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

artificial 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, Com...

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Main Authors: Jumin E., Basaruddin F.B., Yusoff Y.B.M., Latif S.D., Ahmed A.N.
Other Authors: 57216831084
Format: Article
Published: Springer Science and Business Media Deutschland GmbH 2023
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id my.uniten.dspace-26185
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spelling my.uniten.dspace-261852023-05-29T17:07:31Z Solar radiation prediction using boosted decision tree regression model: A case study in Malaysia Jumin E. Basaruddin F.B. Yusoff Y.B.M. Latif S.D. Ahmed A.N. 57216831084 15073693400 57221716993 57216081524 57214837520 artificial 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 Energy Reliable 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. Final 2023-05-29T09:07:31Z 2023-05-29T09:07:31Z 2021 Article 10.1007/s11356-021-12435-6 2-s2.0-85099985169 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099985169&doi=10.1007%2fs11356-021-12435-6&partnerID=40&md5=1536bdd8d5b30dfbc7519130a597c455 https://irepository.uniten.edu.my/handle/123456789/26185 28 21 26571 26583 Springer Science and Business Media Deutschland GmbH Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description artificial 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 Energy
author2 57216831084
author_facet 57216831084
Jumin E.
Basaruddin F.B.
Yusoff Y.B.M.
Latif S.D.
Ahmed A.N.
format Article
author Jumin E.
Basaruddin F.B.
Yusoff Y.B.M.
Latif S.D.
Ahmed A.N.
spellingShingle Jumin E.
Basaruddin F.B.
Yusoff Y.B.M.
Latif S.D.
Ahmed A.N.
Solar radiation prediction using boosted decision tree regression model: A case study in Malaysia
author_sort Jumin E.
title Solar radiation prediction using boosted decision tree regression model: A case study in Malaysia
title_short Solar radiation prediction using boosted decision tree regression model: A case study in Malaysia
title_full Solar radiation prediction using boosted decision tree regression model: A case study in Malaysia
title_fullStr Solar radiation prediction using boosted decision tree regression model: A case study in Malaysia
title_full_unstemmed Solar radiation prediction using boosted decision tree regression model: A case study in Malaysia
title_sort solar radiation prediction using boosted decision tree regression model: a case study in malaysia
publisher Springer Science and Business Media Deutschland GmbH
publishDate 2023
_version_ 1806424040569569280
score 13.211869

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