mlsanityData to 14 Oct 2026

Foundation Models for the Electric Power Grid

arXiv 2407.09434 ↗First posted 12 Jul 2024cs.LG cs.AI cs.CEcited by 28

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Abstract (arXiv)

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.

Key facts. Cited by 28 papers in the map (data through 14 Oct 2026). Builds on 41 of its 107 references that are in the map. First posted on arXiv 12 Jul 2024.

Builds on

41 of 107 references.

References · 41

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
  3. 2021On the Opportunities and Risks of Foundation Modelscited by 4,033
  4. 2017Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layercited by 3,509
  5. 2019Advances and Open Problems in Federated Learningcited by 3,046
  6. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,919
  7. 2023A Survey of Large Language Modelscited by 2,576
  8. 2022Are Transformers Effective for Time Series Forecasting?cited by 1,424
  9. 2022FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operatorscited by 776
  10. 2022GraphMAE: Self-Supervised Masked Graph Autoencoderscited by 448
  11. 2023ClimaX: A foundation model for weather and climatecited by 337
  12. 2021Pre-Trained Models: Past, Present and Futurecited by 319
29 more
  1. 2022GraphCast: Learning skillful medium-range global weather forecastingcited by 267
  2. 2023GenCast: Diffusion-based ensemble forecasting for medium-range weathercited by 177
  3. 2022Forecasting Global Weather with Graph Neural Networkscited by 168
  4. 2023Foundation Models for Decision Making: Problems, Methods, and Opportunitiescited by 164
  5. 2023Foundation Models for Generalist Geospatial Artificial Intelligencecited by 149
  6. 2023GraphMAE2: A Decoding-Enhanced Masked Self-Supervised Graph Learnercited by 132
  7. 2024Granite Code Models: A Family of Open Foundation Models for Code Intelligencecited by 117
  8. 2023Scaling transformer neural networks for skillful and reliable medium-range weather forecastingcited by 110
  9. 2024Position: Graph Foundation Models are Already Herecited by 86
  10. 2017pandapower - an Open Source Python Tool for Convenient Modeling, Analysis and Optimization of Electric Power Systemscited by 85
  11. 2022Graph Neural Networks for Link Prediction with Subgraph Sketchingcited by 81
  12. 2019Physics-Informed Neural Networks for Power Systemscited by 80
  13. 2019The Power Grid Library for Benchmarking AC Optimal Power Flow Algorithmscited by 65
  14. 2022A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinkingcited by 56
  15. 2021Physics-Informed Neural Networks for AC Optimal Power Flowcited by 54
  16. 2018Chiron: Privacy-preserving Machine Learning as a Servicecited by 40
  17. 2024Exploring the Capabilities and Limitations of Large Language Models in the Electric Energy Sectorcited by 30
  18. 2024CANOS: A Fast and Scalable Neural AC-OPF Solver Robust To N-1 Perturbationscited by 27
  19. 2023Dynamical Tests of a Deep-Learning Weather Prediction Modelcited by 24
  20. 2024OPFData: Large-scale datasets for AC optimal power flow with topological perturbationscited by 24
  21. 2023AI Foundation Models for Weather and Climate: Applications, Design, and Implementationcited by 21
  22. 2022APPFL: Open-Source Software Framework for Privacy-Preserving Federated Learningcited by 20
  23. 2024PowerGraph: A power grid benchmark dataset for graph neural networkscited by 19
  24. 2020High-Fidelity Machine Learning Approximations of Large-Scale Optimal Power Flowcited by 13
  25. 2023Physics Informed Neural Networks for Phase Locked Loop Transient Stability Assessmentcited by 3
  26. 2023Physics-Informed Graph Neural Network for Dynamic Reconfiguration of Power Systemscited by 2
  27. 2024Datasets of Great Britain Primary Substations Integrated with Household Heating Informationcited by 2
  28. 2014Decentralized Optimal Dispatch of Photovoltaic Inverters in Residential Distribution Systemscited by 1
  29. 2021Consumer, Commercial and Industrial IoT (In)Security: Attack Taxonomy and Case Studiescited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 4; 2025: 5; 2026: 19.

28 papers.

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  1. 2024ControlAgent: Automating Control System Design via Novel Integration of LLM Agents and Domain Expertisecited by 21
  2. 2026LUMINA: Foundation Models for Topology Transferable ACOPFcited by 8
  3. 2025Towards Generalization of Graph Neural Networks for AC Optimal Power Flowcited by 6
  4. 2026GENCO - A Unified Neural Solver Embedded in a Development Framework for Steady-State Grid Analysiscited by 6
  5. 2025gridfm-datakit-v1: A Python Library for Scalable and Realistic Power Flow and Optimal Power Flow Data Generationcited by 3
  6. 2024Optimal Power Grid Operations with Foundation Modelscited by 2
  7. 2025Physics-informed GNN for medium-high voltage AC power flow with edge-aware attention and line search correction operatorcited by 2
  8. 2026LUMINA: A Grid Foundation Model for Benchmarking AC Optimal Power Flow Surrogate Learningcited by 2
  9. 2024Diffusion Model-based Parameter Estimation in Dynamic Power Systemscited by 1
  10. 2026Residual Power Flow for Neural Solverscited by 1
  11. 2026Parameter-Efficient Domain Adaptation of Physics-Informed Self-Attention based GNNs for AC Power Flow Predictioncited by 1
  12. 2026Towards Systematic Generalization for Power Grid Optimization Problemscited by 1
16 more
  1. 2026MxGPS: Multiplex Graph Transformers for a Power Grid Foundation Modelcited by 1
  2. 2026Revisiting data-driven dynamic security assessment with a tabular foundation modelcited by 1
  3. 2024Accelerating Quasi-Static Time Series Simulations with Foundation Modelscited by 0
  4. 2025ElectriQ: A Benchmark for Assessing the Response Capability of Large Language Models in Power Marketingcited by 0
  5. 2025Synergies between Federated Foundation Models and Smart Power Gridscited by 0
  6. 2026Empowering All-in-Loop Health Management of Spacecraft Power System in the Mega-Constellation Era via Human-AI Collaborationcited by 0
  7. 2026Differentiable Power-Flow Optimizationcited by 0
  8. 2026Scalable Heterogeneous Graph Foundation Models for Data-Driven Optimal Power Flow in Smart Gridscited by 0
  9. 2026Neural Controlled Differential Equations for EMT-Level Surrogate Modeling of Grid-Forming Inverterscited by 0
  10. 2026LLMs and Agentic AI Systems for Smart Grids: A Tutorial on Architectures and Applicationscited by 0
  11. 2026Graph Machine Learning: An Opportunity for Power Systemscited by 0
  12. 2026Scalable Self-Supervised Learning for Multiphase AC-OPF in Distribution Systems with Topology Reconfigurationcited by 0
  13. 2026Scaling Laws for Physics-Aware ACOPF Surrogate Learningcited by 0
  14. 2026Hierarchical GNNs for power flow: letting physics shape the hierarchycited by 0
  15. 2026Mycelium: A Generalizable Cross-Grid Multi-Task Model for Electrical Distribution Systemscited by 0
  16. 2026Learning by Examples: Power Grid Steady State Analysis with a Tabular Foundation Modelcited by 0