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

Rapid developments in large language models (LLMs) have created new opportunities for their use in the energy sector, from forecasting renewable energy to power system operation and energy market analysis. These models help improve decision-making, anomaly detection, and optimization procedures in intricate energy systems by using vast amounts of structured and unstructured data. This study provides a comprehensive review of the LLM origins, evaluation, and fine-tuning techniques as well as their integration into energy systems, including their application in fault detection and diagnosis, energy forecasting, document automation, energy management, defect detection, and power system operation. Their performance in terms of explainability, generalization ability, and scalability for energy-related applications is critically examined in this paper. The report also emphasizes significant challenges to the adoption of LLMs, such as the need for computing power, the lack of data, and ethical issues like bias and false information. Power-efficient models, hybrid artificial intelligence (AI) platforms, and domain-specific fine-tuning are some of the solutions discussed. Future areas of interest include multi-modality to obtain maximal forecasting and operational intelligence, real-time adaptability, and explainable. This paper summarizes current developments and provides information on LLM-driven innovation in energy systems while maintaining transparency and dependability. Compared with prior LLM–energy surveys that either remain general-purpose or focus on a single subdomain, this review fills three concrete gaps: (i) a cross-domain synthesis of energy-specific LLM applications spanning power systems, buildings, and forecasting; (ii) a methods-oriented consolidation of evaluation and parameter-efficient fine-tuning practices tailored to energy tasks; and (iii) a deployment-centric analysis of real-time and edge constraints (energy, latency, hardware) with a practical reporting checklist for operational adoption.