Clustering disaggregated load profiles using a Dirichlet process mixture model
- Submitting institution
-
Brunel University London
- Unit of assessment
- 12 - Engineering
- Output identifier
- 271-109025-8298
- Type
- D - Journal article
- DOI
-
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
-
2
- Research group(s)
-
1 - Energy & Environment
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Real-world power and energy use data is noisy and presents many problems for processing and mining; this method is robust and computationally scalable.. The use of UK and Bulgarian data is for illustrative purposes only, the method is internationally applicable for discovering patterns and clusters of daily profiles with noisy data. One of a series of papers resulting from EPSRC grant (EP/I000194/1 Advanced Dynamic Energy Pricing and Tariffs (ADEPT)).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -