| Download | - View accepted manuscript: Energy performance based anomaly detection in non-residential buildings using symbolic aggregate approximation (PDF, 604 KiB)
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| DOI | Resolve DOI: https://doi.org/10.1109/COASE.2018.8560433 |
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| Author | Search for: Ashouri, Araz1; Search for: Hu, Yitian1; Search for: Newsham, Guy R.1; Search for: Shen, Weiming1 |
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| Affiliation | - National Research Council Canada. Construction
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| Format | Text, Article |
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| Conference | 2018 IEEE 14th International Conference on Automation Science and Engineering (CASE), 20-24 August 2018, Munich, Germany |
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| Subject | fault detection and diagnosis; building energy management; energy auditing; data analysis; electricity demand |
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| Abstract | Building system faults in commercial and office buildings can result in a reduced occupant comfort and increased utility bills. Energy performance-based anomaly detection helps operators efficiently identify faults. In this work, a data-driven method for anomaly detection is presented. Using a symbolic aggregate method, the weekly energy demand profiles are statistically quantised and labeled to determine normal and abnormal building behaviours. A case study with three federal office buildings has been conducted to demonstrate the proposed method. The resulting technology provides building operators with easily-interpreted and actionable information for optimised building performance. |
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| Publication date | 2018-12-06 |
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| Publisher | IEEE |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| NRC number | NRCC-CONST-56261E |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | 4a2c0581-f009-4692-a91c-c6c91a1f71d9 |
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| Record created | 2019-04-11 |
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| Record modified | 2020-06-03 |
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