Knowledge Graph for Renewable Energy Power Plant Design Using Natural Language Processing and Graph-Based Reasoning

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.