Decision Tree and SVM-Based Data Analytics for Theft Detection in Smart Grid
- Submitting institution
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The University of Essex
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1429
- Type
- D - Journal article
- DOI
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10.1109/tii.2016.2543145
- Title of journal
- IEEE Transactions on Industrial Informatics
- Article number
- -
- First page
- 1005
- Volume
- 12
- Issue
- 3
- ISSN
- 1551-3203
- Open access status
- Out of scope for open access requirements
- Month of publication
- March
- Year of publication
- 2016
- 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
-
5
- Research group(s)
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A - Artificial Intelligence (AI)
- Citation count
- 106
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This highly-cited (>3500 downloads) interdisciplinary work, listed as one of the most popular articles for four months after publication, won the "2019 IEEE TCSC Outstanding Ph.D. Dissertation Award" recognising contributions combining theory and practice. Significantly, it proposed a comprehensive data-analytical scheme integrating decision tree and support vector machine to precisely detect real-time electricity theft (estimated $96bnp.a.) in power systems. This paper outperformed several state-of-the-art techniques in terms of very high detection accuracy (~18% better) and very low false positive ratio (5.12%) and influenced various other studies published in energy theft detection (Karabiber; Toma et al.; Saeed et al. 2019).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -