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

Author

Listed:
  • Samuel-Soma M. Ajibade

    (Department of Computer Engineering, Istanbul Ticaret University, Istanbul 34445, Turkey)

  • Festus Victor Bekun

    (Faculty of Economics Administrative and Social Sciences, Istanbul Gelisim University, Istanbul 34310, Turkey
    Department of Economics, Adnan Kassar School of Business, Lebanese American University, Beirut 1102-2801, Lebanon)

  • Festus Fatai Adedoyin

    (Department of Computing and Informatics, Bournemouth University, Poole BH12 5BB, UK)

  • Bright Akwasi Gyamfi

    (School of Management, Sir Padampat Singhania University, Bhatewar, Udaipur 313601, India)

  • Anthonia Oluwatosin Adediran

    (Department of Estate Management, The Federal Polytechnic, Ado Ekiti 23401, Nigeria)

Abstract

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.

Suggested Citation

  • Samuel-Soma M. Ajibade & Festus Victor Bekun & Festus Fatai Adedoyin & Bright Akwasi Gyamfi & Anthonia Oluwatosin Adediran, 2023. "Machine Learning Applications in Renewable Energy (MLARE) Research: A Publication Trend and Bibliometric Analysis Study (2012–2021)," Clean Technol., MDPI, vol. 5(2), pages 1-21, April.
    • Handle: RePEc:gam:jcltec:v:5:y:2023:i:2:p:26-517:d:1127160
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    Cited by:

    1. Anthonia Oluwatosin Adediran & Samuel-Soma Ajibade & Abdelhamid Zaidi & Faizah Mohammed Bashir & Emmanuel Falude & Yakubu Aminu Dodo & Muhammed Basheer Jasser, 2024. "A Science Mapping Analysis of Energy Efficiency and Affordable Housing Research," International Journal of Energy Economics and Policy, Econjournals, vol. 14(6), pages 436-449, November.
    2. Abdelhamid Zaidi & Samuel-Soma M. Ajibade & Majd Musa & Festus Victor Bekun, 2023. "New Insights into the Research Landscape on the Application of Artificial Intelligence in Sustainable Smart Cities: A Bibliometric Mapping and Network Analysis Approach," International Journal of Energy Economics and Policy, Econjournals, vol. 13(4), pages 287-299, July.
    3. Samuel-Soma Ajibade & Abdelhamid Zaidi & Asamh Saleh M. Al Luhayb & Anthonia Oluwatosin Adediran & Liton Chandra Voumik & Fazle Rabbi, 2023. "New Insights into the Emerging Trends Research of Machine and Deep Learning Applications in Energy Storage: A Bibliometric Analysis and Publication Trends," International Journal of Energy Economics and Policy, Econjournals, vol. 13(5), pages 303-314, September.
    4. Manal Elhaj & Jihen Bousrih & Hind Alofaysan, 2024. "Can Technological Advancement Empower the Future of Renewable Energy? A Panel Autoregressive Distributed Lag Approach," Energies, MDPI, vol. 17(20), pages 1-18, October.

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