Review of LLMs Applications in Electrical Power and Energy Systems

Abstract

This paper presents a comprehensive review of the applications, challenges, and future directions of Large Language Models (LLMs) in the Electrical Power Domain (EPD). Leveraging transformer-based architectures such as GPT, BERT, and LLaMA, LLMs have shown transformative potential across power system applications including load forecasting, fault diagnosis, regulatory compliance, question answering, risk assessment, and intelligent data analysis. Through a systematic analysis of over 45 studies, the review highlights measurable benefits such as up to 20% improvement in load forecasting accuracy, 30% reduction in operational response time, and 40% decrease in manual workload for regulatory tasks. LLMs have demonstrated strong adaptability through zero-shot and few-shot learning and are capable of processing multimodal inputs for real-time decision-making. However, limitations such as high computational costs, lack of domain-specific datasets, limited explainability, and concerns around regulatory alignment hinder widespread deployment. To address these gaps, the paper outlines research opportunities including domain-specific fine-tuning, scalable deployment strategies, multimodal integration, and the development of unified benchmarks such as ElecBench. Overall, the integration of LLMs in power systems represents a significant step toward more intelligent, reliable, and sustainable energy management.

Topics & Concepts