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

The rapid rise in energy consumption by artificial intelligence (AI) systems necessitates advancements in energy technologies. Training large AI models, like GPT, demands considerable computing power, overwhelming data centers and straining energy systems due to the use of energy-intensive hardware. Consequently, high energy usage poses a significant limitation on AI development and scalability. IEEE Xplore has been chosen for bibliometric analysis in energy technologies because of its emphasis on electrical engineering and computer science. 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. One of the goals of the study is to propose a format for additional conclusions and recommendations on each issue considered, formulated in a semi-formal but personalized style. 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. 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. 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. For further research, it is proposed to use the expandable IEEE thesaurus for analyzing publications, with a particular focus on the frequency of term occurrences in titles and abstracts.