The rapid progression in artificial intelligence has facilitated the emergence of large language models like ChatGPT, offering potential applications extending into specialized engineering modeling, especially physics-based building energy modeling. This paper investigates the innovative integration of large language models with building energy modeling software, focusing specifically on the fusion of ChatGPT with EnergyPlus. A literature review is first conducted to reveal a growing trend of incorporating of large language models in engineering modeling, albeit limited research on their application in building energy modeling. We underscore the potential of large language models in addressing building energy modeling challenges and outline potential applications including 1) simulation input generation, 2) simulation output analysis and visualization, 3) conducting error analysis, 4) co-simulation, 5) simulation knowledge extraction and training, and 6) simulation optimization. Three case studies reveal the transformative potential of large language models in automating and optimizing building energy modeling tasks, underscoring the pivotal role of artificial intelligence in advancing sustainable building practices and energy efficiency. The case studies demonstrate that selecting the right large language model techniques is essential to enhance performance and reduce engineering efforts. Besides direct use of large language models, three specific techniques were utilized: 1) prompt engineering, 2) retrieval-augmented generation, and 3) multi-agent large language models. The findings advocate a multidisciplinary approach in future artificial intelligence research, with implications extending beyond building energy modeling to other specialized engineering modeling.

翻译：人工智能的快速发展催生了如ChatGPT等大语言模型，为其在专业工程建模（尤其是基于物理的建筑能耗建模）中的应用提供了潜力。本文研究了大语言模型与建筑能耗建模软件的创新融合，重点关注ChatGPT与EnergyPlus的结合。首先通过文献综述发现，大语言模型在工程建模中的应用呈增长趋势，但在建筑能耗建模方面的研究仍有限。我们强调了大语言模型在应对建筑能耗建模挑战方面的潜力，并概述了潜在应用，包括：1）模拟输入生成，2）模拟输出分析与可视化，3）误差分析，4）协同模拟，5）模拟知识提取与训练，以及6）模拟优化。三项案例研究揭示了大语言模型在自动化和优化建筑能耗建模任务中的变革潜力，凸显了人工智能在推动可持续建筑实践和能效提升中的关键作用。案例研究表明，选择合适的大语言模型技术对于提升性能并减少工程工作量至关重要。除直接使用大语言模型外，还采用了三项具体技术：1）提示工程，2）检索增强生成，以及3）多智能体大语言模型。研究结果倡导未来人工智能研究采用多学科方法，其影响不仅限于建筑能耗建模，还可扩展到其他专业工程建模领域。