A Review of Large Language Models for Energy Systems: Applications, Challenges, and Future Prospects

Analysis

Why This Paper Matters

This review arrives at a critical juncture where large language models are rapidly expanding beyond natural language processing into domain-specific applications like energy systems. The energy sector generates vast amounts of structured and unstructured data, from sensor readings to maintenance logs, and LLMs offer a promising way to harness this data for improved forecasting, fault detection, and operational decision-making. However, prior surveys have either remained general-purpose or focused on a single subdomain, leaving a gap in cross-domain synthesis. This paper fills that gap by providing a comprehensive overview that spans power systems, buildings, and forecasting, making it a valuable resource for researchers and practitioners seeking to understand the full landscape of LLM applications in energy.

Moreover, the paper goes beyond a simple catalog of applications by addressing practical deployment concerns. It emphasizes real-time and edge constraints, such as energy consumption, latency, and hardware limitations, which are often overlooked in academic reviews. By offering a reporting checklist for operational adoption, it bridges the gap between research and real-world implementation, which is crucial for the industry's move toward more intelligent and responsive energy systems.

Technical Contributions

The paper makes several key technical contributions:

• Cross-domain synthesis: It consolidates LLM applications across power systems, buildings, and forecasting, providing a holistic view that is missing in prior surveys.
• Methods-oriented consolidation: It systematically reviews evaluation metrics and parameter-efficient fine-tuning techniques (e.g., LoRA, adapters) tailored to energy tasks, offering practical guidance for model adaptation.
• Deployment-centric analysis: It analyzes real-time and edge constraints, including energy consumption, latency, and hardware requirements, and proposes a practical reporting checklist for operational adoption.
• Critical performance examination: It evaluates LLMs in terms of explainability, generalization, and scalability, which are essential for trust and reliability in energy applications.
• Challenge and solution framework: It identifies key challenges (computing power, data scarcity, bias) and discusses solutions like power-efficient models, hybrid AI platforms, and domain-specific fine-tuning.

Results

As a review paper, the results are qualitative rather than quantitative. The paper does not present new experimental metrics but synthesizes findings from existing literature. It highlights that LLMs have been successfully applied to fault detection and diagnosis, energy forecasting, document automation, energy management, defect detection, and power system operation. The critical examination reveals that while LLMs show promise, their performance is often limited by explainability and generalization issues. The paper also underscores the need for power-efficient models and domain-specific fine-tuning to overcome computational and data challenges. The proposed reporting checklist is a practical outcome that can help standardize deployment practices.

Significance

The broader impact of this review lies in its potential to guide future research and development in LLM-driven energy systems. By identifying gaps and proposing solutions, it sets a research agenda that includes multi-modality for maximal forecasting and operational intelligence, real-time adaptability, and explainable AI. This is particularly important as the energy sector moves toward decarbonization and digitalization, where intelligent systems are needed to manage complex, distributed, and renewable energy sources. The paper's emphasis on transparency and dependability also addresses ethical concerns, which is crucial for gaining stakeholder trust. For AI practitioners, this review offers a comprehensive starting point for exploring LLM applications in energy, and its deployment-focused insights are directly actionable for those working on edge and real-time systems.