Foundation Models for the Electric Power Grid
- URL: http://arxiv.org/abs/2407.09434v2
- Date: Tue, 12 Nov 2024 17:49:12 GMT
- Title: Foundation Models for the Electric Power Grid
- Authors: Hendrik F. Hamann, Thomas Brunschwiler, Blazhe Gjorgiev, Leonardo S. A. Martins, Alban Puech, Anna Varbella, Jonas Weiss, Juan Bernabe-Moreno, Alexandre Blondin Massé, Seong Choi, Ian Foster, Bri-Mathias Hodge, Rishabh Jain, Kibaek Kim, Vincent Mai, François Mirallès, Martin De Montigny, Octavio Ramos-Leaños, Hussein Suprême, Le Xie, El-Nasser S. Youssef, Arnaud Zinflou, Alexander J. Belyi, Ricardo J. Bessa, Bishnu Prasad Bhattarai, Johannes Schmude, Stanislav Sobolevsky,
- Abstract summary: Foundation models (FMs) currently dominate news headlines.
We argue that an FM learning from diverse grid data and topologies could unlock transformative capabilities.
We discuss a power grid FM concept, namely GridFM, based on graph neural networks and show how different downstream tasks benefit.
- Score: 53.02072064670517
- License:
- Abstract: 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.
Related papers
- Optimal Power Grid Operations with Foundation Models [0.0]
We propose the use of AI Foundation Models (FMs) and advances in Graph Neural Networks to efficiently exploit poorly available grid data for different downstream tasks.
For capturing the grid's underlying physics, we believe that building a self-supervised model learning the power flow dynamics is a critical first step towards developing an FM for the power grid.
arXiv Detail & Related papers (2024-09-03T09:06:13Z) - SafePowerGraph: Safety-aware Evaluation of Graph Neural Networks for Transmission Power Grids [55.35059657148395]
We present SafePowerGraph, the first simulator-agnostic, safety-oriented framework and benchmark for Graph Neural Networks (GNNs) in power systems (PS) operations.
SafePowerGraph integrates multiple PF and OPF simulators and assesses GNN performance under diverse scenarios, including energy price variations and power line outages.
arXiv Detail & Related papers (2024-07-17T09:01:38Z) - Forging Vision Foundation Models for Autonomous Driving: Challenges,
Methodologies, and Opportunities [59.02391344178202]
Vision foundation models (VFMs) serve as potent building blocks for a wide range of AI applications.
The scarcity of comprehensive training data, the need for multi-sensor integration, and the diverse task-specific architectures pose significant obstacles to the development of VFMs.
This paper delves into the critical challenge of forging VFMs tailored specifically for autonomous driving, while also outlining future directions.
arXiv Detail & Related papers (2024-01-16T01:57:24Z) - The Role of Federated Learning in a Wireless World with Foundation Models [59.8129893837421]
Foundation models (FMs) are general-purpose artificial intelligence (AI) models that have recently enabled multiple brand-new generative AI applications.
Currently, the exploration of the interplay between FMs and federated learning (FL) is still in its nascent stage.
This article explores the extent to which FMs are suitable for FL over wireless networks, including a broad overview of research challenges and opportunities.
arXiv Detail & Related papers (2023-10-06T04:13:10Z) - Transformer-Empowered 6G Intelligent Networks: From Massive MIMO
Processing to Semantic Communication [71.21459460829409]
We introduce an emerging deep learning architecture, known as the transformer, and discuss its potential impact on 6G network design.
Specifically, we propose transformer-based solutions for massive multiple-input multiple-output (MIMO) systems and various semantic communication problems in 6G networks.
arXiv Detail & Related papers (2022-05-08T03:22:20Z) - Knowledge- and Data-driven Services for Energy Systems using Graph
Neural Networks [0.9809636731336702]
We propose a data- and knowledge-driven probabilistic graphical model for energy systems based on the framework of graph neural networks (GNNs)
The model can explicitly factor in domain knowledge, in the form of grid topology or physics constraints, thus resulting in sparser architectures and much smaller parameters dimensionality.
Results obtained from a real-world smart-grid demonstration project show how the GNN was used to inform grid congestion predictions and market bidding services.
arXiv Detail & Related papers (2021-03-12T13:00:01Z) - Learning Discrete Energy-based Models via Auxiliary-variable Local
Exploration [130.89746032163106]
We propose ALOE, a new algorithm for learning conditional and unconditional EBMs for discrete structured data.
We show that the energy function and sampler can be trained efficiently via a new variational form of power iteration.
We present an energy model guided fuzzer for software testing that achieves comparable performance to well engineered fuzzing engines like libfuzzer.
arXiv Detail & Related papers (2020-11-10T19:31:29Z) - Smart Grid: A Survey of Architectural Elements, Machine Learning and
Deep Learning Applications and Future Directions [0.0]
Big data analytics, machine learning (ML), and deep learning (DL) plays a key role when it comes to the analysis of this massive amount of data and generation of valuable insights.
This paper explores and surveys the Smart grid architectural elements, machine learning, and deep learning-based applications and approaches in the context of the Smart grid.
arXiv Detail & Related papers (2020-10-16T01:40:24Z) - From Data to Knowledge to Action: Enabling the Smart Grid [0.11726720776908521]
"The Grid" is a relic based in many respects on century-old technology.
Many people are pinning their hopes on the "smart grid"
Initial plans for the smart grid suggest it will make extensive use of existing information technology.
arXiv Detail & Related papers (2020-07-31T19:43:48Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.