article · Clean Technologies
A bibliometric analysis of research published between 2012 and 2021 assesses the global landscape of machine learning applications in renewable energy. Drawing on 1,218 documents indexed in Scopus, the study tracks publication outputs, leading institutions, key funders, and topical clusters across science, technology, engineering, and mathematics. Most outputs consist of journal articles and conference papers, with high productivity driven by cross-institutional collaborations and dedicated funding support. The United States and the National Renewable Energy Laboratory represent the leading country and affiliation, while the National Natural Science Foundation of China is the most prominent funder. The analysis highlights four thematic clusters covering systems, technologies, tools, and socio-technical dynamics. Across these areas, machine learning plays a vital role in prediction, system operation, technology optimisation, and the design and development of renewable energy materials.
Understanding how machine learning integrates into renewable energy helps researchers, funders, and policymakers identify technological trends and investment priorities. Mapping global publication networks reveals where technical expertise is concentrated. This insight helps stakeholders navigate international partnerships and direct resources towards high-impact areas, such as energy prediction, system optimisation, and clean-energy materials design.
Because this work is a bibliometric analysis rather than an experimental study, it does not assess a specific product or technology readiness level. However, it indicates that machine learning tools are applicable to the prediction, operation, and optimisation of renewable energy technologies and materials. Clean-tech developers, grid operators, and industrial partners can use these mapping insights to identify established research clusters and potential institutional partners for commercial development.
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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.
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DOI: 10.3390/cleantechnol5020026
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