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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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Main Authors: Hossain Lipu M.S., Miah M.S., Ansari S., Hannan M.A., Hasan K., Sarker M.R., Mahmud M.S., Hussain A., Mansor M.
Other Authors: 36518949700
Format: Review
Published: Elsevier Ltd 2023
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spelling 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
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 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

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