@article{Sun:2016:10.1109/TPWRS.2016.2614366,
author = {Sun, M and Konstantelos, I and Strbac, G},
doi = {10.1109/TPWRS.2016.2614366},
journal = {IEEE Transactions on Power Systems},
pages = {2382--2393},
title = {C-Vine copula mixture model for clustering of residential electrical load pattern data},
url = {http://dx.doi.org/10.1109/TPWRS.2016.2614366},
volume = {32},
year = {2016}
}

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TY  - JOUR
AB  - The ongoing deployment of residential smart meters in numerous jurisdictions has led to an influx of electricity consumption data. This information presents a valuable opportunity to suppliers for better understanding their customer base and designing more effective tariff structures. In the past, various clustering methods have been proposed for meaningful customer partitioning. This paper presents a novel finite mixture modeling framework based on C-vine copulas (CVMM) for carrying out consumer categorization. The superiority of the proposed framework lies in the great flexibility of pair copulas towards identifying multi-dimensional dependency structures present in load profiling data. CVMM is compared to other classical methods by using real demand measurements recorded across 2,613 households in a London smart-metering trial. The superior performance of the proposed approach is demonstrated by analyzing four validity indicators. In addition, a decision tree classification module for partitioning new consumers is developed and the improved predictive performance of CVMM compared to existing methods is highlighted. Further case studies are carried out based on different loading conditions and different sets of large numbers of households to demonstrate the advantages and to test the scalability of the proposed method.
AU  - Sun,M
AU  - Konstantelos,I
AU  - Strbac,G
DO  - 10.1109/TPWRS.2016.2614366
EP  - 2393
PY  - 2016///
SN  - 0885-8950
SP  - 2382
TI  - C-Vine copula mixture model for clustering of residential electrical load pattern data
T2  - IEEE Transactions on Power Systems
UR  - http://dx.doi.org/10.1109/TPWRS.2016.2614366
UR  - http://hdl.handle.net/10044/1/42645
VL  - 32
ER  - 

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