Big Data and Artificial Intelligence Application in Energy Field: A Bibliometric Analysis

Abstract This paper uses bibliometrics to characterize the knowledge systems of big data, artificial intelligence (AI) and energy based on the Science Citation Index Extension (SCI-E) and Social Science Citation Index (SSCI) of the Web of Science from 2001 to 2020. Results show that the most influential country in the field is the United States, with an h-index of 75. The Chinese Academy of Sciences publishes the largest number of papers and plays a vital role in the collaboration network. The study also reveals that the IEEE Access and Energies are the most productive journals in terms of the number of publications, and engineering is the most popular subject. The key theoretical foundation includes deep learning, reinforcement learning, energy big data and prediction of energy consumption. The application of big data and AI in the field of energy focuses on smart grid, energy consumption and renewable energy. Early research frontiers involve optimization and prediction of energy-related problems using the genetic algorithm and neural networks. Since 2013, energy big data have gained prominence. At present, machine learning, deep learning and fog computing are frequently combined with energy saving. In the future, big data and AI will be utilized to promote the application of renewable energy and energy-saving renovation of buildings. These findings can help researchers understand the developmental trends and correctly grasp the research direction and method of the emerging interdisciplinary field. They can also assist energy enterprises in finding the right direction for technology investment.