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Big data and artificial intelligence application in energy field: a bibliometric analysis

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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 China is the country with the highest number of publications (1115), accounting for 29% of the total; however, the most influential country in the field is the USA, with an h-index of 75. The Chinese Academy of Sciences publishes the largest number of papers (104) and plays a vital role in the collaboration network. The study also reveals that the IEEE Access is the most productive journal (195) in terms of the number of publications, and engineering is the most popular discipline (1526). The key theoretical foundation includes deep learning (293), big data (105), energy consumption (79), and reinforcement learning (40). 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.

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Data availability

The datasets used and analyzed during the current study can be provided on reasonable request.

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Funding

This study is grateful for financial support provided by the National Natural Science Foundation of China (grant numbers: 52270183).

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Authors and Affiliations

  1. College of Information Engineering, Nanjing Xiaozhuang University, Nanjing, 211171, China

    Yali Hou

  2. College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China

    Qunwei Wang

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  1. Yali Hou
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Yali Hou contributed to data curation, formal analysis, writing—original draft. Qunwei Wang contributed to conceptualization, methodology.

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Correspondence to Qunwei Wang.

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Hou, Y., Wang, Q. Big data and artificial intelligence application in energy field: a bibliometric analysis. Environ Sci Pollut Res 30, 13960–13973 (2023). https://doi.org/10.1007/s11356-022-24880-y

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