An unsupervised training method for non-intrusive appliance load monitoring
- Submitting institution
-
University of Southampton
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 34228918
- Type
- D - Journal article
- DOI
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10.1016/j.artint.2014.07.010
- Title of journal
- Artificial Intelligence
- Article number
- -
- First page
- 1
- Volume
- 217
- Issue
- -
- ISSN
- 0004-3702
- Open access status
- Out of scope for open access requirements
- Month of publication
- July
- Year of publication
- 2014
- 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
- 88
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This article, extended from a best paper winning NIPS paper (https://eprints.soton.ac.uk/272990/), describes a novel unsupervised training method that advances the state of the art in automatically disaggregating appliances for aggregate load measurements. The work lead to the development of a long running workshop series (http://www.nilm.eu/) and the NILMTK toolkit (http://nilmtk.github.io) for non-intrusive load monitoring which one the best demo award at the ACM BuildSys 2014.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -