{"id":"https://openalex.org/W3195609462","doi":"https://doi.org/10.1109/tim.2021.3106678","title":"Sequence-to-Point Learning Based on Temporal Convolutional Networks for Nonintrusive Load Monitoring","display_name":"Sequence-to-Point Learning Based on Temporal Convolutional Networks for Nonintrusive Load Monitoring","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W3195609462","doi":"https://doi.org/10.1109/tim.2021.3106678","mag":"3195609462"},"language":"en","primary_location":{"id":"doi:10.1109/tim.2021.3106678","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2021.3106678","pdf_url":null,"source":{"id":"https://openalex.org/S10892749","display_name":"IEEE Transactions on Instrumentation and Measurement","issn_l":"0018-9456","issn":["0018-9456","1557-9662"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Instrumentation and Measurement","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5070814003","display_name":"Weijun Yang","orcid":"https://orcid.org/0000-0001-6206-5684"},"institutions":[{"id":"https://openalex.org/I23632641","display_name":"Shanghai University of Electric Power","ror":"https://ror.org/02w4tny03","country_code":"CN","type":"education","lineage":["https://openalex.org/I23632641"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Weijun Yang","raw_affiliation_strings":["School of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0001-6206-5684","affiliations":[{"raw_affiliation_string":"School of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai, China","institution_ids":["https://openalex.org/I23632641"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043185150","display_name":"Chengxin Pang","orcid":"https://orcid.org/0000-0003-2481-1433"},"institutions":[{"id":"https://openalex.org/I23632641","display_name":"Shanghai University of Electric Power","ror":"https://ror.org/02w4tny03","country_code":"CN","type":"education","lineage":["https://openalex.org/I23632641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chengxin Pang","raw_affiliation_strings":["School of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0003-2481-1433","affiliations":[{"raw_affiliation_string":"School of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai, China","institution_ids":["https://openalex.org/I23632641"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000003620","display_name":"Jinhai Huang","orcid":"https://orcid.org/0000-0001-6772-4415"},"institutions":[{"id":"https://openalex.org/I23632641","display_name":"Shanghai University of Electric Power","ror":"https://ror.org/02w4tny03","country_code":"CN","type":"education","lineage":["https://openalex.org/I23632641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinhai Huang","raw_affiliation_strings":["School of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0001-6772-4415","affiliations":[{"raw_affiliation_string":"School of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai, China","institution_ids":["https://openalex.org/I23632641"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5054486930","display_name":"Xinhua Zeng","orcid":"https://orcid.org/0000-0002-3903-0392"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinhua Zeng","raw_affiliation_strings":["Academy for Engineering and Technology, Fudan University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0002-3903-0392","affiliations":[{"raw_affiliation_string":"Academy for Engineering and Technology, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5070814003"],"corresponding_institution_ids":["https://openalex.org/I23632641"],"apc_list":null,"apc_paid":null,"fwci":3.1519,"has_fulltext":false,"cited_by_count":45,"citation_normalized_percentile":{"value":0.92214811,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":"70","issue":null,"first_page":"1","last_page":"10"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10603","display_name":"Smart Grid Energy Management","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10603","display_name":"Smart Grid Energy Management","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11954","display_name":"Energy Efficiency and Management","score":0.9905999898910522,"subfield":{"id":"https://openalex.org/subfields/2105","display_name":"Renewable Energy, Sustainability and the Environment"},"field":{"id":"https://openalex.org/fields/21","display_name":"Energy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6671468019485474},{"id":"https://openalex.org/keywords/activation-function","display_name":"Activation function","score":0.5380119681358337},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5012121200561523},{"id":"https://openalex.org/keywords/decomposition","display_name":"Decomposition","score":0.46721354126930237},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.41997408866882324},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4103028178215027},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3878936469554901},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.37654030323028564},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3453775644302368},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.30048131942749023}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6671468019485474},{"id":"https://openalex.org/C38365724","wikidata":"https://www.wikidata.org/wiki/Q4677469","display_name":"Activation function","level":3,"score":0.5380119681358337},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5012121200561523},{"id":"https://openalex.org/C124681953","wikidata":"https://www.wikidata.org/wiki/Q339062","display_name":"Decomposition","level":2,"score":0.46721354126930237},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.41997408866882324},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4103028178215027},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3878936469554901},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.37654030323028564},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3453775644302368},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.30048131942749023},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tim.2021.3106678","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2021.3106678","pdf_url":null,"source":{"id":"https://openalex.org/S10892749","display_name":"IEEE Transactions on Instrumentation and Measurement","issn_l":"0018-9456","issn":["0018-9456","1557-9662"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Instrumentation and Measurement","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","score":0.8899999856948853,"display_name":"Affordable and clean energy"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W1479651931","https://openalex.org/W1735866749","https://openalex.org/W1988917187","https://openalex.org/W1993297921","https://openalex.org/W1994682479","https://openalex.org/W1996944908","https://openalex.org/W2006478669","https://openalex.org/W2043279449","https://openalex.org/W2046031607","https://openalex.org/W2047861906","https://openalex.org/W2081314233","https://openalex.org/W2088953431","https://openalex.org/W2093726391","https://openalex.org/W2123910460","https://openalex.org/W2132991226","https://openalex.org/W2170014209","https://openalex.org/W2289761462","https://openalex.org/W2292311507","https://openalex.org/W2404840071","https://openalex.org/W2566212894","https://openalex.org/W2750941888","https://openalex.org/W2764060181","https://openalex.org/W2796394553","https://openalex.org/W2899663614","https://openalex.org/W2944008045","https://openalex.org/W2949115596","https://openalex.org/W2963030810","https://openalex.org/W2964666531","https://openalex.org/W3092177662","https://openalex.org/W3095472304","https://openalex.org/W3099873379","https://openalex.org/W3103730413","https://openalex.org/W3160603218","https://openalex.org/W6634986005","https://openalex.org/W6649617573","https://openalex.org/W6662319118","https://openalex.org/W6731843568","https://openalex.org/W6755977528"],"related_works":["https://openalex.org/W3110577345","https://openalex.org/W2946235959","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W3167935049","https://openalex.org/W3029198973","https://openalex.org/W2806426850","https://openalex.org/W4297795098"],"abstract_inverted_index":{"Nonintrusive":[0,34],"load":[1,22,35,93,145,216],"monitoring":[2,7],"(NILM)":[3],"is":[4,37,45,62],"performed":[5],"by":[6],"the":[8,14,51,65,70,75,89,103,107,117,120,125,144,150,156,163,166,172,179,184,188,192,196,200,204,212,220],"total":[9],"electricity":[10,28],"consumption":[11,29],"data":[12,30],"from":[13],"customer\u2019s":[15],"meter":[16],"and":[17,81,148,170,176],"decomposing":[18],"it":[19,100],"with":[20,134,155],"a":[21,38,129,135],"decomposition":[23,36,94,146,189,217],"algorithm":[24,85,164,185],"to":[25,47,68,74,142],"get":[26],"real-time":[27],"for":[31,195],"individual":[32],"appliances.":[33],"single-channel":[39],"blind":[40],"source":[41],"separation":[42],"problem,":[43],"which":[44],"difficult":[46],"implement":[48],"because":[49,99],"of":[50,77,106,119,140,183,191,215],"inherently":[52],"unidentifiable":[53],"problem.":[54],"Recent":[55],"studies":[56],"have":[57],"shown":[58],"that":[59,203],"deep":[60,82],"learning":[61],"gradually":[63],"becoming":[64],"mainstream":[66],"approach":[67],"solve":[69],"NILM":[71],"problem":[72],"owing":[73],"availability":[76],"data,":[78],"computing":[79],"power,":[80],"network":[83,108,121,132,207],"training":[84],"models.":[86],"Among":[87],"them,":[88],"sequence-to-point":[90],"(seq2point)":[91],"(Zhang)":[92],"model":[95,122,208,221],"achieves":[96],"state-of-the-art":[97],"prediction":[98],"concentrates":[101],"on":[102,124],"representation":[104],"ability":[105],"at":[109],"one":[110],"point.":[111],"In":[112],"this":[113],"article,":[114],"we":[115,161],"optimize":[116],"structure":[118],"based":[123],"seq2point":[126],"model,":[127,147],"use":[128],"temporal":[130,205],"convolutional":[131,206],"(TCN)":[133],"more":[136],"flexible":[137],"perceptual":[138],"field":[139],"view":[141],"train":[143],"replace":[149],"traditional":[151],"activation":[152,157],"function":[153,158],"Relu":[154],"GELU.":[159],"Finally,":[160],"test":[162],"in":[165],"public":[167],"dataset":[168],"UK-DALE":[169],"select":[171],"mean":[173],"absolute":[174],"error":[175],"F-score":[177],"as":[178],"main":[180],"evaluation":[181],"indexes":[182],"performance.":[186],"Comparing":[187],"results":[190,201],"two":[193],"algorithms":[194],"same":[197],"period":[198],"dataset,":[199],"show":[202],"has":[209],"substantially":[210],"improved":[211],"performance":[213],"index":[214],"while":[218],"reducing":[219],"parameters.":[222]},"counts_by_year":[{"year":2025,"cited_by_count":14},{"year":2024,"cited_by_count":10},{"year":2023,"cited_by_count":14},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}