Large-language-model-driven multiple algorithm evolution for network-constrained unit commitment model reduction

Yanhong Liu

doi:10.1016/j.apenergy.2026.128745

2026-09-01

PRE

钟海旺 (Haiwang Zhong) *

Chongqing Kang

DOI：10.1016/j.apenergy.2026.128745

原文链接

摘要

En 中文

• 针对NCUC模型降阶问题，开发了一种基于大型语言模型（LLM）的多算法协同进化方法。 • 通过引入代码修复策略和领域特定知识嵌入，增强了算法的进化能力。 • 将现有NCUC模型降阶算法与LLM进行系统性融合，以提高算法的泛化能力、准确性和计算效率。 • 集成了不可行解修复算法，以确保模型降阶后的解的可行性。

期刊

Applied Energy

IF:

论文数:

2.6W

被引数:

19.9W

机构

Tsinghua University

学者数:

8.7K

论文数: 3.0K

被引数: 9

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