The Go-to-Market Game Changer in the Low-Carbon Energy Industry: Generative Pre-Transformer and Its Potential for Improving Efficiency and Sustainability
Conventional decision-making problem in the energy Industry requires complying with codes which requires programming ability and expert domain knowledge. The new AI technology Generative Pretrained Transformer 4 (GPT4) has exhibited human-like performance in professional activities through generated prompts instead of coding, demonstrating its potential to change the energy industry in terms of efficiency and sustainability. In this paper, how the generative AI technology of GPT could revolutionize the low-carbon energy sector is considered, particularly how it could provide unparalleled support in power system operation, smart home management, and electrical engineering education. Firstly, GPT holds promise to be an efficiency optimizer and sustainability promoter because it can be leveraged to deliver the best value for analyzing huge amounts of data from multiple sources, i.e., weather information and load demand, to provide real-time decision-making and insightful interpretations for power grid operation. Secondly, with its powerful self-learning ability, GPT could be utilized to help the household electronics systems of smart homes adapt to various human activities and environmental conditions. Thirdly, GPT could be used for communication innovations to boost meaningful conversation and avoid misunderstanding in learning complicated engineering mathematics and physical concepts, which would play a critical role in cultivating the next generation of electrical engineers. To demonstrate the potential of GPT in the Low-carbon energy industry, ChatGPT is applied to interpret the OPF solution with MATLAB for real-time decision, to forecast the solar energy with an explanation for decision support and assessment, and to boost meaningful conversation for electrical education of the electric circuit expression. Finally, future directions and limitations of GPT in the energy industry are discussed.
Authors
- Chunyue Song (ORCID: https://orcid.org/0000-0003-2196-520X)
- Chaojie Li (ORCID: https://orcid.org/0000-0002-0557-1481)
- Rui Wang (ORCID: https://orcid.org/0000-0002-7651-795X)
- Zhao Yang Dong (ORCID: https://orcid.org/0000-0001-9659-0858)
- Yi Wang (ORCID: https://orcid.org/0000-0003-1143-0666)
Institutions
- Ningbo University (CN)
- City University of Hong Kong (HK)
- UNSW Sydney (AU)
- Zhejiang University (CN)
- University of Hong Kong (HK)
Publication Details
- Journal
- IEEE Systems Man and Cybernetics Magazine
- Published
- 2025-04-01
- DOI
- https://doi.org/10.1109/msmc.2024.3440569
- Primary Topic
- Global Energy and Sustainability Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Ningbo University
- Australian Research Council