mlsanityData to 13 Oct 2026

Large Foundation Models for Power Systems

arXiv 2312.07044 ↗First posted 12 Dec 2023eess.SY cs.LG cs.SYcited by 17

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Abstract (arXiv)

Foundation models, such as Large Language Models (LLMs), can respond to a wide range of format-free queries without any task-specific data collection or model training, creating various research and application opportunities for the modeling and operation of large-scale power systems. In this paper, we outline how such large foundation model such as GPT-4 are developed, and discuss how they can be leveraged in challenging power and energy system tasks. We first investigate the potential of existing foundation models by validating their performance on four representative tasks across power system domains, including the optimal power flow (OPF), electric vehicle (EV) scheduling, knowledge retrieval for power engineering technical reports, and situation awareness. Our results indicate strong capabilities of such foundation models on boosting the efficiency and reliability of power system operational pipelines. We also provide suggestions and projections on future deployment of foundation models in power system applications.

Key facts. Cited by 17 papers in the map (data through 13 Oct 2026). Builds on 16 of its 33 references that are in the map. First posted on arXiv 12 Dec 2023.

Builds on

16 of 33 references.

References · 16

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  2. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,588
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  11. 2023A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPTcited by 616
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4 more
  1. 2023Towards Generic Anomaly Detection and Understanding: Large-scale Visual-linguistic Model (GPT-4V) Takes the Leadcited by 40
  2. 2019Machine Learning for AC Optimal Power Flowcited by 24
  3. 2023A Human-on-the-Loop Optimization Autoformalism Approach for Sustainabilitycited by 2
  4. 2021State-of-Charge Aware EV Chargingcited by 1

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2024: 4; 2025: 10; 2026: 3.

17 papers.

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  1. 2024Towards Urban General Intelligence: A Review and Outlook of Urban Foundation Modelscited by 23
  2. 2024Enhancing LLMs for Power System Simulations: A Feedback-driven Multi-agent Frameworkcited by 17
  3. 2024RL2: Reinforce Large Language Model to Assist Safe Reinforcement Learning for Energy Management of Active Distribution Networkscited by 9
  4. 2024Enabling Large Language Models to Perform Power System Simulations with Previously Unseen Tools: A Case of Dalinecited by 7
  5. 2025Large Language Model Powered Automated Modeling and Optimization of Active Distribution Network Dispatch Problemscited by 7
  6. 2025PowerGraph-LLM: Novel Power Grid Graph Embedding and Optimization with Large Language Modelscited by 6
  7. 2025Large Language Model Interface for Home Energy Management Systemscited by 6
  8. 2025From Natural Language to Solver-Ready Power System Optimization: An LLM-Assisted, Validation-in-the-Loop Frameworkcited by 3
  9. 2025Large Language Model-Empowered Interactive Load Forecastingcited by 2
  10. 2025LLM-Enhanced Multi-Agent Reinforcement Learning with Expert Workflow for Real-Time P2P Energy Tradingcited by 2
  11. 2025LLM4DistReconfig: A Fine-tuned Large Language Model for Power Distribution Network Reconfigurationcited by 1
  12. 2025Large Language Model-Assisted Planning of Electric Vehicle Charging Infrastructure with Real-World Case Studycited by 1
5 more
  1. 2026Knowledge boundary probing and demand-guided intervention for LLM-based power system code generationcited by 1
  2. 2025Automated Heuristic Design for Unit Commitment Using Large Language Modelscited by 0
  3. 2025Keep the Lights On, Keep the Lengths in Check: Plug-In Adversarial Detection for Time-Series LLMs in Energy Forecastingcited by 0
  4. 2026LLMs and Agentic AI Systems for Smart Grids: A Tutorial on Architectures and Applicationscited by 0
  5. 2026Training Numerical Intelligence via Auto-Diagnosis and Skill Discoverycited by 0