Clustering disaggregated load profiles using a Dirichlet process mixture model
- Submitting institution
-
University of Oxford
- Unit of assessment
- 12 - Engineering
- Output identifier
- 9532
- Type
- D - Journal article
- DOI
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10.1016/j.enconman.2014.12.080
- Title of journal
- Energy Conversion and Management
- Article number
- -
- First page
- 507
- Volume
- 92
- Issue
- -
- ISSN
- 0196-8904
- Open access status
- Out of scope for open access requirements
- Month of publication
- January
- Year of publication
- 2015
- 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
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2
- Research group(s)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The utilisation of smart meter data to understand the drivers of consumption will be essential if we are to successfully utilise demand response as a way of limiting energy consumption during times of peak load. Utilising a Baysean clustering methodology rather than a more simple K means or similar means that the clusters developed within the data are no longer dependent on intuition of the researcher but inherent features of the data itself. This work when submitted was to our understanding globally unique.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -