Opportunities and Challenges of Applying Large Language Models in
  Building Energy Efficiency and Decarbonization Studies: An Exploratory
  Overview
Liang Zhang Zhelun Chen
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.