Final Report on Retrospective Testing and Application 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 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.