Analysis of ventilation and infiltration rates using physics-informed neural networks: Impact of space operation and meteorological factors

Abstract

This study presents the development and application of a Physics-Informed Neural Network (PINN) model to estimate ventilation and infiltration rates using long-term observation data, addressing the challenge of dynamically varying space operations and meteorological conditions. A central research equestion is: How can we accurately estimate ventilation rates while accounting for these time-varying factors? Traditional tracer gas methods require numerous measurements to accurately characterize air change rates (ACR) under dynamic space operations and varying meteorological conditions. Our PINN model integrates these fluctuating factors, providing a more precise analysis of their transient effects on ACR. We employed Shapley Additive Explanations (SHAP) to interpret the sensitivity and contributions of each influencing factor. Our findings indicate that the state of windows and doors significantly affects spatial operations, while wind speed and direction are the most impactful meteorological factors. The interaction between open windows and doors results in higher ventilation rates compared to their individual effects. Wind-related factors cause ACR variations exceeding 200 %, with the wind direction relative to the office window playing a crucial role. Additionally, external temperature and indoor-outdoor temperature differences show a strong correlation with ACR. However, limitations include the lack of outdoor CO₂ measurements and the assumption of uniform indoor CO₂ levels, which may affect accuracy. Generalizability is also limited due to the specificity of the space studied. Future work should incorporate outdoor CO₂ data and multiple spaces to enhance model applicability. This study contributes to optimizing ventilation strategies for better indoor air quality and energy efficiency.