Artículo

New Insights into the Emerging Trends Research of Machine and Deep Learning Applications in Energy Storage: A Bibliometric Analysis and Publication Trends

Resumen

The publication trends and bibliometric analysis of the research landscape on the applications of machine and deep learning in energy storage (MDLES) research were examined in this study based on published documents in the Elsevier Scopus database between 2012 and 2022. The PRISMA technique employed to identify, screen, and filter related publications on MDLES research recovered 969 documents comprising articles, conference papers, and reviews published in English. The results showed that the publications count on the topic increased from 3 to 385 (or a 12,733.3% increase) along with citations between 2012 and 2022. The high publications and citations rate was ascribed to the MDLES research impact, co-authorships/collaborations, as well as the source title/journals’ reputation, multidisciplinary nature, and research funding. The top/most prolific researcher, institution, country, and funding body on MDLES research are; is Yan Xu, Tsinghua University, China, and the National Natural Science Foundation of China, respectively. Keywords occurrence analysis revealed three clusters or hotspots based on machine learning, digital storage, and Energy Storage. Further analysis of the research landscape showed that MDLES research is currently and largely focused on the application of machine/deep learning for predicting, operating, and optimising energy storage as well as the design of energy storage materials for renewable energy technologies such as wind, and PV solar. However, future research will presumably include a focus on advanced energy materials development, operational systems monitoring and control as well as techno-economic analysis to address challenges associated with energy efficiency analysis, costing of renewable energy electricity pricing, trading, and revenue prediction.
Ajibade, Samuel-Soma M. (57209028006); Zaidi, Abdelhamid (9241739100); Al Luhayb, Asamh Saleh M. (57211063812); Adediran, Anthonia Oluwatosin (57210390374); Voumik, Liton Chandra (58529725000); Rabbi, Fazle (57211523145)
New Insights into the Emerging Trends Research of Machine and Deep Learning Applications in Energy Storage: A Bibliometric Analysis and Publication Trends
2023
10.32479/ijeep.14832
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85172359728&doi=10.32479%2fijeep.14832&partnerID=40&md5=76b225ec7d79eae758a3430aed1aa980
Department of Computer Engineering, Istanbul Ticaret University, Istanbul, Turkey; Department of Mathematics, College of Science, Qassim University, Qassim, Buraydah, Saudi Arabia; Faculty of Architecture and Urban Design, Federal University of Uberlandia, Brazil; Department of Economics, Noakhali Science and Technology University, Noakhali, Bangladesh; Australian Computer Society, VIC, Australia
All Open Access; Gold Open Access
Scopus
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