Khan Kanwal Iqbal, Nasir Adeel
Department of Management Sciences, University of Engineering and Technology, New Campus, Kala Shah Kaku, Pakistan.
Department of Management Sciences, Lahore College for Women University, Lahore, Pakistan.
Environ Sci Pollut Res Int. 2023 May;30(24):64845-64859. doi: 10.1007/s11356-023-27038-6. Epub 2023 Apr 25.
Environmental pollution has become a significant concern of nations. International organizations, local authorities, and social activists try to achieve sustainable development goals (SDGs) to protect the environment. However, this cannot be achieved without acknowledging the role of advanced technology applications. Previous studies found a significant relationship between technology and energy resources. But the need to highlight the significance of artificial intelligence (AI) in dealing with inevitable environmental issues still requires more attention. This study aims to analyze the application of AI applications in predicting, developing, and implementing wind and solar energy resources through a bibliometric analysis from 1991 to 2022. It uses bilioshiny of the "bibliometrix 3.0" package of R-programming for influential core aspects and keyword analysis and VOSviewer for co-occurrence analysis. The study provides significant implications for core authors, documents, sources, affiliations, and countries. It also provides keyword analysis and a co-occurrence network to cope with the conceptual integration of the literature. It reports three significant streams of literature in clusters: AI optimization and renewable energy resources; smart renewable energy resource challenges and opportunities; deep learning and machine learning forecasting; and energy efficiency. The findings will uncover the strategic perspective of AI technology for wind and solar energy generation projects.
环境污染已成为各国关注的重大问题。国际组织、地方当局和社会活动家努力实现可持续发展目标（SDGs）以保护环境。然而，如果不承认先进技术应用的作用，这一目标就无法实现。先前的研究发现技术与能源资源之间存在显著关系。但仍需更多关注人工智能（AI）在应对不可避免的环境问题方面的重要性。本研究旨在通过对1991年至2022年的文献计量分析，剖析人工智能应用在预测、开发和实施风能及太阳能资源方面的应用情况。它使用R编程的“bibliometrix 3.0”包中的bilioshiny进行有影响力的核心方面和关键词分析，并使用VOSviewer进行共现分析。该研究对核心作者、文献、来源、机构和国家具有重要意义。它还提供关键词分析和共现网络，以应对文献的概念整合。它报告了聚类中的三个重要文献流：人工智能优化与可再生能源资源；智能可再生能源资源挑战与机遇；深度学习和机器学习预测；以及能源效率。研究结果将揭示人工智能技术在风能和太阳能发电项目中的战略视角。
