Data-driven hybrid approaches for renewable power prediction toward grid decarbonization: Applications, issues and suggestions
Decarbonization; Electric power transmission networks; Forecasting; Fossil fuels; Global warming; Solar energy; Data driven; Data-driven algorithm; Data-driven approach; Data-driven methods; Decarbonisation; Hybrid approach; Hybrid datum; Optimisations; Power predictions; Renewable Power; Wind power
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2023
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my.uniten.dspace-258462023-05-29T17:05:15Z Data-driven hybrid approaches for renewable power prediction toward grid decarbonization: Applications, issues and suggestions Hossain Lipu M.S. Miah M.S. Ansari S. Hannan M.A. Hasan K. Sarker M.R. Mahmud M.S. Hussain A. Mansor M. 36518949700 57226266149 57218906707 7103014445 57205215021 57537703000 57220492528 57208481391 6701749037 Decarbonization; Electric power transmission networks; Forecasting; Fossil fuels; Global warming; Solar energy; Data driven; Data-driven algorithm; Data-driven approach; Data-driven methods; Decarbonisation; Hybrid approach; Hybrid datum; Optimisations; Power predictions; Renewable Power; Wind power Global warming and climate change are serious problems that need urgent action and replacement. Renewable power could be the promising alternative solution to fossil fuel-based electricity generation in minimizing carbon intensity and achieving the global decarbonization target by 2050. However, intermittent characteristics of renewables such as solar and wind have resulted in negative effects on the operation, reliability, and stability of the power grid. To address these concerns, the hybridization of data-driven algorithms has achieved substantial contributions in renewable power prediction with regard to efficiency, precision and robustness. The main contribution of this study is to provide a detailed explanation of the recent progress of hybrid data-driven algorithms for renewable power prediction including solar, wind, ocean, hydro, and geothermal highlighting their variables, forecasting horizons, performance indexes, contributions and limitations. Besides, the impact of grid decarbonization in connection with renewable power is analyzed rigorously. Furthermore, this review explores the key issues and challenges of hybrid data-driven approaches in renewable power prediction to identify existing research gaps and limitations. Finally, this paper delivers selective suggestions that will support academic researchers and power engineers to develop advanced hybrid data-driven approaches for future renewable power prediction toward achieving the decarbonization goal. � 2021 Elsevier Ltd Final 2023-05-29T09:05:15Z 2023-05-29T09:05:15Z 2021 Review 10.1016/j.jclepro.2021.129476 2-s2.0-85119970556 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119970556&doi=10.1016%2fj.jclepro.2021.129476&partnerID=40&md5=772c82c212716ec20a8f95b117f42e3b https://irepository.uniten.edu.my/handle/123456789/25846 328 129476 Elsevier Ltd Scopus |
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| description |
Decarbonization; Electric power transmission networks; Forecasting; Fossil fuels; Global warming; Solar energy; Data driven; Data-driven algorithm; Data-driven approach; Data-driven methods; Decarbonisation; Hybrid approach; Hybrid datum; Optimisations; Power predictions; Renewable Power; Wind power |
| author2 |
36518949700 |
| author_facet |
36518949700 Hossain Lipu M.S. Miah M.S. Ansari S. Hannan M.A. Hasan K. Sarker M.R. Mahmud M.S. Hussain A. Mansor M. |
| format |
Review |
| author |
Hossain Lipu M.S. Miah M.S. Ansari S. Hannan M.A. Hasan K. Sarker M.R. Mahmud M.S. Hussain A. Mansor M. |
| spellingShingle |
Hossain Lipu M.S. Miah M.S. Ansari S. Hannan M.A. Hasan K. Sarker M.R. Mahmud M.S. Hussain A. Mansor M. Data-driven hybrid approaches for renewable power prediction toward grid decarbonization: Applications, issues and suggestions |
| author_sort |
Hossain Lipu M.S. |
| title |
Data-driven hybrid approaches for renewable power prediction toward grid decarbonization: Applications, issues and suggestions |
| title_short |
Data-driven hybrid approaches for renewable power prediction toward grid decarbonization: Applications, issues and suggestions |
| title_full |
Data-driven hybrid approaches for renewable power prediction toward grid decarbonization: Applications, issues and suggestions |
| title_fullStr |
Data-driven hybrid approaches for renewable power prediction toward grid decarbonization: Applications, issues and suggestions |
| title_full_unstemmed |
Data-driven hybrid approaches for renewable power prediction toward grid decarbonization: Applications, issues and suggestions |
| title_sort |
data-driven hybrid approaches for renewable power prediction toward grid decarbonization: applications, issues and suggestions |
| publisher |
Elsevier Ltd |
| publishDate |
2023 |
| _version_ |
1806427810137374720 |
| score |
13.222552 |