Leveraging RAG for Reasoning and Analyses of Electricity Distribution Codes

Generation (RAG)-based semantic analysis framework for electricity codes, providing quantitative and qualitative comparisons across countries, and showcasing the system’s potential to accelerate understanding and decision-making in power grid management; (2)

Identifying the differences between three codes and show the impact of our RAG-approach.

Results demonstrate that Country#2 code leads with a high average similarity (0.289) and full coverage above threshold, indicating the system found rich, focused content relevant to planning. Country#1 code scores low similarity (0.0593) and zero coverage, reflecting fewer relevant passages and broader, less focused retrievals. Country#3 is intermediate, with moderate similarity (0.1413) and stronger coherence but limited relevant coverage. Moreover, the results also reveals that the system captures key differences. Country#1 code centers on planning but lacks detailed forecasting and digital integration; Country#2 balances planning, forecasting, and renewable guidelines; Country#3 enforces long-term horizons and strict standards with heavier regulatory complexity. These patterns reflect how the RAG system effectively distinguishes semantic richness and document focus across diverse codes.

This RAG-based approach dramatically accelerates understanding of complex regulatory texts, transforming dense documents into accessible insights. By automating semantic retrieval and interpretation, it empowers policymakers and engineers to make faster, better-informed decisions, fostering smarter, more resilient power grid management.