A Bibliometric Assessment of AI, IoT, Blockchain, and Big Data in Renewable Energy-Oriented Power Systems - Manuel Jaramillo; Diego Carrión; Jorge Muñoz; Luis Tipán (Energies)
This study presents a systematic bibliometric review of digital innovations in renewable energy-oriented power systems, with a focus on Blockchain, Artificial Intelligence (AI), the Internet of Things (IoT), and Data Analytics. The objective is to evaluate the research landscape, trends, and integration potential of these technologies within sustainable energy infrastructures. Peer-reviewed journal articles published between 2020 and 2025 were retrieved from Scopus using a structured search strategy. A total of 23,074 records were initially identified and filtered according to inclusion criteria based on relevance, peer-review status, and citation impact. No risk of bias assessment was applicable due to the nature of the study. The analysis employed bibliometric and keyword clustering techniques using VOSviewer and MATLAB to identify publication trends, citation patterns, and technology-specific application areas. AI emerged as the most studied domain, peaking with 1209 papers and 15,667 citations in 2024. IoT and Data Analytics followed in relevance, contributing to real-time system optimization and monitoring. Blockchain, while less frequent, is gaining traction in secure decentralized energy markets. Limitations include possible indexing delays affecting 2025 trends and the exclusion of gray literature. This study offers actionable insights for researchers and policymakers by identifying converging research fronts and recommending areas for regulatory, infrastructural, and collaborative focus. This review was not pre-registered. Funding was provided by the Universidad Politécnica Salesiana under project code 005-01-2025-02-07.