Analysis of the use of author keywords and IEEE terms in IEEE Xplore data to identify current research topics in energy technology and existing limitations

Relevance. The study analyzes the topic of increasing energy consumption by AI systems, especially in training large models, which places pressure on energy infrastructures. The aim of this study is to analyze the limitations in the use of Author Keywords and IEEE terms presented in the corresponding fields of bibliometric records in the IEEE Xplore database on the topic of energy technologies. Materials and methods. The material under study consisted of 12,000 bibliometric records exported from the IEEE Xplore database from 2020 to 2025. Of these records, 6,000 were conference materials and 6,000 were journal articles. Results. The identified research highlights major trends in smart energy systems, emphasizing the integration of IoT, AI, and machine learning for enhanced operation and predictive maintenance. Key focus areas are smart microgrids, hydrogen energy storage, and electric transport/battery systems. A format is proposed for additional conclusions and recommendations on each issue under consideration, formulated in a semi-formal but clear descriptive style. Conclusion. The publications reflect a strong emphasis on cybersecurity, data privacy, and addressing economic and accessibility issues. Furthermore, research involves advanced topics like mathematical modeling, innovative components (e. g., varactor diodes), and thermal management to improve energy efficiency and ensure safe, modern energy infrastructure, particularly in applications like smart cities. Future research suggests utilizing the IEEE thesaurus to analyze publication trends based on term frequency in titles and abstracts.

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