Artificial intelligence in renewable energy: A comprehensive bibliometric analysis

• Published in: Energy reports Vol. 8; pp. 14072 - 14088
• Main Authors: Zhang, Lili, Ling, Jie, Lin, Mingwei
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01-11-2022; Elsevier
• Subjects: Artificial intelligence; Bibliometric analysis; Renewable energy; Visualization; Visualization; Renewable energy; Bibliometric analysis; Artificial intelligence
• ISSN: 2352-4847; 2352-4847
• DOI: 10.1016/j.egyr.2022.10.347

In recent years, artificial intelligence methods have been widely applied to solve issues related to renewable energy because of their ability to solve nonlinear and complex data structures. In this paper, we provide a comprehensive bibliometric analysis to better understand the evolution of Artificial Intelligence in Renewable Energy (AI&RE) research from 2006 to 2022. This study is performed based on the Web of Science Core Collection Database, and a dataset of 469 publications have been retrieved. This paper uses VOS viewer, CiteSpace, and Bibliometrix to perform bibliometric analysis and science mapping. The analysis results show that China is the most productive and influential country/region, with the widest range of collaborative partners. The study reveals that AI-related technologies can effectively solve issues related to integrating renewable energy with power system, such as solar and wind forecasting, power system frequency analysis and control, and transient stability assessment. In addition, future research trends are discussed. This paper helps scholars to understand the evolution of AI&RE research from a bibliometric perspective and inspires them to think about the field through multiple aspects.