Heating, ventilation, and air conditioning (HVAC) is an indoor environmental technology that is extensively instrumented for large-scale buildings. Among all subsystems of buildings, the HVAC system dominates the energy consumption and accounts for 57% of the energy used in U.S. commercial and residential buildings. Unfortunately, the HVAC system may fail to meet the performance expectations due to various faults, including not only complete hardware failures, but also non-optimal operations. These faults waste more than 20% of the energy HVAC consumes. Therefore, it is of great potential to develop automatic, quick-responding, intelligent, and reliable monitoring and diagnosis tools to ensure the normal operations of HVAC and increase the energy efficiency of buildings.
To achieve these goals, increasing attentions have been attracted to two research areas, i.e., models that monitor the indoor thermal environment, and fault detection and diagnosis (FDD) tools that capture abnormal HVAC performance. Despite contributions of the existing works, there are still many challenges in these two areas. For the thermal models, the major concerns lie in 1) most of the models are determined empirically, 2) optimal structures and orders of the models are often determined through simulations, 3) the predictions of the models degrade quickly over longer time intervals, and 4) a lack of studies to incorporate architectural parameters and control variables into the models. For the FDD, we face the challenges of 1) the inherent complexity, coupled hardware and software, and increasing scale of HVAC significantly complicate the nature of faults, 2) faults occur at different levels with various degrees of impacts on upper-level HVAC units, 3) practical FDD tools at the system-level are scarce, and 4) the computational efficiency and calibration onerousness of the simulation-based FDD is a concern.
In this thesis, we address these challenges by innovating a system-level monitoring and diagnosis tool for HVAC. For the monitoring, we study and establish a parametric modeling approach to present indoor air temperature and thermal comfort. The resulting models take advantages of both analytical and numerical modeling techniques. These models have a two-stage regression structure, and explicitly include both architectural parameters and control variables as its predictors. As a result, they allow parametric studies of influence of the building envelope on indoor thermal behavior, serve as an efficient foundation for intelligent HVAC control design, and help optimize the design of and the material selection for office buildings. For the diagnosis, we innovate and develop a system-level FDD architecture for detecting faults across different levels of the HVAC system. Specifically, this architecture monitors and detects faulty HVAC units in a top-down manner. By monitoring HVAC units at higher level, instead of lower level components, the proposed FDD strategy reduces the computational effort in real-time monitoring of the HVAC system, obtains a system-level view of the HVAC operation, and provides a way to integrate the existing methods for component fault detection when needed. Based on extensive data collected from an office building on the campus of the University of California at Merced, numerical validations of the models, and examples of detected faults demonstrate the effectiveness of the proposed monitoring and diagnosis tool.