Generative artificial intelligence: Pioneering a new paradigm for research and education in smart energy systems

Promoting low-carbon energy systems as a centerpiece of global sustainable development goals is essential. As part of this low-carbon transition, smart energy systems have been an active area of research and education, where artificial intelligence (AI) intersects with energy science. It is an emerging area where research and education face new challenges as new knowledge keeps coming in. During this process, generative artificial intelligence (GAI) plays a critical role in education and research activities. However, GAI's impact on smart energy systems research and education is less discussed. Especially, its impact on education is rarely discussed when compared to research. GAI reshapes both the research process and the roles of teachers and students in the course. This perspective offers insights into the ongoing research and education paradigm shifts observed in the smart energy system. This perspective synthesizes existing studies on "GAI for Science" and "GAI for Education" practices in the field of smart energy systems. In research, the impact of GAI is discussed from both macro and micro levels. In education, this perspective examines how a GAI-driven teaching approach addresses the challenges of teaching smart energy systems compared to the traditional approach. This perspective could benefit the discussion of GAI-reshaped research and education in energy science.

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Publication Details

Journal
Energy and AI
Published
2025-09-02
DOI
https://doi.org/10.1016/j.egyai.2025.100610
Citations
2
Primary Topic
Smart Grid Security and Resilience
Type
article
Field-Weighted Citation Impact
2.43

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article

Generative artificial intelligence: Pioneering a new paradigm for research and education in smart energy systems

Xiaojie Lin, Liuliu Du, Wei Zhong, Xueru Lin et al.
2 citations
Energy and AI
Smart Grid Security and Resilience
2.43
article

Generative artificial intelligence: Pioneering a new paradigm for research and education in smart energy systems

Xiaojie Lin, Liuliu Du, Wei Zhong, Xueru Lin, Haoyu Jiang, Shuai Wang, Luo Zheng, Zi‐Tao Yu, Yihui Mao, Chongshuo Yuan
article en
2 citations

Abstract

Promoting low-carbon energy systems as a centerpiece of global sustainable development goals is essential. As part of this low-carbon transition, smart energy systems have been an active area of research and education, where artificial intelligence (AI) intersects with energy science. It is an emerging area where research and education face new challenges as new knowledge keeps coming in. During this process, generative artificial intelligence (GAI) plays a critical role in education and research activities. However, GAI's impact on smart energy systems research and education is less discussed. Especially, its impact on education is rarely discussed when compared to research. GAI reshapes both the research process and the roles of teachers and students in the course. This perspective offers insights into the ongoing research and education paradigm shifts observed in the smart energy system. This perspective synthesizes existing studies on "GAI for Science" and "GAI for Education" practices in the field of smart energy systems. In research, the impact of GAI is discussed from both macro and micro levels. In education, this perspective examines how a GAI-driven teaching approach addresses the challenges of teaching smart energy systems compared to the traditional approach. This perspective could benefit the discussion of GAI-reshaped research and education in energy science.

Energy and AIVol. 22
Zhejiang Energy Research Institute (CN), Shanghai Advanced Research Institute (CN), Hangzhou Wanxiang Polytechnic (CN), Hangzhou City University, Lappeenranta-Lahti University of Technology (FI), Zhejiang University (CN)
National Natural Science Foundation of China, State Key Laboratory Of Alternate Electrical Power System With Renewable Energy Sources, National Key Research and Development Program of China
Openalex Percentile: Top 10%
Smart Grid Security and Resilience
2.43
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