Building fault detection data to aid diagnostic algorithm creation and performance testing.

It is estimated that approximately 4-5% of national energy consumption can be saved through corrections to existing commercial building controls infrastructure and resulting improvements to efficiency. Correspondingly, automated fault detection and diagnostics (FDD) algorithms are designed to identify the presence of operational faults and their root causes. A diversity of techniques is used for FDD spanning physical models, black box, and rule-based approaches. A persistent challenge has been the lack of common datasets and test methods to benchmark their performance accuracy. This article presents a first of its kind public dataset with ground-truth data on the presence and absence of building faults. This dataset spans a range of seasons and operational conditions and encompasses multiple building system types. It contains information on fault severity, as well as data points reflective of the measurements in building control systems that FDD algorithms typically have access to. The data were created using simulation models as well as experimental test facilities, and will be expanded over time

Development of New Whole Building Fault Detection and Diagnosis Techniques for Commissioning Persistence

Commercial building owners spent $167 billion for energy in 2006. Building commissioning services have proven to be successful in saving building energy consumption. However, the optimal energy performance obtained by commissioning may subsequently degrade. The persistence of savings is of significant interest. For commissioning persistence, two statistical approaches, Days Exceeding Threshold-Date (DET-Date) method and Days Exceeding Threshold-Outside Air Temperature (DET-Toa) method, are developed to detect abnormal whole building energy consumption, and two approaches called Cosine Similarity method and Euclidean Distance Similarity method are developed to isolate the possible fault reasons. The effectiveness of these approaches is demonstrated and compared through tests in simulation and real buildings. The impacts of the factors including calibrated simulation model accuracy, fault severity, the time of fault occurrence, reference control change magnitude setting, and fault period length are addressed in the sensitivity study. The study shows that the DET-Toa method and the Cosine Similarity method are superior and more useful for the whole building fault detection and diagnosis

Fault “Auto-correction” for HVAC Systems: A Preliminary Study

A Fault Detection and Diagnostics (FDD) tool is a type of energy management and information system that is designed to continuously identify the presence of faults and efficiency improvement opportunities through a 1-way interface to the building automation system and application of automated analytics. It is estimated that 5-30% energy saving can be achieved by employing FDD tools and implementing efficiency measures based on FDD findings. Although the potential of this technology is high, actual savings are only realized when an operator takes an action to fix the problem. There is a subset of faults that can be potentially addressed automatically by the system, without operator intervention. Automating this fault correction can significantly increase the savings generated by FDD tools and reduce the reliance on human intervention. This paper presents preliminary efforts towards delivering automated fault correction. It describes nine fault auto-correction algorithms for heating ventilation and air conditioning (HVAC) systems that were developed to automatically correct faults or improve controls operation. It also presents preliminary testing results of one auto-correction algorithm (improve air handling unit static pressure setpoint reset) in a commercial building, located in Berkeley, California, US. The auto-correction algorithms and implementation frameworks of this initial study provide a foundation for future auto-correction algorithm development and novel schemes for improving building operation performance and reliability

Final Report on Retrospective Testing and Application of an Automated Building Commissioning Analysis Tool (ABCAT)

More than $18 billion of energy is wasted annually in the U.S. commercial building sector. Commissioning services have proven successful in reducing building energy consumption, but the optimal energy performance obtained by commissioning may subsequently degrade. Therefore, it is very helpful to have tools that can help maintain the optimal building energy performance. An Automated Building Commissioning Analysis Tool (ABCAT) that combines a calibrated simulation operated in conjunction with diagnostic techniques is such a simple and cost efficient tool, which can continuously monitor whole building energy consumption after commissioning, warn operation personnel when an HVAC system problem has increased energy consumption, and assist them in identifying the possible cause(s) of the problem. This report presents the results of six retrospective and nine live test case implementations of ABCAT on a total of fifteen buildings located in the United States and Europe. For each building, the energy simulation model used was calibrated to the building energy consumption data in a baseline period. Then, the model was used to predict the optimal cooling and heating consumption in the following days. A cumulative energy difference plot is the primary fault detection metric used in ABCAT; this plot continuously computes and plots the algebraic sum of the daily differences between the measured and simulated consumption. A fault detection standard is developed and defined in the report. In total, ABCAT detected 23 faults in ten of the fifteen buildings using the fault detection standard developed and other means. This report also outlines a methodology for identifying the cause(s) of the faults identified. Where applicable, the reasons for the detected faults are discussed in the report. The causes of some of the detected faults are verified with historical documentation, and the remaining diagnoses remain unconfirmed due to data quality issues, incomplete information on maintenance performed in the buildings and time constraints

Modeling Air Handling Units to Create a Diverse Fault Dataset for FDD Innovation: Lessons Learned and Recommendations

As energy management and information systems (e.g., automated fault detection and diagnostics [AFDD] tools) become more prevalent in the commercial building stock, it is important to determine the effectiveness of these technologies by benchmarking their performance. The authors have been working to develop the largest publicly available dataset of HVAC fault datasets for performance benchmarking applications, covering the most common HVAC systems and designs including chiller plants, rooftop packaged units, dual duct air handling unit and single duct air handling units. This study covers the development, modeling, and validation of a synthetic fault dataset for the air handling unit (AHU), one of the most common HVAC configurations found in the commercial building stock. Despite this being a common system, real-world time series data are scarce and usually do not span a wide range of weather conditions. Due to this limitation, two detailed AHU models, which included the single duct AHU and dual duct AHU developed in the Modelica language and HVACSIM+ were employed to carry out annual simulations of numerous common sensor faults, mechanical faults, and control sequence faults. The fault inclusive data were then validated by comparing fault effects on system performance to expected symptoms. We summarize the nature of each fault and their impacts under different weather and operation conditions. We report some lessons learnt during the efforts of validating the high volumes of the FDD data sets. Finally, we highlight considerations for FDD developers that may want to use this dataset to assess their algorithms’ performance and their improvement over time

Development and Implementation of Fault-Correction Algorithms in Fault Detection and Diagnostics Tools

A fault detection and diagnostics (FDD) tool is a type of energy management and information system that continuously identifies the presence of faults and efficiency improvement opportunities through a one-way interface to the building automation system and the application of automated analytics. Building operators on the leading edge of technology adoption use FDD tools to enable median whole-building portfolio savings of 8%. Although FDD tools can inform operators of operational faults, currently an action is always required to correct the faults to generate energy savings. A subset of faults, however, such as biased sensors, can be addressed automatically, eliminating the need for staff intervention. Automating this fault “correction” can significantly increase the savings generated by FDD tools and reduce the reliance on human intervention. Doing so is expected to advance the usability and technical and economic performance of FDD technologies. This paper presents the development of nine innovative fault auto-correction algorithms for Heating, Ventilation, and Air Conditioning pi(HVAC) systems. When the auto-correction routine is triggered, it overwrites control setpoints or other variables to implement the intended changes. It also discusses the implementation of the auto-correction algorithms in commercial FDD software products, the integration of these strategies with building automation systems and their preliminary testing