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Machine Learning Applications in Renewable Energy (MLARE) Research: A Publication Trend and Bibliometric Analysis Study (2012-2021).

Ajibade, S-S. M., Bekun, F. V., Adedoyin, F. F., Gyamfi, B. A. and Adediran, A. O., 2023. Machine Learning Applications in Renewable Energy (MLARE) Research: A Publication Trend and Bibliometric Analysis Study (2012-2021). Clean Technologies, 5 (2), 497-517.

Full text available as:

cleantechnol-05-00026.pdf - Published Version
Available under License Creative Commons Attribution.


DOI: 10.3390/cleantechnol5020026


This study examines the research climate on machine learning applications in renewable energy (MLARE). Therefore, the publication trends (PT) and bibliometric analysis (BA) on MLARE research published and indexed in the Elsevier Scopus database between 2012 and 2021 were examined. The PT was adopted to deduce the major stakeholders, top-cited publications, and funding organizations on MLARE, whereas BA elucidated critical insights into the research landscape, scientific developments, and technological growth. The PT revealed 1218 published documents comprising 46.9% articles, 39.7% conference papers, and 6.0% reviews on the topic. Subject area analysis revealed MLARE research spans the areas of science, technology, engineering, and mathematics among others, which indicates it is a broad, multidisciplinary, and impactful research topic. The most prolific researcher, affiliations, country, and funder are Ravinesh C. Deo, National Renewable Energy Laboratory, United States, and the National Natural Science Foundation of China, respectively. The most prominent journals on the top are Applied Energy and Energies, which indicates that journal reputation and open access are critical considerations for the author’s choice of publication outlet. The high productivity of the major stakeholders in MLARE is due to collaborations and research funding support. The keyword co-occurrence analysis identified four (4) clusters or thematic areas on MLARE, which broadly describe the systems, technologies, tools/technologies, and socio-technical dynamics of MLARE research. Overall, the study showed that ML is critical to the prediction, operation, and optimization of renewable energy technologies (RET) along with the design and development of RE-related materials.

Item Type:Article
Uncontrolled Keywords:machine learning; algorithms; supervised learning; unsupervised learning; deep learning; renewable energy; forecasting; optimization
Group:Faculty of Science & Technology
ID Code:38780
Deposited By: Symplectic RT2
Deposited On:13 Jul 2023 15:12
Last Modified:13 Jul 2023 15:12


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