A BIBLIOMETRIC EXAMINATION OF DIGITAL TWIN APPLICATIONS IN SUSTAINABLE ENERGY

The rapid development of digital technologies has created new opportunities to improve the efficiency, flexibility, and sustainability of energy systems, with Digital Twin technology emerging as an important approach for integrating real-time monitoring, simulation, prediction, and optimization. This study aims to examine the development, intellectual structure, thematic evolution, and collaboration patterns of research on Digital Twin applications in sustainable energy. A bibliometric analysis was conducted using scientific publications related to Digital Twin and sustainable energy, with the literature examined through citation analysis, keyword co-occurrence analysis, temporal overlay analysis, density visualization, and collaboration mapping at the author, institutional, and country levels. The findings indicate that digital twin, energy systems, energy management, energy efficiency, and optimization constitute central themes in the research landscape. The temporal analysis further reveals increasing attention to machine learning, deep learning, predictive analytics, forecasting, predictive maintenance, renewable energy, energy storage, and predictive control systems. Highly cited studies demonstrate a parallel development from foundational Digital Twin architectures and integrated energy-system scheduling toward applications involving smart grids, renewable energy, electric vehicles, and AI-supported optimization. The collaboration analysis shows a globally distributed research landscape involving multiple countries and research communities, although institutional and author collaboration remains relatively fragmented. The findings indicate that Digital Twin research in sustainable energy is shifting from foundational system representation toward AI-enabled, predictive, and integrated applications that support more efficient and sustainable energy-system operation.