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

Öz

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.

Anahtar Kelimeler

• Machine Learning
• Geothermal Energy
• Bibliometric Analysis
• Biblioshiny
• Web of Science

"Jeotermal Enerjide Makine Öğrenmesi" Konulu Çalışmaların Bibliyometrik Analizi

Öz

Bu çalışma, jeotermal enerji ve makine öğrenmesi kesişiminde gelişen bilimsel literatürü bibliyometrik bir bakış açısıyla incelemektedir. Web of Science veri tabanından elde edilen 2010-2025 yılları arasında yayınlanmış 300 akademik yayın, R tabanlı Biblioshiny aracı kullanılarak analiz edilmiştir. Çalışmada yayınların yıllara göre dağılımı, atıf performansı, yazar, kurum ve ülke iş birlikleri, en çok atıf alan çalışmalar ve anahtar kelime eş-geçiş ağları ortaya konulmuştur. Bulgular, 2019'dan sonra ve özellikle 2022'den itibaren alanda önemli bir ivmelenme olduğunu, üretimin 2024-2025'te yüksek bir seviyeye ulaştığını göstermektedir. En çok yayını olan dergi Geothermics olmakla birlikte, Renewable Energy, Energies ve Applied Energy gibi multidisipliner dergiler de dikkat çekmiştir. Anahtar kelime analizinde Organik Rankin Döngüsü, Geliştirilmiş Jeotermal Sistem, rezervuar ve sıcaklık optimizasyonu gibi teknik temalar merkezi konumdadır; 2024 yılına gelindiğinde, hidrojen ve gelişmiş jeotermal sistemler gibi yeni trendler ortaya çıkmıştır. Çin, yayın ve atıf sayısında açık ara lider konumda olup, Amerika Birleşik Devletleri ve Almanya ile güçlü iş birliklerini sürdürmektedir. Çalışma, jeotermal enerji-makine öğrenmesi alanının durumunu kapsamlı bir şekilde özetlemekte ve gelecekteki araştırma trendleri ve iş birliği alanları için yol gösterici bir çerçeve sunmaktadır.

Anahtar Kelimeler

• Makine Öğrenmesi
• Jeotermal Enerji
• Bibliyometrik Analiz
• Biblioshiny
• Web of Science

Kaynakça

1. Chen, Y., Yu, S., Islam, S., Lim, C. P., & Muyeen, S. M. (2022). Decomposition-based wind power forecasting models and their boundary issue: An in-depth review and comprehensive discussion on potential solutions. Energy Reports, 8, 8805-8820.
2. Zhu, Y. (2024). Leveraging machine learning for subsurface geothermal energy development. Highlights in Science, Engineering and Technology, 121, 440-449.
3. Teke, O. (2024). unlocking the power of artificial intelligence: building digital twins with classification algorithms for optimized geothermal drilling. International Journal of Advanced Natural Sciences and Engineering Research, 8(5), 52–59.
4. Al‐Fakih, A., Abdulraheem, A., & Kaka, S. (2024). Application of machine learning and deep learning in geothermal resource development: Trends and perspectives. Deep Underground Science and Engineering, 3(3), 286-301.
5. Clarivate. Web Of Science Core Collection. Available: https://clarivate.com (Accessed: 20/08/2025)
6. Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of business research, 133, 285-296.
7. Ji, B., Zhao, Y., Vymazal, J., Mander, Ü., Lust, R., & Tang, C. (2021). Mapping the field of constructed wetland-microbial fuel cell: A review and bibliometric analysis. Chemosphere, 262, 128366.
8. Yu, D., Xu, Z., Kao, Y., & Lin, C. T. (2017). The structure and citation landscape of IEEE Transactions on Fuzzy Systems (1994–2015). IEEE Transactions on Fuzzy Systems, 26(2), 430-442.

9. Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of informetrics, 11(4), 959-975.
10. Ahmad, T., Zhang, H., & Yan, B. (2020). A review on renewable energy and electricity requirement forecasting models for smart grid and buildings. Sustainable Cities and Society, 55, 102052.
11. Genter, A., Evans, K., Cuenot, N., Fritsch, D., & Sanjuan, B. (2010). Contribution of the exploration of deep crystalline fractured reservoir of Soultz to the knowledge of enhanced geothermal systems (EGS). Comptes Rendus Geoscience, 342(7-8), 502-516.
12. Jha, S. K., Bilalovic, J., Jha, A., Patel, N., & Zhang, H. (2017). Renewable energy: Present research and future scope of Artificial Intelligence. Renewable and Sustainable Energy Reviews, 77, 297-317.
13. Viswanathan, H. S., Ajo‐Franklin, J., Birkholzer, J. T., Carey, J. W., Guglielmi, Y., Hyman, J. D., ... & Tartakovsky, D. M. (2022). From fluid flow to coupled processes in fractured rock: Recent advances and new frontiers. Reviews of Geophysics, 60(1), e2021RG000744.
14. Ahmad, T., & Chen, H. (2020). A review on machine learning forecasting growth trends and their real-time applications in different energy systems. Sustainable Cities and Society, 54, 102010.
15. Mehrenjani, J. R., Gharehghani, A., & Sangesaraki, A. G. (2022). Machine learning optimization of a novel geothermal driven system with LNG heat sink for hydrogen production and liquefaction. Energy Conversion and Management, 254, 115266.
16. Lin, Z., Liu, X., Lao, L., & Liu, H. (2020). Prediction of two-phase flow patterns in upward inclined pipes via deep learning. Energy, 210, 118541.
17. Shi, Y., Song, X., & Song, G. (2021). Productivity prediction of a multilateral-well geothermal system based on a long short-term memory and multi-layer perceptron combinational neural network. Applied Energy, 282, 116046.
18. Chitgar, N., Hemmati, A., & Sadrzadeh, M. (2023). A comparative performance analysis, working fluid selection, and machine learning optimization of ORC systems driven by geothermal energy. Energy Conversion and Management, 286, 117072.
19. Okoroafor, E. R., Smith, C. M., Ochie, K. I., Nwosu, C. J., Gudmundsdottir, H., & Aljubran, M. J. (2022). Machine learning in subsurface geothermal energy: Two decades in review. Geothermics, 102, 102401.