Foundation Models for the Electric Power Grid
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arXiv
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2024
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| author | Hamann, Hendrik F. Brunschwiler, Thomas Gjorgiev, Blazhe Martins, Leonardo S. A. Puech, Alban Varbella, Anna Weiss, Jonas Bernabe-Moreno, Juan Massé, Alexandre Blondin Choi, Seong Foster, Ian Hodge, Bri-Mathias Jain, Rishabh Kim, Kibaek Mai, Vincent Mirallès, François De Montigny, Martin Ramos-Leaños, Octavio Suprême, Hussein Xie, Le Youssef, El-Nasser S. Zinflou, Arnaud Belyi, Alexander J. Bessa, Ricardo J. Bhattarai, Bishnu Prasad Schmude, Johannes Sobolevsky, Stanislav |
| author_facet | Hamann, Hendrik F. Brunschwiler, Thomas Gjorgiev, Blazhe Martins, Leonardo S. A. Puech, Alban Varbella, Anna Weiss, Jonas Bernabe-Moreno, Juan Massé, Alexandre Blondin Choi, Seong Foster, Ian Hodge, Bri-Mathias Jain, Rishabh Kim, Kibaek Mai, Vincent Mirallès, François De Montigny, Martin Ramos-Leaños, Octavio Suprême, Hussein Xie, Le Youssef, El-Nasser S. Zinflou, Arnaud Belyi, Alexander J. Bessa, Ricardo J. Bhattarai, Bishnu Prasad Schmude, Johannes Sobolevsky, Stanislav |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_09434 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Foundation Models for the Electric Power Grid Hamann, Hendrik F. Brunschwiler, Thomas Gjorgiev, Blazhe Martins, Leonardo S. A. Puech, Alban Varbella, Anna Weiss, Jonas Bernabe-Moreno, Juan Massé, Alexandre Blondin Choi, Seong Foster, Ian Hodge, Bri-Mathias Jain, Rishabh Kim, Kibaek Mai, Vincent Mirallès, François De Montigny, Martin Ramos-Leaños, Octavio Suprême, Hussein Xie, Le Youssef, El-Nasser S. Zinflou, Arnaud Belyi, Alexander J. Bessa, Ricardo J. Bhattarai, Bishnu Prasad Schmude, Johannes Sobolevsky, Stanislav Machine Learning Artificial Intelligence Computational Engineering, Finance, and Science Systems and Control 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. |
| title | Foundation Models for the Electric Power Grid |
| topic | Machine Learning Artificial Intelligence Computational Engineering, Finance, and Science Systems and Control |
| url | https://arxiv.org/abs/2407.09434 |