Game Theoretic Market Driven Smart Home Scheduling Considering Energy Balancing
- Submitting institution
-
University of Newcastle upon Tyne
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 226306-197872-1292
- Type
- D - Journal article
- DOI
-
10.1109/JSYST.2015.2418032
- Title of journal
- IEEE Systems Journal
- Article number
- -
- First page
- 910
- Volume
- 11
- Issue
- 2
- ISSN
- 1932-8184
- Open access status
- Compliant
- Month of publication
- May
- Year of publication
- 2015
- URL
-
http://dx.doi.org/10.1109/JSYST.2015.2418032
- 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)
-
F - Networked and Ubiquitous Systems Engineering (NUSE)
- Citation count
- 14
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper – resulting from an international collaboration of a group of authors - introduced novel mathematical techniques for energy optimization, which can deliver important efficiency savings. The paper was one of a very small number (top 1%, 5 out of 598 papers were selected) selected for the 2018 Best Paper Award, by the IEEE Systems Council. The paper was published in a premier IEEE Systems Journal, and has already been cited more than 30 times (Google Scholar).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -