C-Vine copula mixture model for clustering of residential electrical load pattern data
File(s)
Accepted version
Author(s)
Sun, M
Konstantelos, I
Strbac, G
Type
Journal Article
Abstract
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.
Date Issued
2016-09-28
Date Acceptance
2016-09-01
Citation
IEEE Transactions on Power Systems, 2016, 32 (3), pp.2382-2393
ISSN
0885-8950
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Start Page
2382
End Page
2393
Journal / Book Title
IEEE Transactions on Power Systems
Volume
32
Issue
3
Copyright Statement
© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Subjects
Science & Technology
Technology
Engineering, Electrical & Electronic
Engineering
Clustering
customer classification
C-vine
decision trees
mixture models
pair-copula construction
smart meters
SMART-METER DATA
CUSTOMERS
CLASSIFICATION
CURVES
CONSUMPTION
PROFILES
Energy
0906 Electrical And Electronic Engineering
Publication Status
Published