A stable, reliable and interpretable diffusion model for HVAC FDD with data unavailability

Abstract

Data-driven fault detection and diagnosis (FDD) methods are emerging and attractive techniques for smart energy management in buildings, including the energy management in heating, ventilation, and air conditioning (HVAC) sub-systems. However, the real-world deployment of FDD in HVAC is hindered by data unavailability scenarios. In the past few years, various data augmentation methods, such as the generative adversarial network (GAN), have been proposed to address the abovementioned problem. However, these data augmentation methods suffer from stability, reliability, and interpretability issues. This paper proposes an interpretable ensemble learning-based diffusion model (IELDM) for HVAC systems, generating stable, reliable synthetic datasets to address the data unavailability issue. A split-gain-based method is introduced in IELDM to enhance the interpretability of the overall machine learning framework. Experimental results show that IELDM stably boosts FDD accuracy under extremely limited fault data, with improvements of up to 11.2 %, 13.2 %, and 12.08 % across three HVAC systems, clearly outperforming current state-of-the-art methods. By systematically overcoming the challenges of instability, unreliability, and lack of interpretability in current generative models, this work offers a robust solution to close the application gap of HVAC FDD in practical building energy systems.