利用大型语言模型推进建筑能源建模：探索与案例研究
Advancing Building Energy Modeling with Large Language Models: Exploration and Case Studies
AI总结：
本文探索将ChatGPT等大型语言模型与EnergyPlus建筑能源建模软件融合，通过三个案例研究展示了其在仿真生成、分析与优化等任务中的自动化潜力，强调了选择合适技术以提升能效建模性能的重要性。
摘要：
人工智能的快速发展推动了如ChatGPT等大型语言模型的出现，其潜在应用延伸至专业工程建模，尤其是基于物理的建筑能源建模。本文研究了大型语言模型与建筑能源建模软件的创新性整合，特别聚焦于ChatGPT与EnergyPlus的融合。首先进行文献综述，揭示了在工程建模中引入大型语言模型的增长趋势，尽管其在建筑能源建模中的应用研究有限。我们强调了大型语言模型在应对建筑能源建模挑战方面的潜力，并概述了潜在应用，包括仿真输入生成、仿真输出分析与可视化、进行错误分析、联合仿真、仿真知识提取与训练，以及仿真优化。三个案例研究揭示了大型语言模型在自动化和优化建筑能源建模任务方面的变革潜力，强调了人工智能在推进可持续建筑实践和能源效率中的关键作用。案例研究表明，选择正确的大型语言模型技术对于提升性能和减少工程投入至关重要。研究结果提倡在未来人工智能研究中采用多学科方法，其影响超越建筑能源建模，延伸至其他专业工程建模。
英文摘要：
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 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 simulation input generation, simulation output analysis and visualization, conducting error analysis, co-simulation, simulation knowledge extraction and training, and 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. The findings advocate a multidisciplinary approach in future artificial intelligence research, with implications extending beyond building energy modeling to other specialized engineering modeling.
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