New Insights into the Emerging Trends Research of Machine and Deep Learning Applications in Energy Storage: A Bibliometric Analysis and Publication Trends
YĂĽkleniyor...
Tarih
2023
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Econjournals
Erişim Hakkı
info:eu-repo/semantics/openAccess
Ă–zet
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.
Açıklama
Anahtar Kelimeler
Machine Learning, Deep Learning, Artificial Intelligence, Energy Storage, Renewable Energy Technologies, Bibliometric Analysis
Kaynak
International Journal of Energy Economics and Policy
WoS Q DeÄźeri
Scopus Q DeÄźeri
N/A
Cilt
13
Sayı
5
