The Logic and Strategy of Large Models Empowering Power Grids to Promote Management Through Cases
- DOI
- 10.2991/978-94-6463-845-5_109How to use a DOI?
- Keywords
- Large model; Power grid management; Promote management through cases; Artificial intelligence
- Abstract
With the rapid development of artificial intelligence technology, large models are increasingly widely applied in all walks of life. As an important component of the country’s key infrastructure, power grid enterprises are confronted with multiple challenges such as improving operational efficiency and ensuring power supply security. This article aims to explore how large models can empower power grid enterprises and enhance management levels through the approach of “promoting management through cases”. The article first analyzes the application potential of large models in power grid management, then elaborates in detail the logical framework of “promoting management through cases”, proposes specific implementation strategies, and discusses potential challenges and coping strategies.
- Copyright
- © 2025 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Jiayun Shi AU - Lei Xu AU - Chengyan Huang AU - Xiaoyun Zha AU - Jin Liu PY - 2025 DA - 2025/09/16 TI - The Logic and Strategy of Large Models Empowering Power Grids to Promote Management Through Cases BT - Proceedings of the 2025 6th International Conference on Management Science and Engineering Management (ICMSEM 2025) PB - Atlantis Press SP - 1111 EP - 1126 SN - 2667-1271 UR - https://doi.org/10.2991/978-94-6463-845-5_109 DO - 10.2991/978-94-6463-845-5_109 ID - Shi2025 ER -