用大模型+图神经网络优化电动车充电，提升电网稳定性。
Optimizing Electric Vehicles Charging using Large Language Models and Graph Neural Networks
- 结合大模型与图神经网络处理充电序列与关系信息
- 相比传统方法显著提升充电优化效果
- 适合电网管理、智能交通系统研究者参考
在电动汽车广泛普及的背景下，维持电网稳定对可持续交通至关重要。传统优化方法和强化学习（RL）在应对实时充电的高维性与动态性时表现不佳，常导致次优解。本研究证明，将大语言模型（LLMs）用于序列建模，结合图神经网络（GNNs）提取关系信息，不仅优于传统智能充电方法，还为全新研究方向与创新解决方案开辟了道路。
原文摘要 · Abstract (English)
Maintaining grid stability amid widespread electric vehicle (EV) adoption is vital for sustainable transportation. Traditional optimization methods and Reinforcement Learning (RL) approaches often struggle with the high dimensionality and dynamic nature of real-time EV charging, leading to sub-optimal solutions. To address these challenges, this study demonstrates that combining Large Language Models (LLMs), for sequence modeling, with Graph Neural Networks (GNNs), for relational information extraction, not only outperforms conventional EV smart charging methods, but also paves the way for entirely new research directions and innovative solutions.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。