Evaluation of large language models (LLMs) on the mastery of knowledge and skills in the heating, ventilation and air conditioning (HVAC) industry

Jie Lu; Xiangning Tian; Chaobo Zhang; Yang Zhao; Jian Zhang; Wenkai Zhang; Chenxin Feng; Jianing He; Jiaxi Wang; Fengtai He

doi:10.1016/j.enbenv.2024.03.010

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Evaluation of large language models (LLMs) on the mastery of knowledge and skills in the heating, ventilation and air conditioning (HVAC) industry

更新时间：2025-12-05

- Evaluation of large language models (LLMs) on the mastery of knowledge and skills in the heating, ventilation and air conditioning (HVAC) industry
- Evaluation of large language models (LLMs) on the mastery of knowledge and skills in the heating, ventilation and air conditioning (HVAC) industry
- 能源与人工环境（英文） 2025年第5期页码：875-892
- 作者机构：
  
  1. Institute of Refrigeration and Cryogenics, Zhejiang University,Hangzhou,China,310027
  2. Energy Efficient Cities Initiative, Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, United Kingdom
  3. Architectural Design & Research Institute of Zhejiang University Co., Ltd.,Hangzhou,China,310027
  4. Center for Balance Architecture, Zhejiang University,Hangzhou,China,310027
  5. Department of the Built Environment, Eindhoven University of Technology,Eindhoven,the Netherlands,5612 AZ
  6. Key Laboratory of Clean Energy and Carbon Neutrality of Zhejiang Province, Jiaxing Research Institute, Zhejiang University,Jiaxing,China,314000
- 作者简介：
- 基金信息：
- DOI：10.1016/j.enbenv.2024.03.010
  中图分类号：
- 收稿：2024-01-29，
  
  网络出版：2024-03-27，
  
  纸质出版：2025-09-30
- 稿件说明：
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Jie Lu, Xiangning Tian, Chaobo Zhang, 等. Evaluation of large language models (LLMs) on the mastery of knowledge and skills in the heating, ventilation and air conditioning (HVAC) industry[J]. 能源与人工环境（英文）, 2025,(5):875-892.

Jie Lu, Xiangning Tian, Chaobo Zhang, et al. Evaluation of large language models (LLMs) on the mastery of knowledge and skills in the heating, ventilation and air conditioning (HVAC) industry[J]. Energy and Built Environment, 2025, (5): 875-892.
Jie Lu, Xiangning Tian, Chaobo Zhang, 等. Evaluation of large language models (LLMs) on the mastery of knowledge and skills in the heating, ventilation and air conditioning (HVAC) industry[J]. 能源与人工环境（英文）, 2025,(5):875-892. DOI： 10.1016/j.enbenv.2024.03.010.

Jie Lu, Xiangning Tian, Chaobo Zhang, et al. Evaluation of large language models (LLMs) on the mastery of knowledge and skills in the heating, ventilation and air conditioning (HVAC) industry[J]. Energy and Built Environment, 2025, (5): 875-892. DOI： 10.1016/j.enbenv.2024.03.010.

摘要

Abstract

Large language models (LLMs) have shown human-level capabilities in solving various complex tasks. However

it is still unknown whether state-of-the-art LLMs master sufficient knowledge related to heating

ventilation and air conditioning (HVAC) systems. It will be inspiring if LLMs can think and learn like professionals in the HVAC industry. Hence

this study investigates the performance of LLMs on mastering the knowledge and skills related to the HVAC industry by letting them take the ASHRAE Certified HVAC Designer examination

an authoritative examination in the HVAC industry. Three key knowledge capabilities are explored: recall

analysis and application. Twelve representative LLMs are tested such as GPT-3.5

GPT-4 and LLaMA. According to the results

GPT-4 passes the ASHRAE Certified HVAC Designer examination with scores from 74 to 78

which is higher than about half of human examinees. Besides

GPT-3.5 passes the examination twice out of five times. It demonstrates that some LLMs such as GPT-4 and GPT-3.5 have great potential to assist or replace humans in designing and operating HVAC systems. However

they still make some mistakes sometimes due to the lack of knowledge

poor reasoning capabilities and unsatisfactory equation calculation abilities. Accordingly

four future research directions are proposed to reveal how to utilize and improve LLMs in the HVAC industry: teaching LLMs to use design tools or software in the HVAC industry

enabling LLMs to read and analyze the operational data from HVAC systems

developing tailored corpuses for the HVAC industry

and assessing the performance of LLMs in real-world HVAC design and operation scenarios.

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