Loading…

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...

Full description

Saved in:
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:
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c469t-b0751afdc7b90d55f762a2630e521221881c378f07c0dd65e21084b1a1cfea723
cites cdi_FETCH-LOGICAL-c469t-b0751afdc7b90d55f762a2630e521221881c378f07c0dd65e21084b1a1cfea723
container_end_page 940
container_issue 2
container_start_page 933
container_title IEEE transactions on power systems
container_volume 21
creator Chicco, G.
Napoli, R.
Piglione, F.
description 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.
doi_str_mv 10.1109/TPWRS.2006.873122
format article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_crossref_primary_10_1109_TPWRS_2006_873122</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>1626400</ieee_id><sourcerecordid>896207827</sourcerecordid><originalsourceid>FETCH-LOGICAL-c469t-b0751afdc7b90d55f762a2630e521221881c378f07c0dd65e21084b1a1cfea723</originalsourceid><addsrcrecordid>eNp90U1LxDAQBuAgCq6rP0C8FA966jpJm48eZfELhBVd8RiyaaJZ2mZN2sP-e1NXEDx4ykCeGWZ4ETrFMMMYqqvl09vzy4wAsJngBSZkD00wpSIHxqt9NAEhaC4qCofoKMY1JJg-Jmgx9-1GBRd9FzPV-u49080QexNcKnujPzr3OZiYWR8y0xjdB6ddv810Qr41IXEVo7NOq9757hgdWNVEc_LzTtHr7c1yfp8_Lu4e5tePuS5Z1ecr4BQrW2u-qqCm1HJGFGEFGErS8lgIrAsuLHANdc2oIRhEucIKa2sUJ8UUXe7mboIf9-tl66I2TaM644coRcUIcEF4khf_SsIrwUhJEzz_A9d-CF26QgpGRcGBjAjvkA4-xmCs3ATXqrCVGOSYhPxOQo5JyF0Sqeds1-OMMb-eEVYCFF8KkIV3</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>865837025</pqid></control><display><type>article</type><title>Comparisons among clustering techniques for electricity customer classification</title><source>IEEE Xplore (Online service)</source><creator>Chicco, G. ; Napoli, R. ; Piglione, F.</creator><creatorcontrib>Chicco, G. ; Napoli, R. ; Piglione, F.</creatorcontrib><description>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.</description><identifier>ISSN: 0885-8950</identifier><identifier>EISSN: 1558-0679</identifier><identifier>DOI: 10.1109/TPWRS.2006.873122</identifier><identifier>CODEN: ITPSEG</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>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</subject><ispartof>IEEE transactions on power systems, 2006-05, Vol.21 (2), p.933-940</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2006</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c469t-b0751afdc7b90d55f762a2630e521221881c378f07c0dd65e21084b1a1cfea723</citedby><cites>FETCH-LOGICAL-c469t-b0751afdc7b90d55f762a2630e521221881c378f07c0dd65e21084b1a1cfea723</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/1626400$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,54796</link.rule.ids></links><search><creatorcontrib>Chicco, G.</creatorcontrib><creatorcontrib>Napoli, R.</creatorcontrib><creatorcontrib>Piglione, F.</creatorcontrib><title>Comparisons among clustering techniques for electricity customer classification</title><title>IEEE transactions on power systems</title><addtitle>TPWRS</addtitle><description>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.</description><subject>Algorithms</subject><subject>Buildings</subject><subject>Classification</subject><subject>Clustering</subject><subject>Clustering algorithms</subject><subject>Condition monitoring</subject><subject>curvilinear component analysis</subject><subject>customer classification</subject><subject>Electricity</subject><subject>follow-the-leader</subject><subject>Fuzzy</subject><subject>fuzzy K-means</subject><subject>Fuzzy logic</subject><subject>hierarchical clustering</subject><subject>K-means</subject><subject>load pattern</subject><subject>Neural networks</subject><subject>Pattern analysis</subject><subject>Principal component analysis</subject><subject>Sammon map</subject><subject>self-organizing map (SOM)</subject><subject>Studies</subject><subject>Tariffs</subject><subject>Testing</subject><issn>0885-8950</issn><issn>1558-0679</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2006</creationdate><recordtype>article</recordtype><recordid>eNp90U1LxDAQBuAgCq6rP0C8FA966jpJm48eZfELhBVd8RiyaaJZ2mZN2sP-e1NXEDx4ykCeGWZ4ETrFMMMYqqvl09vzy4wAsJngBSZkD00wpSIHxqt9NAEhaC4qCofoKMY1JJg-Jmgx9-1GBRd9FzPV-u49080QexNcKnujPzr3OZiYWR8y0xjdB6ddv810Qr41IXEVo7NOq9757hgdWNVEc_LzTtHr7c1yfp8_Lu4e5tePuS5Z1ecr4BQrW2u-qqCm1HJGFGEFGErS8lgIrAsuLHANdc2oIRhEucIKa2sUJ8UUXe7mboIf9-tl66I2TaM644coRcUIcEF4khf_SsIrwUhJEzz_A9d-CF26QgpGRcGBjAjvkA4-xmCs3ATXqrCVGOSYhPxOQo5JyF0Sqeds1-OMMb-eEVYCFF8KkIV3</recordid><startdate>20060501</startdate><enddate>20060501</enddate><creator>Chicco, G.</creator><creator>Napoli, R.</creator><creator>Piglione, F.</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>KR7</scope><scope>L7M</scope><scope>F28</scope></search><sort><creationdate>20060501</creationdate><title>Comparisons among clustering techniques for electricity customer classification</title><author>Chicco, G. ; Napoli, R. ; Piglione, F.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c469t-b0751afdc7b90d55f762a2630e521221881c378f07c0dd65e21084b1a1cfea723</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2006</creationdate><topic>Algorithms</topic><topic>Buildings</topic><topic>Classification</topic><topic>Clustering</topic><topic>Clustering algorithms</topic><topic>Condition monitoring</topic><topic>curvilinear component analysis</topic><topic>customer classification</topic><topic>Electricity</topic><topic>follow-the-leader</topic><topic>Fuzzy</topic><topic>fuzzy K-means</topic><topic>Fuzzy logic</topic><topic>hierarchical clustering</topic><topic>K-means</topic><topic>load pattern</topic><topic>Neural networks</topic><topic>Pattern analysis</topic><topic>Principal component analysis</topic><topic>Sammon map</topic><topic>self-organizing map (SOM)</topic><topic>Studies</topic><topic>Tariffs</topic><topic>Testing</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chicco, G.</creatorcontrib><creatorcontrib>Napoli, R.</creatorcontrib><creatorcontrib>Piglione, F.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEL</collection><collection>CrossRef</collection><collection>Electronics &amp; Communications Abstracts</collection><collection>Mechanical &amp; Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>ANTE: Abstracts in New Technology &amp; Engineering</collection><jtitle>IEEE transactions on power systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Chicco, G.</au><au>Napoli, R.</au><au>Piglione, F.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Comparisons among clustering techniques for electricity customer classification</atitle><jtitle>IEEE transactions on power systems</jtitle><stitle>TPWRS</stitle><date>2006-05-01</date><risdate>2006</risdate><volume>21</volume><issue>2</issue><spage>933</spage><epage>940</epage><pages>933-940</pages><issn>0885-8950</issn><eissn>1558-0679</eissn><coden>ITPSEG</coden><abstract>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.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TPWRS.2006.873122</doi><tpages>8</tpages></addata></record>
fulltext fulltext
identifier ISSN: 0885-8950
ispartof IEEE transactions on power systems, 2006-05, Vol.21 (2), p.933-940
issn 0885-8950
1558-0679
language eng
recordid cdi_crossref_primary_10_1109_TPWRS_2006_873122
source IEEE Xplore (Online service)
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
title Comparisons among clustering techniques for electricity customer classification
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-25T13%3A30%3A20IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Comparisons%20among%20clustering%20techniques%20for%20electricity%20customer%20classification&rft.jtitle=IEEE%20transactions%20on%20power%20systems&rft.au=Chicco,%20G.&rft.date=2006-05-01&rft.volume=21&rft.issue=2&rft.spage=933&rft.epage=940&rft.pages=933-940&rft.issn=0885-8950&rft.eissn=1558-0679&rft.coden=ITPSEG&rft_id=info:doi/10.1109/TPWRS.2006.873122&rft_dat=%3Cproquest_cross%3E896207827%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c469t-b0751afdc7b90d55f762a2630e521221881c378f07c0dd65e21084b1a1cfea723%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=865837025&rft_id=info:pmid/&rft_ieee_id=1626400&rfr_iscdi=true