A Metadata Inference Framework to Provide Operational Information Support for Fault Detection and Diagnosis Applications in Secondary HVAC Systems
As the cost of hardware decreases and software technology advances, building automation systems (BAS) have been widely deployed to new buildings or as part of the retrofit to replace the old control systems. Though they are becoming more prevalent and promise important benefits to the society, such as improved energy-efficiency and occupants’ comfort, many of their benefits remain unreachable. Research suggests that this is because of the heterogeneous, fragmented and nonstandardized nature of existing BASs. One of the purported benefits of these systems is the ability to reduce energy consumption through the application of automated approaches such as fault detection and diagnosis (FDD) algorithms. Savings of up to 0.16 quadrillion BTUs per year could be obtained in the US alone through the use of these approaches, which are just software applications running on BAS hardware. However, deployment of these applications for buildings remains a challenge due to the non-trivial efforts of organizing, managing and extracting metadata associated with sensors (e.g., information about their type, function, etc.), which is required by them. One of the reasons leading to the problem is that varying conventions, acronyms, and standards are used to define this metadata. Though standards and governmentmandated policies may lift these obstacles and enable these softwarebased improvements to our building stock, this effort could take years to come to fruition and there are alternative technical solutions, such as automated metadata inference techniques, that could help reign in on the non-standardized nature of today’s BASs. This thesis sheds light on the visibility of this alternative approach by answering three key questions, which are then validated using data from more than 400 buildings in the US: (a) What is the specific operational information required by FDD approaches for secondary heating, ventilation, and air conditioning (HVAC) systems found in existing literature? (b) How is the performance of existing metadata inference approaches affected by changes in building characteristics, weather conditions, building usage patterns, and geographical locations? (c) What is an approach that can provide physical interpretations in the case of incorrect metadata being inferred? We find that: (a) The BAS points required by more than 30% of FDD approaches include six sensors in AHUs monitoring supply air temperature, outside air temperature, chilled water valve position, return air temperature, supply air flow rate, and mixed air temperature; (b) The average performance of existing inference approaches in terms of accuracy is similar across building sites, though there is significant variance, and the expected accuracy of classifying the type of points required by a particular FDD application for a new unseen building is, on average, 75%; (c) A new approach based on physical models is developed and validated on both the simulation data and the real-world data to infer the point types confused by data-driven models with an accuracy ranging from 73% to 100%, and this approach can provide physical interpretations in the case of incorrect inference. Our results provide a foundation and starting point to infer the metadata required by FDD approaches and minimize the implementation cost of deploying FDD applications on multiple buildings.