In recent years, the rapid advancement and impressive capabilities of Large Language Models (LLMs) have been evident across various domains. This paper explores the application, implications, and potential of LLMs in building energy efficiency and decarbonization studies. The wide-ranging capabilities of LLMs are examined in the context of the building energy field, including intelligent control systems, code generation, data infrastructure, knowledge extraction, and education. Despite the promising potential of LLMs, challenges including complex and expensive computation, data privacy, security and copyright, complexity in fine-tuned LLMs, and self-consistency are discussed. The paper concludes with a call for future research focused on the enhancement of LLMs for domain-specific tasks, multi-modal LLMs, and collaborative research between AI and energy experts.

翻译：近年来，大型语言模型（LLMs）的快速发展及其在各领域展现出的卓越能力已得到广泛印证。本文探讨了LLMs在建筑能效与脱碳研究中的应用、影响及潜力。通过考察LLMs在建筑能源领域所具备的广泛能力，包括智能控制系统、代码生成、数据基础设施、知识提取及教育应用等方面。尽管LLMs前景广阔，但本文同时讨论了其面临的挑战，包括复杂且昂贵的计算成本、数据隐私与安全及版权问题、微调模型的复杂性以及自一致性难题。文章最后呼吁未来研究应聚焦于提升LLMs在领域特定任务中的性能、发展多模态大型语言模型，以及推动人工智能与能源领域专家的协同研究。