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

Author

Listed:
  • Samuel-Soma Ajibade

    (Department of Computer Engineering, Istanbul Ticaret University, Istanbul, Turkiye,)

  • Abdelhamid Zaidi

    (Department of Mathematics, College of Science, Qassim University, Buraydah, Qassim, Saudi Arabia,)

  • Asamh Saleh M. Al Luhayb

    (Department of Mathematics, College of Science, Qassim University, Buraydah, Qassim, Saudi Arabia,)

  • Anthonia Oluwatosin Adediran

    (Faculty of Architecture and Urban Design, Federal University of Uberlandia, Brazil,)

  • Liton Chandra Voumik

    (Department of Economics, Noakhali Science and Technology University, Noakhali, Bangladesh,)

  • Fazle Rabbi

    (Australian Computer Society, Victoria, Australia.)

Abstract

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.

Suggested Citation

  • 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.
    • Handle: RePEc:eco:journ2:2023-05-35
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    References listed on IDEAS

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    More about this item

    Keywords

    Machine Learning; Deep learning; Energy Storage; Renewable Energy Technologies; Bibliometric Analysis;
    All these keywords.

    JEL classification:

    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • R3 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location

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