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Fault detection and diagnosis (FDD) using smart thermostat data is a relatively new field of research, but one with immediate practical application to residential indoor climate control. In this two-part report, a statistics-based FDD method is proposed to identify anomalous residential air conditioning systems using aggregated thermostat data. The proposed method extracts features that characterize system operational behaviors, and determines outliers by comparing the features between thousands of systems. Those outliers are subsequently classified into several categories of anomalous operational behaviors, such as near-continuous operation, poor control, and so on, which are closely related to soft mechanical faults. While the first part of the report details the methodology, this second part provides five case studies to demonstrate the effectiveness of the proposed method. Although these cases should not be considered a complete representation of all kinds of anomalous behaviors, the examples illustrate typical problems, including performance degradation faults with or without affecting occupant comfort, control problems with slight degradation, and oversized systems. Each case study exemplifies one type of behavior that is common among a group of systems, rather than a single outlier that behaves much differently from all others. The case studies illustrate how the proposed fault detection and diagnosis method is capable of identifying malfunctioning systems from a population of residential air conditioning systems.

Multivariate fault detection for residential HVAC systems using cloud-based thermostat data, part II: Case studies