Transfer Learning for Non-Intrusive Load Monitoring
- Submitting institution
-
University of Aberdeen
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 171257059
- Type
- D - Journal article
- DOI
-
10.1109/TSG.2019.2938068
- Title of journal
- IEEE Transactions on Smart Grid
- Article number
- 19391868
- First page
- 1419
- Volume
- 11
- Issue
- 2
- ISSN
- 1949-3053
- Open access status
- Technical exception
- Month of publication
- August
- Year of publication
- 2019
- 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
- Yes
- Number of additional authors
-
2
- Research group(s)
-
-
- Citation count
- 10
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper proposed transfer learning for Non-Intrusive Load Monitoring (NILM) for approaching oracle NILM. This paper is crucial for the NILM community because most current research in NILM only address training and testing the model on the same application domain, but in real-world applications when the NILM system is deployed the algorithms need to be able to disaggregate appliances of unseen houses and even of those houses in different countries. This paper proposed two strategies to disaggregate appliance for those unseen houses and as well as houses in different countries.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
