Computational approaches to understanding interaction and development

Messinger, Daniel S.; Perry, Lynn K.; Mitsven, Samantha G.; Tao, Yudong; Moffitt, Jacquelyn; Fasano, Regina M.; Custode, Stephanie; Jerry, Christian M.

doi:10.1016/bs.acdb.2021.12.002

Cited by 3 publications

(1 citation statement)

References 76 publications

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“…On the other hand, deep learning is defined as a branch of ML that utilizes multilayered neural networks to solve complex computational problems (Gotway et al, 2016). According to (Messinger et al, 2022), DL has revolutionised the tractability and precision of ML, which has resulted in many breakthrough innovations in fields. Likewise, ML offers an integrated structure for presenting intelligent decisionmaking into several domains (Aguiar-Pérez et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

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

Ajibade,

Zaidi,

Al Luhayb

et al. 2023

IJEEP

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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

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Section: Introductionmentioning

confidence: 99%