Download | - View accepted manuscript: Energy performance based anomaly detection in non-residential buildings using symbolic aggregate approximation (PDF, 604 KiB)
|
---|
DOI | Resolve DOI: https://doi.org/10.1109/COASE.2018.8560433 |
---|
Author | Search for: Ashouri, Araz1; Search for: Hu, Yitian1; Search for: Newsham, Guy R.1; Search for: Shen, Weiming1 |
---|
Affiliation | - National Research Council of Canada. Construction
|
---|
Format | Text, Article |
---|
Conference | 2018 IEEE 14th International Conference on Automation Science and Engineering (CASE), 20-24 August 2018, Munich, Germany |
---|
Subject | fault detection and diagnosis; building energy management; energy auditing; data analysis; electricity demand |
---|
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. |
---|
Publication date | 2018-12-06 |
---|
Publisher | IEEE |
---|
In | |
---|
Language | English |
---|
Peer reviewed | Yes |
---|
NRC number | NRCC-CONST-56261E |
---|
Export citation | Export as RIS |
---|
Report a correction | Report a correction (opens in a new tab) |
---|
Record identifier | 4a2c0581-f009-4692-a91c-c6c91a1f71d9 |
---|
Record created | 2019-04-11 |
---|
Record modified | 2020-06-03 |
---|