Analysis and Identification of Power Blackout-Sensitive Users by Using Big Data in the Energy System
With the further liberalization of the electricity market of China, customers' requirements, characteristics, and distribution, as well as the quality, security, and reliability of power supplies without interruption, have received considerable attention from power companies, policymakers, and...
Gespeichert in:
Veröffentlicht in: | IEEE access 2019, Vol.7, p.19488-19501 |
---|---|
Hauptverfasser: | , , , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 19501 |
---|---|
container_issue | |
container_start_page | 19488 |
container_title | IEEE access |
container_volume | 7 |
creator | Shuai, Chunyan Yang, Hengcheng Ouyang, Xin He, Mingwei Gong, Zeweiyi Shu, Wanneng |
description | With the further liberalization of the electricity market of China, customers' requirements, characteristics, and distribution, as well as the quality, security, and reliability of power supplies without interruption, have received considerable attention from power companies, policymakers, and researchers. How to deeply explore the distribution characteristics of electricity customers and analyze their sensitivities to electricity blackouts has become an especially important problem. This paper takes over 0.1 billion data, collected by various smart devices of the Internet of Things in the power system of China, such as smart meters, intelligent power consumption interactive terminals, data concentrators, and other cross-platform data, for example, 95 598 telephone records, complaint information, user bills, user information, and maintenance records, as study objects, to analyze the consumption characteristics of power users. It has been found that there is a wide range of power users who pay different electricity bills; a long-tail distribution following a power law lies in the number of users versus their paid electricity bills. Meanwhile, there are two Pareto effects (2-8 rule): the number of residents and non-residents versus their electricity bills, and the number of large industrial users and general industry (business users) versus in their electricity consumption and bills. Then, a decision tree algorithm is proposed to capture the characteristics of electricity consumers and to recognize the crowd who is power blackout sensitive. The evaluation indexes and parameters of the decision tree are discussed in detail, and a comparison with other intelligent algorithms shows that the decision tree has a good recognition performance over that of others, and the characteristics used to identify the blackout-sensitive crowd is various. All the results state that except for economic factors, positive social effects should also be considered. Various marketing strategies to satisfy different requirements of power users should be provided to promote long-term relationships between the power companies and power customers. |
doi_str_mv | 10.1109/ACCESS.2018.2886551 |
format | Article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2455619190</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>8576506</ieee_id><doaj_id>oai_doaj_org_article_a15151a4a7394ca0973831703fa8ee0d</doaj_id><sourcerecordid>2455619190</sourcerecordid><originalsourceid>FETCH-LOGICAL-c408t-6bec1ebbdd6de22681df31c83d243d60bfdc82622366deefdf50fd54e0e42e493</originalsourceid><addsrcrecordid>eNpNUV1P4zAQjE4gHQJ-QV8s3XOKP2LHeSy9wlVCAqnwbDn2uufSxmC7h_LvzxCE2H3Y0WpmVtqpqhnBc0Jwd7VYLlebzZxiIudUSsE5-VGdUSK6mnEmTr7hn9VlSjtcSpYVb88quxj0fkw-IT1YtLYwZO-80dmHAQWHHsIbRHS91-Y5HHO9gSH57P8BekoQE-rHAvywRdd-i37rrJEfUP4LaDVA3I5oM6YMh4vq1Ol9gsvPeV493awel3_qu_vb9XJxV5sGy1yLHgyBvrdWWKBUSGIdI0YySxtmBe6dNZIKSpkoBHDWcewsbwBDQ6Hp2Hm1nnxt0Dv1Ev1Bx1EF7dXHIsSt0jF7swelCS-tG92yrjEady2TjLSYOS0BsC1evyavlxhej5Cy2oVjLM9KijacC9KRDhcWm1gmhpQiuK-rBKv3dNSUjnpPR32mU1SzSeUB4EsheSs4Fuw_qVmLOw</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2455619190</pqid></control><display><type>article</type><title>Analysis and Identification of Power Blackout-Sensitive Users by Using Big Data in the Energy System</title><source>IEEE Open Access Journals</source><source>DOAJ Directory of Open Access Journals</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><creator>Shuai, Chunyan ; Yang, Hengcheng ; Ouyang, Xin ; He, Mingwei ; Gong, Zeweiyi ; Shu, Wanneng</creator><creatorcontrib>Shuai, Chunyan ; Yang, Hengcheng ; Ouyang, Xin ; He, Mingwei ; Gong, Zeweiyi ; Shu, Wanneng</creatorcontrib><description>With the further liberalization of the electricity market of China, customers' requirements, characteristics, and distribution, as well as the quality, security, and reliability of power supplies without interruption, have received considerable attention from power companies, policymakers, and researchers. How to deeply explore the distribution characteristics of electricity customers and analyze their sensitivities to electricity blackouts has become an especially important problem. This paper takes over 0.1 billion data, collected by various smart devices of the Internet of Things in the power system of China, such as smart meters, intelligent power consumption interactive terminals, data concentrators, and other cross-platform data, for example, 95 598 telephone records, complaint information, user bills, user information, and maintenance records, as study objects, to analyze the consumption characteristics of power users. It has been found that there is a wide range of power users who pay different electricity bills; a long-tail distribution following a power law lies in the number of users versus their paid electricity bills. Meanwhile, there are two Pareto effects (2-8 rule): the number of residents and non-residents versus their electricity bills, and the number of large industrial users and general industry (business users) versus in their electricity consumption and bills. Then, a decision tree algorithm is proposed to capture the characteristics of electricity consumers and to recognize the crowd who is power blackout sensitive. The evaluation indexes and parameters of the decision tree are discussed in detail, and a comparison with other intelligent algorithms shows that the decision tree has a good recognition performance over that of others, and the characteristics used to identify the blackout-sensitive crowd is various. All the results state that except for economic factors, positive social effects should also be considered. Various marketing strategies to satisfy different requirements of power users should be provided to promote long-term relationships between the power companies and power customers.</description><identifier>ISSN: 2169-3536</identifier><identifier>EISSN: 2169-3536</identifier><identifier>DOI: 10.1109/ACCESS.2018.2886551</identifier><identifier>CODEN: IAECCG</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Algorithms ; big data ; Blackout ; Blackout sensitivity ; Commercial energy ; Concentrators ; Consumption ; Customers ; Data mining ; decision tree ; Decision trees ; Economic factors ; Economics ; Electric power systems ; Electricity ; Electricity consumption ; electricity market ; Electricity meters ; Electricity supply industry ; Electronic devices ; Internet of Things ; long-tailed ; Parameter sensitivity ; Pareto effect ; Performance indices ; Power consumption ; Power management ; Power supplies ; Power system reliability ; Security ; Sensitivity analysis ; User requirements ; User satisfaction</subject><ispartof>IEEE access, 2019, Vol.7, p.19488-19501</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2019</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c408t-6bec1ebbdd6de22681df31c83d243d60bfdc82622366deefdf50fd54e0e42e493</citedby><cites>FETCH-LOGICAL-c408t-6bec1ebbdd6de22681df31c83d243d60bfdc82622366deefdf50fd54e0e42e493</cites><orcidid>0000-0001-8282-2697</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/8576506$$EHTML$$P50$$Gieee$$Hfree_for_read</linktohtml><link.rule.ids>314,776,780,860,2096,4010,27610,27900,27901,27902,54908</link.rule.ids></links><search><creatorcontrib>Shuai, Chunyan</creatorcontrib><creatorcontrib>Yang, Hengcheng</creatorcontrib><creatorcontrib>Ouyang, Xin</creatorcontrib><creatorcontrib>He, Mingwei</creatorcontrib><creatorcontrib>Gong, Zeweiyi</creatorcontrib><creatorcontrib>Shu, Wanneng</creatorcontrib><title>Analysis and Identification of Power Blackout-Sensitive Users by Using Big Data in the Energy System</title><title>IEEE access</title><addtitle>Access</addtitle><description>With the further liberalization of the electricity market of China, customers' requirements, characteristics, and distribution, as well as the quality, security, and reliability of power supplies without interruption, have received considerable attention from power companies, policymakers, and researchers. How to deeply explore the distribution characteristics of electricity customers and analyze their sensitivities to electricity blackouts has become an especially important problem. This paper takes over 0.1 billion data, collected by various smart devices of the Internet of Things in the power system of China, such as smart meters, intelligent power consumption interactive terminals, data concentrators, and other cross-platform data, for example, 95 598 telephone records, complaint information, user bills, user information, and maintenance records, as study objects, to analyze the consumption characteristics of power users. It has been found that there is a wide range of power users who pay different electricity bills; a long-tail distribution following a power law lies in the number of users versus their paid electricity bills. Meanwhile, there are two Pareto effects (2-8 rule): the number of residents and non-residents versus their electricity bills, and the number of large industrial users and general industry (business users) versus in their electricity consumption and bills. Then, a decision tree algorithm is proposed to capture the characteristics of electricity consumers and to recognize the crowd who is power blackout sensitive. The evaluation indexes and parameters of the decision tree are discussed in detail, and a comparison with other intelligent algorithms shows that the decision tree has a good recognition performance over that of others, and the characteristics used to identify the blackout-sensitive crowd is various. All the results state that except for economic factors, positive social effects should also be considered. Various marketing strategies to satisfy different requirements of power users should be provided to promote long-term relationships between the power companies and power customers.</description><subject>Algorithms</subject><subject>big data</subject><subject>Blackout</subject><subject>Blackout sensitivity</subject><subject>Commercial energy</subject><subject>Concentrators</subject><subject>Consumption</subject><subject>Customers</subject><subject>Data mining</subject><subject>decision tree</subject><subject>Decision trees</subject><subject>Economic factors</subject><subject>Economics</subject><subject>Electric power systems</subject><subject>Electricity</subject><subject>Electricity consumption</subject><subject>electricity market</subject><subject>Electricity meters</subject><subject>Electricity supply industry</subject><subject>Electronic devices</subject><subject>Internet of Things</subject><subject>long-tailed</subject><subject>Parameter sensitivity</subject><subject>Pareto effect</subject><subject>Performance indices</subject><subject>Power consumption</subject><subject>Power management</subject><subject>Power supplies</subject><subject>Power system reliability</subject><subject>Security</subject><subject>Sensitivity analysis</subject><subject>User requirements</subject><subject>User satisfaction</subject><issn>2169-3536</issn><issn>2169-3536</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><sourceid>DOA</sourceid><recordid>eNpNUV1P4zAQjE4gHQJ-QV8s3XOKP2LHeSy9wlVCAqnwbDn2uufSxmC7h_LvzxCE2H3Y0WpmVtqpqhnBc0Jwd7VYLlebzZxiIudUSsE5-VGdUSK6mnEmTr7hn9VlSjtcSpYVb88quxj0fkw-IT1YtLYwZO-80dmHAQWHHsIbRHS91-Y5HHO9gSH57P8BekoQE-rHAvywRdd-i37rrJEfUP4LaDVA3I5oM6YMh4vq1Ol9gsvPeV493awel3_qu_vb9XJxV5sGy1yLHgyBvrdWWKBUSGIdI0YySxtmBe6dNZIKSpkoBHDWcewsbwBDQ6Hp2Hm1nnxt0Dv1Ev1Bx1EF7dXHIsSt0jF7swelCS-tG92yrjEady2TjLSYOS0BsC1evyavlxhej5Cy2oVjLM9KijacC9KRDhcWm1gmhpQiuK-rBKv3dNSUjnpPR32mU1SzSeUB4EsheSs4Fuw_qVmLOw</recordid><startdate>2019</startdate><enddate>2019</enddate><creator>Shuai, Chunyan</creator><creator>Yang, Hengcheng</creator><creator>Ouyang, Xin</creator><creator>He, Mingwei</creator><creator>Gong, Zeweiyi</creator><creator>Shu, Wanneng</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>ESBDL</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>7SR</scope><scope>8BQ</scope><scope>8FD</scope><scope>JG9</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0001-8282-2697</orcidid></search><sort><creationdate>2019</creationdate><title>Analysis and Identification of Power Blackout-Sensitive Users by Using Big Data in the Energy System</title><author>Shuai, Chunyan ; Yang, Hengcheng ; Ouyang, Xin ; He, Mingwei ; Gong, Zeweiyi ; Shu, Wanneng</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c408t-6bec1ebbdd6de22681df31c83d243d60bfdc82622366deefdf50fd54e0e42e493</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Algorithms</topic><topic>big data</topic><topic>Blackout</topic><topic>Blackout sensitivity</topic><topic>Commercial energy</topic><topic>Concentrators</topic><topic>Consumption</topic><topic>Customers</topic><topic>Data mining</topic><topic>decision tree</topic><topic>Decision trees</topic><topic>Economic factors</topic><topic>Economics</topic><topic>Electric power systems</topic><topic>Electricity</topic><topic>Electricity consumption</topic><topic>electricity market</topic><topic>Electricity meters</topic><topic>Electricity supply industry</topic><topic>Electronic devices</topic><topic>Internet of Things</topic><topic>long-tailed</topic><topic>Parameter sensitivity</topic><topic>Pareto effect</topic><topic>Performance indices</topic><topic>Power consumption</topic><topic>Power management</topic><topic>Power supplies</topic><topic>Power system reliability</topic><topic>Security</topic><topic>Sensitivity analysis</topic><topic>User requirements</topic><topic>User satisfaction</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Shuai, Chunyan</creatorcontrib><creatorcontrib>Yang, Hengcheng</creatorcontrib><creatorcontrib>Ouyang, Xin</creatorcontrib><creatorcontrib>He, Mingwei</creatorcontrib><creatorcontrib>Gong, Zeweiyi</creatorcontrib><creatorcontrib>Shu, Wanneng</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE Open Access Journals</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>IEEE access</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Shuai, Chunyan</au><au>Yang, Hengcheng</au><au>Ouyang, Xin</au><au>He, Mingwei</au><au>Gong, Zeweiyi</au><au>Shu, Wanneng</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Analysis and Identification of Power Blackout-Sensitive Users by Using Big Data in the Energy System</atitle><jtitle>IEEE access</jtitle><stitle>Access</stitle><date>2019</date><risdate>2019</risdate><volume>7</volume><spage>19488</spage><epage>19501</epage><pages>19488-19501</pages><issn>2169-3536</issn><eissn>2169-3536</eissn><coden>IAECCG</coden><abstract>With the further liberalization of the electricity market of China, customers' requirements, characteristics, and distribution, as well as the quality, security, and reliability of power supplies without interruption, have received considerable attention from power companies, policymakers, and researchers. How to deeply explore the distribution characteristics of electricity customers and analyze their sensitivities to electricity blackouts has become an especially important problem. This paper takes over 0.1 billion data, collected by various smart devices of the Internet of Things in the power system of China, such as smart meters, intelligent power consumption interactive terminals, data concentrators, and other cross-platform data, for example, 95 598 telephone records, complaint information, user bills, user information, and maintenance records, as study objects, to analyze the consumption characteristics of power users. It has been found that there is a wide range of power users who pay different electricity bills; a long-tail distribution following a power law lies in the number of users versus their paid electricity bills. Meanwhile, there are two Pareto effects (2-8 rule): the number of residents and non-residents versus their electricity bills, and the number of large industrial users and general industry (business users) versus in their electricity consumption and bills. Then, a decision tree algorithm is proposed to capture the characteristics of electricity consumers and to recognize the crowd who is power blackout sensitive. The evaluation indexes and parameters of the decision tree are discussed in detail, and a comparison with other intelligent algorithms shows that the decision tree has a good recognition performance over that of others, and the characteristics used to identify the blackout-sensitive crowd is various. All the results state that except for economic factors, positive social effects should also be considered. Various marketing strategies to satisfy different requirements of power users should be provided to promote long-term relationships between the power companies and power customers.</abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/ACCESS.2018.2886551</doi><tpages>14</tpages><orcidid>https://orcid.org/0000-0001-8282-2697</orcidid><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 2169-3536 |
ispartof | IEEE access, 2019, Vol.7, p.19488-19501 |
issn | 2169-3536 2169-3536 |
language | eng |
recordid | cdi_proquest_journals_2455619190 |
source | IEEE Open Access Journals; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals |
subjects | Algorithms big data Blackout Blackout sensitivity Commercial energy Concentrators Consumption Customers Data mining decision tree Decision trees Economic factors Economics Electric power systems Electricity Electricity consumption electricity market Electricity meters Electricity supply industry Electronic devices Internet of Things long-tailed Parameter sensitivity Pareto effect Performance indices Power consumption Power management Power supplies Power system reliability Security Sensitivity analysis User requirements User satisfaction |
title | Analysis and Identification of Power Blackout-Sensitive Users by Using Big Data in the Energy System |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-31T10%3A50%3A28IST&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=Analysis%20and%20Identification%20of%20Power%20Blackout-Sensitive%20Users%20by%20Using%20Big%20Data%20in%20the%20Energy%20System&rft.jtitle=IEEE%20access&rft.au=Shuai,%20Chunyan&rft.date=2019&rft.volume=7&rft.spage=19488&rft.epage=19501&rft.pages=19488-19501&rft.issn=2169-3536&rft.eissn=2169-3536&rft.coden=IAECCG&rft_id=info:doi/10.1109/ACCESS.2018.2886551&rft_dat=%3Cproquest_cross%3E2455619190%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2455619190&rft_id=info:pmid/&rft_ieee_id=8576506&rft_doaj_id=oai_doaj_org_article_a15151a4a7394ca0973831703fa8ee0d&rfr_iscdi=true |