Ontology-assisted GPT-based building performance simulation and assessment: Implementation of multizone airflow simulation

Abstract

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.