RAG-Enhanced Knowledge Base Construction for Power Grid Planning Education: A Vertical Domain Approach

This paper presents a novel approach to constructing a vertical domain knowledge base for power grid planning education using Retrieval-Augmented Generation (RAG) technology. We propose a comprehensive theoretical framework consisting of ten mathematical models to quantify the educational effectiveness of RAG-enhanced systems. Our methodology integrates semantic similarity computation, personalized learning adaptation, and cognitive load optimization to improve learning outcomes in professional education. Experimental results demonstrate that our RAG-enhanced knowledge base achieves an $88 \%$ knowledge retrieval accuracy rate, reduces learning time by $38 \%$, and improves practical application skills by $21.4 \%$. The system has been successfully implemented in the “Power Grid Planning Professional Vertical Domain Intelligent Knowledge Base Construction and Application Research” project at Shandong Electric Power College. Our findings indicate that RAG technology can significantly enhance the quality and efficiency of vocational education in the power industry.