Large language models for building energy applications:Opportunities and challenges  

作　　者：Mingzhe Liu Liang Zhang Jianli Chen Wei-An Chen Zhiyao Yang L.James Lo Jin Wen Zheng O’Neill 

机构地区：[1]J Mike Walker’66 Department of Mechanical Engineering,Texas A&M University,College Station,TX 77843,USA [2]Department of Civil and Architectural Engineering and Mechanics,The University of Arizona,Tucson,AZ 85719,USA [3]College of Civil Engineering,Tongji University,Shanghai 200092,China [4]Department of Multidisciplinary Engineering,Texas A&M University,College Station,TX 77843,USA [5]Civil,Architectural and Environmental Engineering,Drexel University,Philadelphia,PA 19104,USA

出　　处：《Building Simulation》2025年第2期225-234,共10页建筑模拟(英文)

基　　金：supported by the U.S.National Science Foundation(Grant Number:2309030).

摘　　要：Large language models(LLMs)are gaining attention due to their potential to enhance efficiency and sustainability in the building domain,a critical area for reducing global carbon emissions.Built on transformer architectures,LLMs excel at text generation and data analysis,enabling applications such as automated energy model generation,energy management optimization,and fault detection and diagnosis.These models can potentially streamline complex workflows,enhance decision-making,and improve energy efficiency.However,integrating LLMs into building energy systems poses challenges,including high computational demands,data preparation costs,and the need for domain-specific customization.This perspective paper explores the role of LLMs in the building energy system sector,highlighting their potential applications and limitations.We propose a development roadmap built on in-context learning,domain-specific fine-tuning,retrieval augmented generation,and multimodal integration to enhance LLMs’customization and practical use in this field.This paper aims to spark ideas for bridging the gap between LLMs capabilities and practical building applications,offering insights into the future of LLM-driven methods in building energy applications.

关 键 词：large language models building energy applications artificial intelligence energy management optimization LLM-as-agent workflows 

分 类 号：TU201.5[建筑科学—建筑设计及理论] TP18[自动化与计算机技术—控制理论与控制工程]