Exploring the capabilities and limitations of large language models in the electric energy sector-Reference-Cited by-同舟云学术

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Exploring the capabilities and limitations of large language models in the electric energy sector

Published:2024-06 Issue:6 Volume:8 Page:1544-1549
ISSN:2542-4351
Container-title:Joule
language:en
Short-container-title:Joule

Author:

Majumder Subir,Dong Lin,Doudi Fatemeh,Cai Yuting,Tian Chao,Kalathil Dileep,Ding Kevin,Thatte Anupam A.,Li Na,Xie Le^ORCID

Publisher

Elsevier BV

Reference16 articles.

1. Attention is All You Need;Vaswani,2017

2. Large Foundation Models for Power Systems;Huang;arXiv,2023

3. On the Potential of ChatGPT to Generate Distribution Systems for Load Flow Studies Using OpenDSS;Bonadia;IEEE Trans. Power Syst.,2023

4. Real-Time Optimal Power Flow With Linguistic Stipulations: Integrating GPT-Agent and Deep Reinforcement Learning;Yan;IEEE Trans. Power Syst.,2024

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Advancing building energy modeling with large language models: Exploration and case studies;Energy and Buildings;2024-11

2. Sustainable electrification in the era of AI;Nature Reviews Electrical Engineering;2024-07-31

3. Toward Cognitive Assistance and Prognosis Systems in Power Distribution Grids—Open Issues, Suitable Technologies, and Implementation Concepts;IEEE Access;2024

4. Leveraging generative AI for knowledge-driven information retrieval in the energy sector;MATEC Web of Conferences;2024

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