RAG-Based Intelligent Literature System for Renewable Energy Assessment
Abstract
Abstract The accelerating pace of renewable energy technology development solar photovoltaics, wind turbines, battery storage, green hydrogen, and smart grid systems generates a literature that outpaces the capacity of human experts to monitor, synthesize, and apply. This paper proposes and evaluates a RAG-Based Intelligent Literature System (RILS) for Renewable Energy Technology Assessment: a specialized retrieval-augmented framework integrating dense semantic retrieval over a curated renewable energy corpus with transformer-based generation to produce structured, evidence-grounded technology assessments. The system indexes 32,800 passages from IRENA cost reports, IEA technology perspectives, peer-reviewed journals (Nature Energy, Renewable and Sustainable Energy Reviews, Energy Policy), and technology roadmaps covering six technology families from 2018 to 2024. Experimental evaluation on 900 technology assessment queries demonstrates 85% response accuracy, a 38% reduction in hallucination compared to parametric baselines, Recall@5 = 86%, MRR = 0.83, quantitative cost accuracy (QA-acc) of 79%,and knowledge freshness accuracy of 87% on rapidly evolving cost metrics. The RILS framework provides technology analysts, R&D strategists, and energy planners with a scalable, transparent, and continuously updatable intelligent literature assistant.
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