  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.
