Building performance simulation (BPS) is crucial for building performance assessments across its lifecycle. However, the complexity of buildings and the iterative nature of simulation poses challenges, leading to high costs and low values. Previous studies focused on simplification, but did not fully utilize advanced simulation engines. Despite recent advancements, there is a lack of research on leveraging artificial intelligence (AI), specifically generative pre-trained transformer (GPT), for BPS. Therefore, this study proposes a GPT-based BPS system, enhancing simulation efficiency and value by integrating simulation engines and advanced data analytics in the GPT environment. The ontology for GPT-based BPS is also developed to enable comprehensive, reliable, informative BPS environments. Based on this framework, case studies were conducted for GPT-based multizone airflow network simulation in a high-rise residential building using CONTAM software. They demonstrate GPT’s capabilities in retrieving simulation data, visualizing results with data mining, answering questions based on building knowledge, checking compliance with design guidelines, and proposing design alternatives. Finally, this study emphasizes expert interventions with ontological engineering informatics to utilize strictly structured BPS engines.

中文翻译：

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基于本体辅助GPT的建筑性能模拟与评估：多区域气流模拟的实现


建筑性能模拟 （BPS） 对于建筑性能评估在其整个生命周期中至关重要。然而，建筑物的复杂性和模拟的迭代性质带来了挑战，导致高成本和低价值。以前的研究侧重于简化，但没有充分利用先进的仿真引擎。尽管最近取得了进展，但目前还缺乏关于将人工智能 （AI），特别是生成式预训练转换器 （GPT） 用于 BPS 的研究。因此，本研究提出了一种基于 GPT 的 BPS 系统，通过在 GPT 环境中集成仿真引擎和高级数据分析来提高仿真效率和价值。基于 GPT 的 BPS 的本体也是为了实现全面、可靠、信息丰富的 BPS 环境而开发的。基于此框架，使用 CONTAM 软件对高层住宅建筑中基于 GPT 的多区域气流网络仿真进行了案例研究。它们展示了 GPT 在检索仿真数据、通过数据挖掘可视化结果、根据构建知识回答问题、检查是否符合设计准则以及提出设计替代方案方面的能力。最后，本研究强调专家干预本体论工程信息学，以利用严格结构化的 BPS 引擎。