Fault detection and diagnosis (FDD) in AHUs within HVAC systems ensure optimal performance, energy efficiency, and occupant comfort by quickly identifying and diagnosing faults. Combining deep learnin
The DOE and Berkeley Lab have partnered across the national laboratory complex and with the research community to curate, validate, and publish the world’s largest set of labeled time-series data repr
These data represent the training set for a neural network designed to classify normal and faulty electrical circuit operation. They represent four device classes, and two operating states (normal and
This study provides a review of the current state of knowledge, gaps, and potential value in research on the prevalence of faults in commercial buildings. Two separate efforts were made in this study: