Comparisons among clustering techniques for electricity customer classification

The recent evolution of the electricity business regulation has given new possibilities to the electricity providers for formulating dedicated tariff offers. A key aspect for building specific tariff structures is the identification of the consumption patterns of the customers, in order to form spec...

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Bibliographic Details
Published in:IEEE transactions on power systems 2006-05, Vol.21 (2), p.933-940
Main Authors: Chicco, G., Napoli, R., Piglione, F.
Format: Article
Language:English
Subjects:
Algorithms
Buildings
Classification
Clustering
Clustering algorithms
Condition monitoring
curvilinear component analysis
customer classification
Electricity
follow-the-leader
Fuzzy
fuzzy K-means
Fuzzy logic
hierarchical clustering
K-means
load pattern
Neural networks
Pattern analysis
Principal component analysis
Sammon map
self-organizing map (SOM)
Studies
Tariffs
Testing
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Summary:The recent evolution of the electricity business regulation has given new possibilities to the electricity providers for formulating dedicated tariff offers. A key aspect for building specific tariff structures is the identification of the consumption patterns of the customers, in order to form specific customer classes containing customers exhibiting similar patterns. This paper illustrates and compares the results obtained by using various unsupervised clustering algorithms (modified follow-the-leader, hierarchical clustering, K-means, fuzzy K-means) and the self-organizing maps to group together customers with similar electrical behavior. Furthermore, this paper discusses and compares various techniques-Sammon map, principal component analysis (PCA), and curvilinear component analysis (CCA)-able to reduce the size of the clustering input data set, in order to allow for storing a relatively small amount of data in the database of the distribution service provider for customer classification purposes. The effectiveness of the classifications obtained with the algorithms tested is compared in terms of a set of clustering validity indicators. Results obtained on a set of nonresidential customers are presented.
ISSN:0885-8950
1558-0679
DOI:10.1109/TPWRS.2006.873122