GridFM - Towards Foundation Models for the Electric Grid
summary
Pretrained graph models (GNN and graph transformer) reconstruct blanked bus values from synthetic datasets generated by the GridFM data kit and a physics-informed loss, then fine-tune for downstream tasks. A GNN delivers two-orders-of-magnitude compute advantage versus traditional solvers on many-case runs and achieves a 70x speed up on a Texas 200k contingency sweep with only a ~3% drop in detected line violations. Collaborative training uses the AFFL federated learning stack across DOE supercomputers in 3–4 rounds.
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chapters (7)
- 0:00Foundation models for grids
- 1:03Modeling and architecture
- 3:40Data generation and performance
- 13:32Project resources and community
- 14:40Federated learning framework
- 22:22Security and scalability
- 27:39Collaboration and future outlook
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