Bibliometric Analysis of Studies on "Machine Learning in Geothermal Energy"

Authors : Orkun Teke

Pages : 57-68

Doi:10.55385/kastamonujes.1783433

View : 76 | Download : 169

Publication Date : 2025-12-25

Article Type : Research Paper

Abstract :This study examines the scientific literature developing at the intersection of geothermal energy and machine learning from a bibliometric perspective. 300 academic publications published between 2010 and 2025, obtained from the Web of Science database, were analyzed using the R-based Biblioshiny tool. The study revealed the distribution of publications by year, citation performance, author, institution and country collaborations, the most cited studies and keyword co-occurrence networks. The findings show that there has been a significant acceleration in the field after 2019 and especially from 2022, with production reaching a high level in 2024–2025. While Geothermics was the journal with the most publications, multidisciplinary journals such as Renewable Energy, Energies, and Applied Energy also attracted attention. In the keyword analysis, technical themes such as Organic Rankin Cycle, Enhanced Geothermal System, reservoir, and temperature optimization were central; By 2024, new trends such as hydrogen and advanced geothermal systems have emerged. China leads by far in the number of publications and citations and maintains strong collaborations with the United States and Germany. The study comprehensively summarizes the status of the geothermal energy-machine learning field and provides a guiding framework for future research trends and areas of collaboration.
Keywords : Makine Öğrenmesi, Jeotermal Enerji, Bibliyometrik Analiz, Biblioshiny, Web of Science

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