Book Description

Building HVAC systems account for more than 30% of annual energy consumption in United States. However, it has become apparent that only in a small percentage of buildings do HVAC systems work efficiently or in accordance with design intent. Studies have shown that operational faults are one of the main reasons for the inefficient performance of these systems. It is estimated that an energy saving of 5 to 15 percent is achievable simply by fixing faults and optimizing building control systems. In spite of good progress in recent years, methods to manage faults in building HVAC systems are still generally undeveloped; in particular, there is still a lack of reliable, affordable, and scalable solutions to manage faults in HVAC systems. Modeling limitations, measurement constraints, and the complexity of concurrent faults have made the diagnosis of these problems as much an art as a science. The challenge is how to evaluate system performance within the boundaries defined by such limitations. This thesis focuses on a number of issues that, in our opinion, are crucial to the development of reliable and scalable diagnostic solutions for building HVAC systems. Diagnostic complexity due to modeling and measurement constraints, the pro-activeness of diagnostic mechanisms, bottom-up versus top-down diagnostic perspectives, diagnosis-ability, and the correlation between measurement constraints and diagnostic capability will be discussed in detail. We will develop model-based and non-model-based diagnostic algorithms that have the capability of dealing with modeling and measurement constraints more effectively. We will show how the effect of measurement constraints can be traced to the information entropy of diagnostics assessments and how this can lead to a framework optimizing the architecture of sensor networks from the diagnostic perspective. In another part of this study, we focus on proactive diagnostics. In the past, the topic of proactive fault diagnostics has not been given enough attention, even though the capability of conducting and supervising automated proactive testing is essential in terms of being able to replace manual troubleshooting with automated solutions. We will show how a proactive testing problem can be formulated as a decision making problem coupled with a Bayesian network diagnostic model. The algorithms presented in this thesis have been implemented and tested in the Lawrence Berkeley National Laboratory (LBNL) using real and synthetic data.