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A statistically-based fault detection approach for environmental and energy management in buildings.
- Source :
-
Energy & Buildings . Jan2018, Vol. 158, p1499-1509. 11p. - Publication Year :
- 2018
-
Abstract
- 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 intent and 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 03787788
- Volume :
- 158
- Database :
- Academic Search Index
- Journal :
- Energy & Buildings
- Publication Type :
- Academic Journal
- Accession number :
- 126869862
- Full Text :
- https://doi.org/10.1016/j.enbuild.2017.11.023