An Ensemble Detection Model using Multinomial Classification of Stochastic Gas Smart Meter Data to Improve Wellbeing Monitoring in Smart Cities
- Submitting institution
-
Liverpool John Moores University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1401
- Type
- D - Journal article
- DOI
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10.1109/ACCESS.2020.2964885
- Title of journal
- IEEE Access
- Article number
- -
- First page
- 7877
- Volume
- 8
- Issue
- -
- ISSN
- 2169-3536
- Open access status
- Compliant
- Month of publication
- January
- 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
- Yes
- Number of additional authors
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3
- Research group(s)
-
-
- Citation count
- 0
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This work stems from research developed under EPSRC EP/R020922/1. The dataset used for the research is provided by the Irish Social Science Data Archive. The research presents a novel approach into supporting the wellbeing of vulnerable households from aggregated gas smart meter data, using cloud analytics and machine learning. This research is the first of its kind to use gas smart meter readings for wellbeing monitoring, which is published in the IEEE Access special issue on Future Generation Smart Cities Research: Services, Applications, Case Studies and Policymaking Considerations for Well-Being [Part II].
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
