Robust event-based non-intrusive appliance recognition using multi-scale wavelet packet tree and ensemble bagging tree
- Submitting institution
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De Montfort University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 11270
- Type
- D - Journal article
- DOI
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10.1016/j.apenergy.2020.114877
- Title of journal
- Applied Energy
- Article number
- 114877
- First page
- -
- Volume
- 267
- Issue
- -
- ISSN
- 0306-2619
- Open access status
- Compliant
- Month of publication
- April
- Year of publication
- 2020
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
3
- Research group(s)
-
-
- Citation count
- 11
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- We propose in this paper a novel powerful non-intrusive load monitoring system using a powerful frequency-based technique to detect appliance events, a novel multi-scale wavelet packet tree descriptor to extract relevant features and improved architecture of the ensemble bagging tree for device identification. The work is one of EM3 project outputs, a funded NPRP-QNRF project in collaboration with Qatar University, HUA and ITML in Greece. With significant impact in machine learning applied to energy and high performance of appliance recognition, this work is based on the work presented at ISCAS2019 and ICPR2020, two IEEE prestigious conferences.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -