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Exploring the Capabilities and Limitations of Large Language Models in the Electric Energy Sector

Subir Majumder, Lin Dong, Fatemeh Doudi, Yuting Cai, Chao Tian, Dileep Kalathi, Kevin Ding, Anupam A. Thatte, Na Li, Le Xie

TL;DR

The paper investigates how large language models can assist the electric energy sector by evaluating their capabilities and limitations across tasks such as load forecasting, power-flow analysis, and document QA. It leverages in-weight learning, prompt engineering, tool embedding, and retrieval-augmented generation to demonstrate practical workflows, including multi-modal analyses and integration with power-system tools. The study identifies data, safety, physics-alignment, and cybersecurity as central challenges, and proposes concrete future directions—curated domain data, tool embeddings, robust knowledge bases, and uncertainty quantification—to enable LLMs as decision-support copilots in power systems. The findings highlight the potential for LLMs to augment human operators while emphasizing governance and physics-based constraints to ensure safe, reliable operation of electric infrastructure.

Abstract

Large Language Models (LLMs) as chatbots have drawn remarkable attention thanks to their versatile capability in natural language processing as well as in a wide range of tasks. While there has been great enthusiasm towards adopting such foundational model-based artificial intelligence tools in all sectors possible, the capabilities and limitations of such LLMs in improving the operation of the electric energy sector need to be explored, and this article identifies fruitful directions in this regard. Key future research directions include data collection systems for fine-tuning LLMs, embedding power system-specific tools in the LLMs, and retrieval augmented generation (RAG)-based knowledge pool to improve the quality of LLM responses and LLMs in safety-critical use cases.

Exploring the Capabilities and Limitations of Large Language Models in the Electric Energy Sector

TL;DR

The paper investigates how large language models can assist the electric energy sector by evaluating their capabilities and limitations across tasks such as load forecasting, power-flow analysis, and document QA. It leverages in-weight learning, prompt engineering, tool embedding, and retrieval-augmented generation to demonstrate practical workflows, including multi-modal analyses and integration with power-system tools. The study identifies data, safety, physics-alignment, and cybersecurity as central challenges, and proposes concrete future directions—curated domain data, tool embeddings, robust knowledge bases, and uncertainty quantification—to enable LLMs as decision-support copilots in power systems. The findings highlight the potential for LLMs to augment human operators while emphasizing governance and physics-based constraints to ensure safe, reliable operation of electric infrastructure.

Abstract

Large Language Models (LLMs) as chatbots have drawn remarkable attention thanks to their versatile capability in natural language processing as well as in a wide range of tasks. While there has been great enthusiasm towards adopting such foundational model-based artificial intelligence tools in all sectors possible, the capabilities and limitations of such LLMs in improving the operation of the electric energy sector need to be explored, and this article identifies fruitful directions in this regard. Key future research directions include data collection systems for fine-tuning LLMs, embedding power system-specific tools in the LLMs, and retrieval augmented generation (RAG)-based knowledge pool to improve the quality of LLM responses and LLMs in safety-critical use cases.
Paper Structure (14 sections, 2 figures)

This paper contains 14 sections, 2 figures.

Table of Contents

  1. Introduction
  2. Capabilities of LLMs to Fill in the Gap
  3. Language Models and In-weight Learning
  4. Prompt Engineering and In-context Learning
  5. Enhanced Capability via Tool Embedding
  6. Enhanced Multi-modal Capabilities
  7. Limitations of LLMs for Applications in the Electric Energy Sector
  8. Challenges in Domain-Specific Data Availability and Processing
  9. Lack of Safety Guardrails
  10. Not Adapted to Handle Physical Principles
  11. Potential Exposure to Cybersecurity and Privacy Threats
  12. Future Prospects
  13. Declaration of Interests
  14. Acknowledgements

Figures (2)

  • Figure 1: Capabilities and Limitations of Applying LLMs in the Electric Energy Sector.
  • Figure 2: Applications of LLMs in the Electric Energy Sector. This figure illustrates four distinct applications of LLMs in power systems. (A) Highlights the use of LLMs' multi-modality and appropriate choice of prompts in insulator defect detection from captured images. (B) Illustrates that fine-tuned language models through in-weight learning and further enhanced by prompt engineering techniques can be used for time-series forecasting. (C) Depicts LLMs' tool-embedding ability alongside prompt engineering can be employed to analyze wildfire patterns for risk assessments. (D) Demonstrates natural language processing strengths of LLMs and the use of RAG to generate precise responses to documents LLMs may not have seen before.