PNNL

Abstract

HVAC faults in U.S. commercial buildings are estimated to waste 0.7 quads of energy annually and many companies have deployed software at scale for fault detection and diagnosis (FDD). Yet the lack of comprehensive published data on fault prevalence makes it difficult for researchers, software developers, and building operators to target the most important types of faults and accelerate the widespread deployment of this technology. A multi-lab effort is underway to quantify the prevalence of commercial building HVAC faults. This paper describes initial analysis conducted on over 2-years of FDD data from 12 campus buildings to inform the methodology and data collection strategy of the full study. The analysis found a large variability in fault prevalence across different buildings, pieces of equipment, and fault types, many duplicate fault alarms, and challenges in validating faults with secondary data sources such as manually entered work order data. This paper also discusses the requirements for a successful full study, which necessitates the acquisition of a large, diverse dataset, from buildings that vary in end-use, climate region, and installed FDD software platform, as well as robust validation data easily linked to the FDD data. It is especially important to perform this analysis on data from multiple FDD providers and building owners to ensure that the results are agnostic to the software methodology used and building-specific settings. Future work will present the results of the full study, which will contain data from several different partner organizations, spanning a large proportion of U.S. climate zones.