RAG-Based Intelligent Literature System for Renewable Energy Assessment
再生可能エネルギー評価のためのRAGベース知的文献システム (AI 翻訳)
Orabi A, Harb H, Khedr A
🤖 gxceed AI 要約
日本語
本論文は、再生可能エネルギー技術評価のためのRAGベースの文献システム（RILS）を提案する。32,800のパッセージを索引付け、900のクエリで85%の精度を達成。技術コストや性能の迅速な評価を可能にし、技術アナリストやエネルギー計画者にスケーラブルな支援を提供する。
English
This paper proposes a RAG-based intelligent literature system (RILS) for renewable energy technology assessment. It indexes 32,800 passages from major sources and achieves 85% response accuracy on 900 queries, enabling rapid and transparent technology evaluations for analysts and planners.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本では再生可能エネルギーの導入拡大が進むが、技術評価の効率化は重要な課題。本システムは日本の政策や規制に特化していないが、横断的な知見として技術戦略や研究開発の迅速化に寄与する可能性がある。
In the global GX context
Globally, this system addresses the challenge of keeping up with fast-evolving renewable energy technologies. It provides a scalable, evidence-based tool for technology assessment, relevant for energy transition planning and investment decisions, though not directly tied to disclosure frameworks.
👥 読者別の含意
🔬研究者:A useful methodology for applying RAG to domain-specific literature review in renewable energy.
🏢実務担当者:Energy analysts and R&D strategists can leverage this system to quickly assess technology costs, maturity, and trends.
📄 Abstract（原文）
<title>Abstract</title> <p>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.</p>
🔗 Provenance — このレコードを発見したソース
- Research Square https://doi.org/10.21203/rs.3.rs-10004288/v1first seen 2026-06-16 04:29:51
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