A systematic approach to ON-OFF event detection and clustering analysis of non-intrusive appliance load monitoring
The aim of non-intrusive appliance load monitoring （NIALM） is to disaggregate the energy consumption of individual electrical appliances from total power consumption utilizing non-intrusive methods. In this paper, a systematic approach to 0N-0FF event detection and clustering analysis for NIALM were...
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| Veröffentlicht in: | Frontiers in Energy 2015-06, Vol.9 (2), p.231-237 |
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| creator | YANG, Chuan Choong SOH, Chit Siang YAP, Vooi Voon |
| description | The aim of non-intrusive appliance load monitoring （NIALM） is to disaggregate the energy consumption of individual electrical appliances from total power consumption utilizing non-intrusive methods. In this paper, a systematic approach to 0N-0FF event detection and clustering analysis for NIALM were presented. From the aggregate power consumption data set, the data are passed through median filtering to reduce noise and prepared for the event detection algorithm. The event detection algorithm is to determine the switching of ON and OFF status of electrical appliances. The goodness- of-fit （GOF） methodology is the event detection algorithm implemented. After event detection, the events detected were paired into ON-0FF pairing appliances. The results from the ON-OFF pairing algorithm were further clustered in groups utilizing the K-means clustering analysis. The K- means clustering were implemented as an unsupervised learning methodology for the clustering analysis. The novelty of this paper is the determination of the time duration an electrical appliance is turned ON through combination of event detection, ON-OFF pairing and K- means clustering. The results of the algorithm implemen- tation were discussed and ideas on future work were also proposed. |
| doi_str_mv | 10.1007/s11708-015-0358-6 |
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In this paper, a systematic approach to 0N-0FF event detection and clustering analysis for NIALM were presented. From the aggregate power consumption data set, the data are passed through median filtering to reduce noise and prepared for the event detection algorithm. The event detection algorithm is to determine the switching of ON and OFF status of electrical appliances. The goodness- of-fit （GOF） methodology is the event detection algorithm implemented. After event detection, the events detected were paired into ON-0FF pairing appliances. The results from the ON-OFF pairing algorithm were further clustered in groups utilizing the K-means clustering analysis. The K- means clustering were implemented as an unsupervised learning methodology for the clustering analysis. The novelty of this paper is the determination of the time duration an electrical appliance is turned ON through combination of event detection, ON-OFF pairing and K- means clustering. The results of the algorithm implemen- tation were discussed and ideas on future work were also proposed.</description><identifier>ISSN: 2095-1701</identifier><identifier>EISSN: 2095-1698</identifier><identifier>DOI: 10.1007/s11708-015-0358-6</identifier><language>eng</language><publisher>Beijing: Higher Education Press</publisher><subject>Algorithms ; Appliances ; Cluster analysis ; Datasets ; Energy ; Energy consumption ; Energy management ; Energy Systems ; event detection ; goodness-of-fit (GOF) ; K-means clustering ; K-均值聚类 ; Methods ; Monitoring systems ; Noise ; Noise reduction ; non-intrusive appliance load monitoring ; ON-OFF pairing ; Power consumption ; Research Article ; Signatures ; Studies ; Washers & dryers ; 事件检测 ; 侵入性 ; 功率消耗 ; 家电 ; 系统 ; 聚类分析方法 ; 负荷监测</subject><ispartof>Frontiers in Energy, 2015-06, Vol.9 (2), p.231-237</ispartof><rights>Copyright reserved, 2014, Higher Education Press and Springer-Verlag Berlin Heidelberg</rights><rights>Higher Education Press and Springer-Verlag Berlin Heidelberg 2015</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c462t-b4f69a9ae9bfabd73c417660d247a87ecbeb7ed1979de148a4b945289de7f2ad3</citedby><cites>FETCH-LOGICAL-c462t-b4f69a9ae9bfabd73c417660d247a87ecbeb7ed1979de148a4b945289de7f2ad3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Uhttp://image.cqvip.com/vip1000/qk/71239X/71239X.jpg</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11708-015-0358-6$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11708-015-0358-6$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>YANG, Chuan Choong</creatorcontrib><creatorcontrib>SOH, Chit Siang</creatorcontrib><creatorcontrib>YAP, Vooi Voon</creatorcontrib><title>A systematic approach to ON-OFF event detection and clustering analysis of non-intrusive appliance load monitoring</title><title>Frontiers in Energy</title><addtitle>Front. Energy</addtitle><addtitle>Frontiers in Energy</addtitle><description>The aim of non-intrusive appliance load monitoring （NIALM） is to disaggregate the energy consumption of individual electrical appliances from total power consumption utilizing non-intrusive methods. In this paper, a systematic approach to 0N-0FF event detection and clustering analysis for NIALM were presented. From the aggregate power consumption data set, the data are passed through median filtering to reduce noise and prepared for the event detection algorithm. The event detection algorithm is to determine the switching of ON and OFF status of electrical appliances. The goodness- of-fit （GOF） methodology is the event detection algorithm implemented. After event detection, the events detected were paired into ON-0FF pairing appliances. The results from the ON-OFF pairing algorithm were further clustered in groups utilizing the K-means clustering analysis. The K- means clustering were implemented as an unsupervised learning methodology for the clustering analysis. The novelty of this paper is the determination of the time duration an electrical appliance is turned ON through combination of event detection, ON-OFF pairing and K- means clustering. The results of the algorithm implemen- tation were discussed and ideas on future work were also proposed.</description><subject>Algorithms</subject><subject>Appliances</subject><subject>Cluster analysis</subject><subject>Datasets</subject><subject>Energy</subject><subject>Energy consumption</subject><subject>Energy management</subject><subject>Energy Systems</subject><subject>event detection</subject><subject>goodness-of-fit (GOF)</subject><subject>K-means clustering</subject><subject>K-均值聚类</subject><subject>Methods</subject><subject>Monitoring systems</subject><subject>Noise</subject><subject>Noise reduction</subject><subject>non-intrusive appliance load monitoring</subject><subject>ON-OFF pairing</subject><subject>Power consumption</subject><subject>Research Article</subject><subject>Signatures</subject><subject>Studies</subject><subject>Washers & dryers</subject><subject>事件检测</subject><subject>侵入性</subject><subject>功率消耗</subject><subject>家电</subject><subject>系统</subject><subject>聚类分析方法</subject><subject>负荷监测</subject><issn>2095-1701</issn><issn>2095-1698</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNp9kE9LJDEQxRtRWFE_wN6CnqNJTzp_jiI7qyA7Fz2HdFI9E-lJ2iQjzLffNK3LnjxVFdTv1avXND8puaWEiLtMqSASE9phsuok5ifNeUtUhylX8vSrF4T-aK5yfiOEUEo6ItrzJt2jfMwF9qZ4i8w0pWjsDpWINn_wZr1G8AGhIAcFbPExIBMcsuOhIsmHbR3NeMw-ozigEAP2oaRD9h8wa43eBAtojMahfQy-xJm5bM4GM2a4-qwXzev618vDI37e_H56uH_GlvG24J4NXBllQPWD6Z1YWUYF58S1TBgpwPbQC3BUCeWAMmlYr1jXyjqJoTVuddHcLLr1p_cD5KLf4iFVv1lTLjvKajqkbtFly6aYc4JBT8nvTTpqSvScrl7S1TVdPaereWXahcnT_BCk_5S_geQC7fx2BwnclCBnPaQYiof0PXr96XEXw_a9nvxnkvOOKsUkW_0Fc2ScPQ</recordid><startdate>20150601</startdate><enddate>20150601</enddate><creator>YANG, 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systematic approach to ON-OFF event detection and clustering analysis of non-intrusive appliance load monitoring</title><author>YANG, Chuan Choong ; SOH, Chit Siang ; YAP, Vooi Voon</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c462t-b4f69a9ae9bfabd73c417660d247a87ecbeb7ed1979de148a4b945289de7f2ad3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Algorithms</topic><topic>Appliances</topic><topic>Cluster analysis</topic><topic>Datasets</topic><topic>Energy</topic><topic>Energy consumption</topic><topic>Energy management</topic><topic>Energy Systems</topic><topic>event detection</topic><topic>goodness-of-fit (GOF)</topic><topic>K-means clustering</topic><topic>K-均值聚类</topic><topic>Methods</topic><topic>Monitoring systems</topic><topic>Noise</topic><topic>Noise reduction</topic><topic>non-intrusive appliance load monitoring</topic><topic>ON-OFF pairing</topic><topic>Power consumption</topic><topic>Research Article</topic><topic>Signatures</topic><topic>Studies</topic><topic>Washers & dryers</topic><topic>事件检测</topic><topic>侵入性</topic><topic>功率消耗</topic><topic>家电</topic><topic>系统</topic><topic>聚类分析方法</topic><topic>负荷监测</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>YANG, Chuan Choong</creatorcontrib><creatorcontrib>SOH, Chit Siang</creatorcontrib><creatorcontrib>YAP, Vooi Voon</creatorcontrib><collection>中文科技期刊数据库</collection><collection>中文科技期刊数据库-CALIS站点</collection><collection>中文科技期刊数据库-7.0平台</collection><collection>中文科技期刊数据库-工程技术</collection><collection>中文科技期刊数据库- 镜像站点</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Electronics & Communications Abstracts</collection><collection>Environment Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Access via ABI/INFORM 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Energy</stitle><addtitle>Frontiers in Energy</addtitle><date>2015-06-01</date><risdate>2015</risdate><volume>9</volume><issue>2</issue><spage>231</spage><epage>237</epage><pages>231-237</pages><issn>2095-1701</issn><eissn>2095-1698</eissn><abstract>The aim of non-intrusive appliance load monitoring （NIALM） is to disaggregate the energy consumption of individual electrical appliances from total power consumption utilizing non-intrusive methods. In this paper, a systematic approach to 0N-0FF event detection and clustering analysis for NIALM were presented. From the aggregate power consumption data set, the data are passed through median filtering to reduce noise and prepared for the event detection algorithm. The event detection algorithm is to determine the switching of ON and OFF status of electrical appliances. The goodness- of-fit （GOF） methodology is the event detection algorithm implemented. After event detection, the events detected were paired into ON-0FF pairing appliances. The results from the ON-OFF pairing algorithm were further clustered in groups utilizing the K-means clustering analysis. The K- means clustering were implemented as an unsupervised learning methodology for the clustering analysis. The novelty of this paper is the determination of the time duration an electrical appliance is turned ON through combination of event detection, ON-OFF pairing and K- means clustering. The results of the algorithm implemen- tation were discussed and ideas on future work were also proposed.</abstract><cop>Beijing</cop><pub>Higher Education Press</pub><doi>10.1007/s11708-015-0358-6</doi><tpages>7</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Algorithms Appliances Cluster analysis Datasets Energy Energy consumption Energy management Energy Systems event detection goodness-of-fit (GOF) K-means clustering K-均值聚类 Methods Monitoring systems Noise Noise reduction non-intrusive appliance load monitoring ON-OFF pairing Power consumption Research Article Signatures Studies Washers & dryers 事件检测 侵入性 功率消耗 家电 系统 聚类分析方法 负荷监测 |
| title | A systematic approach to ON-OFF event detection and clustering analysis of non-intrusive appliance load monitoring |
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