Unsupervised automated fault detection and diagnosis for light commercial buildings’ HVAC systems

Abstract

Fault detection in light commercial building HVAC systems can significantly improve the energy efficiency of this class of buildings. A light commercial building is a commercial structure with fewer than six stories and a floor plan area of less than 2500 ft². Data extracted from existing buildings in this class are generally unlabeled, raw, and characterized by many inconsistencies and discontinuities, making Automated Fault Detection and Diagnosis (AFDD) particularly challenging. This study aims to develop an unsupervised AFDD method tailored for light commercial buildings, which is transferable among different HVAC configurations within this building class. The method is designed to handle unlabeled, incomplete, and raw datasets provided by their Building Energy Management Systems (BEMS). Principal Component Analysis (PCA) was selected as the core method due to its scalability and transferability. Specific techniques were introduced to address time series analysis and fault detection and diagnosis (FDD) based on the dynamics of the system, using appropriate window sizing. The method was validated using two different light commercial buildings with distinct configurations and data availability. The primary building, an office in Montreal, Canada, and the secondary building, a small industrial facility in Ireland, served as the test cases. The proposed method demonstrated promising results in detecting and isolating faulty inputs, providing information on the severity levels and locations of faults. It successfully identified whether faults were at the level of the central system or within specific zones in both studied cases.