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

Samuel-Soma M. Ajibade, Festus Bekun, Festus Fatai Adedoyin, Bright Akwasi Gyamfi and Anthonia Oluwatosin Adediran
Additional contact information
Samuel-Soma M. Ajibade: Department of Computer Engineering, Istanbul Ticaret University, Istanbul 34445, Turkey
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

Clean Technol., 2023, vol. 5, issue 2, 1-21

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.

Keywords: machine learning; algorithms; supervised learning; unsupervised learning; deep learning; renewable energy; forecasting; optimization (search for similar items in EconPapers)
JEL-codes: Q2 Q3 Q4 Q5 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://www.mdpi.com/2571-8797/5/2/26/pdf (application/pdf)
https://www.mdpi.com/2571-8797/5/2/26/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jcltec:v:5:y:2023:i:2:p:26-517:d:1127160

Access Statistics for this article

Clean Technol. is currently edited by Ms. Shary Song

More articles in Clean Technol. from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().