Issue |
E3S Web Conf.
Volume 491, 2024
International Conference on Environmental Development Using Computer Science (ICECS’24)
|
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Article Number | 01003 | |
Number of page(s) | 15 | |
Section | Energy Management for Sustainable Environment | |
DOI | https://doi.org/10.1051/e3sconf/202449101003 | |
Published online | 21 February 2024 |
Application of Machine Learning Techniques in Energy Power Production: A Publication Trend and Bibliometrics Analysis (2012-2023)
1 Cebu Technological University, Moalboal, Cebu, Philippines
2 Department of Computing and Information Systems, School of Engineering and Technology, Sunway University, Petaling Jaya, Selangor 47500, Malaysia.
3 Cebu Technological University, Malabuyoc Extension, Malabuyoc, Cebu, Philippines
4 Cebu Technological University, Moalboal, Cebu, Philippines
5 Cebu Technological University, Moalboal, Cebu, Philippines
6 Faculty of Architecture and Urban Design, Federal University of Uberlandia, Uberlândia, Brazil
* Corresponding author: 2samuelma@sunway.edu.my
An analysis of publishing trends and bbibliographic analysis data was conducted to critically analyse the research landscape regarding the utilisation of machine learning techniques in the field of energy and power production (EPP). The Elsevier Scopus database and the PRISMA methodology were utilised to locate and evaluate the published papers. The bibliometric analysis software package RStudio (Biblioshiny) was employed to examine the most significant sources, authors, and institutions with the highest productivity. The findings indicated that a total of 653 documents were published on the subject, consisting of conference proceedings (41.8%) and articles (50.8%), spanning the years 2012 to 2023. An review of publishing patterns indicated a significant increase in the number of publications, rising from 3 to 190, representing a growth of 5,000% over the same period. This surge can be attributed to the expanding scientific interest and the influence of research on the subject. The stakeholder analysis identified Boumaiza A, Sanfilippo A, and Wang X as the leading authors/researchers in the field. Additionally, it revealed that China, India, and the United States are the most actively involved nations in this area. In contrast, the primary financial organisation that actively supports study on the subject is the National Natural Science Foundation of China (NSFC). Overall, the study demonstrated that the utilisation of machine learning in evidence-based policy and practice (EPP) is a dynamic and interdisciplinary field of research that has the potential to make significant contributions to both research and society.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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