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...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Published: |
Springer Science and Business Media Deutschland GmbH
2023
|
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1833349902929756160 |
|---|---|
| author | Jumin E. Basaruddin F.B. Yusoff Y.B.M. Latif S.D. Ahmed A.N. |
| author2 | 57216831084 |
| author_facet | 57216831084 Jumin E. Basaruddin F.B. Yusoff Y.B.M. Latif S.D. Ahmed A.N. |
| author_sort | Jumin E. |
| building | UNITEN Library |
| collection | Institutional Repository |
| content_provider | Universiti Tenaga Nasional |
| content_source | UNITEN Institutional Repository |
| continent | Asia |
| country | Malaysia |
| 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 |
| format | Article |
| id | my.uniten.dspace-26185 |
| institution | Universiti Tenaga Nasional |
| publishDate | 2023 |
| publisher | Springer Science and Business Media Deutschland GmbH |
| record_format | dspace |
| 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 |
| 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 |
| title | 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_short | 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 |
| url_provider | http://dspace.uniten.edu.my/ |