Knowledge Graph for Renewable Energy Power Plant Design Using Natural Language Processing and Graph-Based Reasoning
再エネ発電所設計のための知識グラフ：自然言語処理とグラフベース推論を用いて (AI 翻訳)
Rishitha Rasineni
🤖 gxceed AI 要約
日本語
再エネ発電所設計を支援する知識グラフ（KG）フレームワークを提案。Neo4jとBERTベースの固有表現抽出（EnergyNER）、Flan-T5モデル（CypherT5）を用いて自然言語からCypherクエリへの変換を実現。9種のエネルギー源と8地域のデータを統合し、EnergyNER F1=0.871、CypherT5精度84.6%を達成。フルスタックアプリケーションとして提供。
English
Presents a Knowledge Graph framework for renewable energy power plant design, integrating Neo4j, BERT-based NER (EnergyNER), and a Flan-T5 model for NL-to-Cypher translation. Encodes 9 energy sources across 8 regions with IRENA/NREL/IPCC data. EnergyNER F1=0.871; CypherT5 achieves 84.6% exact-match accuracy, outperforming GPT-3.5. Full-stack application with AI recommendation, NLP extraction, and graph explorer.
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
日本では再エネ導入拡大が進む中、本ツールは地域特性に応じた発電所設計の効率化に貢献し得る。特に地理的・資源的制約の多い日本での再エネ計画に応用が期待される。
In the global GX context
Globally, this work demonstrates how AI and knowledge graphs can accelerate renewable energy deployment by enabling natural language-driven design queries. It integrates authoritative datasets and benchmarks, offering a scalable approach for energy planners.
👥 読者別の含意
🔬研究者:Useful for researchers in AI for energy systems, demonstrating a practical KG-based decision support tool for renewable plant design.
🏢実務担当者:Corporate sustainability teams can leverage this tool to quickly explore renewable site options and design parameters.
📄 Abstract（原文）
The transition toward sustainable energy systems demands intelligent decision-support tools capable of reasoning across heterogeneous data. This paper presents a Knowledge Graph (KG) framework for renewable energy power plant design integrating Neo4j graph storage, BERTbased Named Entity Recognition (EnergyNER), and a finetuned Flan-T5 model (CypherT5) for natural language to Cypher query translation. The KG encodes nine energy source types across eight geographic contexts with quantitative attributes from IRENA, NREL, and IPCC benchmarks. EnergyNER achieves F1 = 0.871; CypherT5 achieves 84.6% exact-match and 90.4% execution accuracy on a 50-question benchmark, outperforming zero-shot GPT3.5-turbo at ~75ms local inference. A programmatic training data generation methodology produces 230 NL-Cypher pairs without manual annotation. The system is delivered as a full-stack Query,React/FastAPI application with AI Recommendation, NLP Extraction, and Graph Explorer modules.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.5281/zenodo.19602677first seen 2026-05-15 16:47:10
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