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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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    1. Sam Aflaki & Serguei Netessine, 2017. "Strategic Investment in Renewable Energy Sources: The Effect of Supply Intermittency," Manufacturing & Service Operations Management, INFORMS, vol. 19(3), pages 489-507, July.
    2. Sharifzadeh, Mahdi & Sikinioti-Lock, Alexandra & Shah, Nilay, 2019. "Machine-learning methods for integrated renewable power generation: A comparative study of artificial neural networks, support vector regression, and Gaussian Process Regression," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 513-538.
    3. Zhong-Hui Shen & Jian-Jun Wang & Jian-Yong Jiang & Sharon X. Huang & Yuan-Hua Lin & Ce-Wen Nan & Long-Qing Chen & Yang Shen, 2019. "Phase-field modeling and machine learning of electric-thermal-mechanical breakdown of polymer-based dielectrics," Nature Communications, Nature, vol. 10(1), pages 1-10, December.
    4. Weitemeyer, Stefan & Kleinhans, David & Vogt, Thomas & Agert, Carsten, 2015. "Integration of Renewable Energy Sources in future power systems: The role of storage," Renewable Energy, Elsevier, vol. 75(C), pages 14-20.
    5. Kuk Yeol Bae & Han Seung Jang & Bang Chul Jung & Dan Keun Sung, 2019. "Effect of Prediction Error of Machine Learning Schemes on Photovoltaic Power Trading Based on Energy Storage Systems," Energies, MDPI, vol. 12(7), pages 1-20, April.
    6. Wang, Zhe & Hong, Tianzhen, 2020. "Reinforcement learning for building controls: The opportunities and challenges," Applied Energy, Elsevier, vol. 269(C).
    7. María Bordons & Javier Aparicio & Rodrigo Costas, 2013. "Heterogeneity of collaboration and its relationship with research impact in a biomedical field," Scientometrics, Springer;Akadémiai Kiadó, vol. 96(2), pages 443-466, August.
    8. Laith Abualigah & Raed Abu Zitar & Khaled H. Almotairi & Ahmad MohdAziz Hussein & Mohamed Abd Elaziz & Mohammad Reza Nikoo & Amir H. Gandomi, 2022. "Wind, Solar, and Photovoltaic Renewable Energy Systems with and without Energy Storage Optimization: A Survey of Advanced Machine Learning and Deep Learning Techniques," Energies, MDPI, vol. 15(2), pages 1-26, January.
    9. 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.
    10. Rodrigo A. Estévez & Valeria Espinoza & Roberto D. Ponce Oliva & Felipe Vásquez-Lavín & Stefan Gelcich, 2021. "Multi-Criteria Decision Analysis for Renewable Energies: Research Trends, Gaps and the Challenge of Improving Participation," Sustainability, MDPI, vol. 13(6), pages 1-13, March.
    11. Zhisen Jiang & Jizhou Li & Yang Yang & Linqin Mu & Chenxi Wei & Xiqian Yu & Piero Pianetta & Kejie Zhao & Peter Cloetens & Feng Lin & Yijin Liu, 2020. "Machine-learning-revealed statistics of the particle-carbon/binder detachment in lithium-ion battery cathodes," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
    12. Wen, Lulu & Zhou, Kaile & Yang, Shanlin & Lu, Xinhui, 2019. "Optimal load dispatch of community microgrid with deep learning based solar power and load forecasting," Energy, Elsevier, vol. 171(C), pages 1053-1065.
    13. Gençer, Emre & Agrawal, Rakesh, 2016. "A commentary on the US policies for efficient large scale renewable energy storage systems: Focus on carbon storage cycles," Energy Policy, Elsevier, vol. 88(C), pages 477-484.
    14. Gautam Gowrisankaran & Stanley S. Reynolds & Mario Samano, 2016. "Intermittency and the Value of Renewable Energy," Journal of Political Economy, University of Chicago Press, vol. 124(4), pages 1187-1234.
    15. Christopher Carroll, 2016. "Measuring academic research impact: creating a citation profile using the conceptual framework for implementation fidelity as a case study," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(2), pages 1329-1340, November.
    16. Schopfer, S. & Tiefenbeck, V. & Staake, T., 2018. "Economic assessment of photovoltaic battery systems based on household load profiles," Applied Energy, Elsevier, vol. 223(C), pages 229-248.
    17. Foley, Aoife M. & Leahy, Paul G. & Marvuglia, Antonino & McKeogh, Eamon J., 2012. "Current methods and advances in forecasting of wind power generation," Renewable Energy, Elsevier, vol. 37(1), pages 1-8.
    18. Marcin Wysokiński & Joanna Domagała & Arkadiusz Gromada & Magdalena Golonko & Paulina Trębska, 2020. "Economic and energy efficiency of agriculture," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 66(8), pages 355-364.
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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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