuracy. The funding of this study could be used as a reliable tool by engineers to improve the renewable energy sector in Malaysia and provide alternative sustainable energy resources. � 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/s11356-021-12435-6
dc.identifier.epage26583
dc.identifier.issue21
dc.identifier.scopus2-s2.0-85099985169
dc.identifier.spage26571
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85099985169&doi=10.1007%2fs11356-021-12435-6&partnerID=40&md5=1536bdd8d5b30dfbc7519130a597c455
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26185
dc.identifier.volume28
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.sourceScopus
dc.sourcetitleEnvironmental Science and Pollution Research
dc.titleSolar radiation prediction using boosted decision tree regression model: A case study in Malaysiaen_US
dc.typeArticleen_US
dspace.entity.typePublication
Files
Collections
SCOPUS