Retrospective Testing of an Automated Building Commissioning Analysis Tool (ABCAT)

Abstract

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 a retrospective implementation of ABCAT on five buildings, each of which consists of offices, classrooms and laboratories and has at least three years of post commissioning daily energy consumption data, on the Texas A&M University campus. For each building, the energy simulation model used was calibrated to the building energy consumption data in a post commissioning 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, and ABCAT detected 18 faults in fifteen building-years of consumption data based on this standard. The minimum, maximum and median magnitudes of the faults detected as a percentage of the average daily baseline energy consumption are 15.5%/89.5%/49.1% for the eight CHW faults, and 14.1%/59.8%/24.7% for the ten HW faults. The possible 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 and incomplete information on maintenance performed in the buildings.