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

Zebaze, Janice Anta; Jiomekong, Azanzi; Djuidje Kenmoe, Germaine; Vidal, Maria-Esther

doi:10.24963/ijcai.2026/954

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

Janice Anta Zebaze, Azanzi Jiomekong, Germaine Djuidje Kenmoe, Maria-Esther Vidal

Download BibTeX Cite

Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence. Doctoral Consortium. Pages 8351-8352.

https://doi.org/10.24963/ijcai.2026/954

Keywords

Data Mining: Knowledge graphs and knowledge base completion
Multidisciplinary Topics and Applications: Energy, environment and sustainability
Natural Language Processing: Question answering
Machine Learning: Neuro-symbolic methods/Abductive Learning

Abstract

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.

Janice Anta Zebaze, Azanzi Jiomekong, Germaine Djuidje Kenmoe, and Maria-Esther Vidal. 2026. Neuro-Symbolic AI for Evidence-Based Renewable Energy Planning in Sub-Saharan Africa. In Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-26. IJCAI, 8351–8352. https://doi.org/10.24963/ijcai.2026/954

@inproceedings{ijcai2026p954,
  title     = {Neuro-Symbolic AI for Evidence-Based Renewable Energy Planning in Sub-Saharan Africa},
  author    = {Zebaze, Janice Anta and Jiomekong, Azanzi and Djuidje Kenmoe, Germaine and Vidal, Maria-Esther},
  booktitle = {Proceedings of the Thirty-Fifth International Joint Conference on
               Artificial Intelligence, {IJCAI-26}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Diego Calvanese},
  pages     = {8351--8352},
  year      = {2026},
  month     = {8},
  note      = {Doctoral Consortium},
  doi       = {10.24963/ijcai.2026/954},
  url       = {https://doi.org/10.24963/ijcai.2026/954},
}

TY  - CPAPER
TI  - Neuro-Symbolic AI for Evidence-Based Renewable Energy Planning in Sub-Saharan Africa
AU  - Zebaze, Janice Anta
AU  - Jiomekong, Azanzi
AU  - Djuidje Kenmoe, Germaine
AU  - Vidal, Maria-Esther
T2  - Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-26
PB  - International Joint Conferences on Artificial Intelligence Organization
PY  - 2026
SP  - 8351
EP  - 8352
DO  - 10.24963/ijcai.2026/954
UR  - https://doi.org/10.24963/ijcai.2026/954
ER  -