Commercial buildings during operation are dynamic environments where changes to control strategies and space usage regularly occur. As a result of these and other issues, a performance gap between design intentand actual building performance emerges. This paper seeks to address the operational performance gap and enhance operational building performance through statistically-based fault detection. Additionally, this paper seeks to remedy the knowledge gap building managers face in the identification of key building faults based on minimal quantities and streams of time-series building data. A new methodology is presented that incorporates simulation and breakout detection to address these issues. Residual based exponentially weighted moving average (EWMA) charts and Shewhart charts are compared against a breakout detection algorithm to identify shifts or faults in building performance data. Artificial faults are introduced into the measured time-series data to test the validity of the chosen statistical techniques. Statistical metric sensitivity and precision are calculated to quantify the performance of the new methodology. A summary of results demonstrate that the breakout detection algorithm was the most effective method in detecting meaningful faults in building performance data, followed by residual based EWMA and Shewhart models. (C) 2017 Elsevier B.V. All rights reserved.