Lawrence Berkeley National Laboratory

It is estimated that 5%–30% energy use in commercial buildings is wasted due to faults and errors in operations. Automated fault detection and diagnostics (AFDD) technologies can address this waste by identifying (detecting) deviations from normal or expected operation (faults), and resolving (diagnosing) the type of problem or its location, minimizing the need for complex manual analysis of operational data. Although currently underutilized, AFDD is a powerful approach to ensuring efficient building operations. AFDD offers the potential to greatly improve performance, and to do so cost effectively. There is currently a diverse landscape of AFDD technologies on the market, but no common framework exists to characterize such tools, or distinguish one offering from another. It is difficult to determine from vendor websites and marketing materials key technology features and capabilities, or the overall state of the technology. In this paper, we present an AFDD characterization framework, and findings from applying the framework to survey over a dozen technologies from today’s market. This paper outlines the current state of the market, as well as outstanding needs in the industry, derived from direct engagement of, and technical assistance provided to users of AFDD technology. A core set of interrelated informational, organizational, and technical needs and barriers that must be addressed to realize the full potential of AFDD at scale are identified. Lastly, based on information gathered through a survey and discussion with both vendors and users, several opportunities to further advance the technology are discussed.