Neuro-Symbolic AI for Evidence-Based Renewable Energy Planning in Sub-Saharan Africa

摘要

Renewable energy is a key alternative to fossil fuels and their environmental harms. However, sub-Saharan African countries remain behind in their implementation, due to poor investment in the appropriate resource, among other causes. This information is dispersed as unstructured knowledge across scientific papers, making the task manually demanding for decision and policy makers. In this work, we combine the strength of Large Language Models (LLMs) and structured knowledge (Knowledge Graphs) in a neuro-symbolic framework to tackle this issue. We implement this approach in the hydropower field, building a question-answering system that uses Retrieval Augmented Generation (RAG) on a knowledge graph. Using LLMs such as LLaMA 3.1 8B, LLaMA 3.3 70B, LLaMA 4 Scout 17B, and Kimi K2, we observe an improvement in exact match of 48 − 60% across all models. These results support the ability of this framework to participate in evidence-based energy planning in developing countries.