用AI自动解读电网规范，提升合规效率
GridCodex: A RAG-Driven AI Framework for Power Grid Code Reasoning and Compliance
- 基于大模型与检索增强生成，分阶段优化查询和检索
- 答案质量提升26.4%，召回率提高10倍以上
- 适合能源企业、电网监管与AI合规研究者
全球向可再生能源转型带来前所未有的挑战，电力行业对法规推理与合规的需求日益关键。电网规范是管理电网运行的规则，复杂且缺乏自动化解读方案，阻碍行业发展并影响企业盈利。我们提出GridCodex，一个端到端的电网规范推理与合规框架，结合大语言模型与检索增强生成（RAG）。通过多阶段查询优化与RAPTOR增强检索，改进传统RAG流程。在涵盖多个监管机构的全面基准测试中验证有效性，包括多维度自动化答案评估。实验结果表明，答案质量提升26.4%，召回率提高超过10倍。消融研究进一步分析了基础模型选择的影响。
原文摘要 · Abstract (English)
The global shift towards renewable energy presents unprecedented challenges for the electricity industry, making regulatory reasoning and compliance increasingly vital. Grid codes, the regulations governing grid operations, are complex and often lack automated interpretation solutions, which hinders industry expansion and undermines profitability for electricity companies. We introduce GridCodex, an end to end framework for grid code reasoning and compliance that leverages large language models and retrieval-augmented generation (RAG). Our framework advances conventional RAG workflows through multi stage query refinement and enhanced retrieval with RAPTOR. We validate the effectiveness of GridCodex with comprehensive benchmarks, including automated answer assessment across multiple dimensions and regulatory agencies. Experimental results showcase a 26.4% improvement in answer quality and more than a 10 fold increase in recall rate. An ablation study further examines the impact of base model selection.
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