LF Energy Summit Europe 2026

EnerGNN is an open-source python package that aims at bridging the gap between research on Graph Neural Networks (GNNs) and industrial operation of energy networks.

This package includes:
- A faithful and adaptive data representation tailored for large scale industrial systems made of interacting entities.
- A modular library of GNN implementations designed to handle such data.
- A clear interface for business-driven use-case.
- A modular training loop for training GNNs on your specific use-case.

It is the result of 8+ years of research at RTE (Réseau de Transport d'Électricité) and academical partners (Université Paris-Saclay & Université de Liège). It is currently being used for multiple use-cases at RTE, and has shown its ability to scale to the full HV-EHV French system, and to output meaningful and high-precision predictions.

This presentation aims at showing the potential of our open-source package EnerGNN, and to attract potential users and contributors to help us on the following topics :
- GNN acceleration, robustification and improvement,
- Application of EnerGNN to new use cases,
- Extension of EnerGNN to other industrial domains.