Speakers
Hendrik Hamann
(IBM)
Thomas Brunschwiler
(IBM Research)
Description
Foundation models (FMs), pre-trained on large datasets and adapted to a broad set of applications, are revolutionizing the field of artificial intelligence (AI). Powerful FMs for language and weather have recently emerged, proving that such models can understand complex physical systems. In this presentation we review a moonshot concept, which are FMs for the electric grid (GridFMs). In an initial implementation, we introduce a GridFM pretrained on load flow data. We show good reconstruction performances and demonstrate that a GridFM pretrained on various grid topologies generalizes well to new grids. Finally, we discuss downstream applications, future development, roadmaps and opportunities of GridFM.
