The development and application of renewable energy technologies have increased in recent years due to the need for energy and the government’s drive to combat climate change. The use of renewable energy presents advantages and challenges that in most cases are solved through product and service innovation. A topical tool used in renewable energy applications is artificial intelligence (AI), which helps address the challenges of the clean energy sector. The objective of the work is to identify the patterns of innovation in renewable energy and artificial intelligence, and a bibliometric analysis was performed to determine the publications, citations, authors, keywords, subjects, journals, countries, and dynamics of collaboration in the field of study. The data were obtained from publications indexed in the Scopus database. Information is displayed in graphs and maps that allow to visually identify trends in the field of knowledge, and tools such as excel solver, VOSviewer, Sankey, and chord diagrams will be used. The rate of publications has grown exponentially from 2018 to 2023, having a rate of 0.039 years−1, also the type of review document has the highest TC/TP ratio, and the most cited author is Tabor, Daniel P. The subject engineering, computer sciences, and energy are the three main fields of knowledge, and the most productive countries in publications are India, the United States, and China. The top 25 documents with the highest number of citations (91.7%) correspond to quartile 1 journals. The keywords related to artificial intelligence are machine learning, internet of things, learning systems, decision support systems, and big data applied to renewable energy. This work may be of interest to students, researchers, academics, and decision-makers in companies related to the renewable energy sector and is the first study to implement bibliometric analysis in combination with innovation and artificial intelligence.
