A Perspective on Foundation Models for the Electric Power Grid
<|reference_start|>A Perspective on Foundation Models for the Electric Power Grid: Foundation models (FMs) currently dominate news headlines. They employ advanced deep learning architectures to extract structural information autonomously from vast datasets through self-supervision. The resulting rich representations of complex systems and dynamics can be applied to many downstream applications. Therefore, FMs can find uses in electric power grids, challenged by the energy transition and climate change. In this paper, we call for the development of, and state why we believe in, the potential of FMs for electric grids. We highlight their strengths and weaknesses amidst the challenges of a changing grid. We argue that an FM learning from diverse grid data and topologies could unlock transformative capabilities, pioneering a new approach in leveraging AI to redefine how we manage complexity and uncertainty in the electric grid. Finally, we discuss a power grid FM concept, namely GridFM, based on graph neural networks and show how different downstream tasks benefit.<|reference_end|>
@article{hamann2024foundation,
    title={Foundation Models for the Electric Power Grid},
    author={Hendrik F. Hamann, Thomas Brunschwiler, Blazhe Gjorgiev, Leonardo S.
  A. Martins, Alban Puech, Anna Varbella, Jonas Weiss, Juan Bernabe-Moreno,
  Alexandre Blondin Mass'e, Seong Choi, Ian Foster, Bri-Mathias Hodge, Rishabh
  Jain, Kibaek Kim, Vincent Mai, Franc{c}ois Mirall`es, Martin De Montigny,
  Octavio Ramos-Lea~nos, Hussein Supr^eme, Le Xie, El-Nasser S. Youssef,
  Arnaud Zinflou, Alexander J. Belyi, Ricardo J. Bessa, Bishnu Prasad
  Bhattarai, Johannes Schmude, Stanislav Sobolevsky},
    journal={arXiv preprint arXiv:2407.09434},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.09434},
    primaryClass={cs.LG cs.AI cs.CE cs.SY eess.SY}
}