{"id":"https://openalex.org/W2990279883","doi":"https://doi.org/10.1109/ias.2019.8912379","title":"Non-Intrusive Appliance Identification with Appliance-Specific Networks","display_name":"Non-Intrusive Appliance Identification with Appliance-Specific Networks","publication_year":2019,"publication_date":"2019-09-01","ids":{"openalex":"https://openalex.org/W2990279883","doi":"https://doi.org/10.1109/ias.2019.8912379","mag":"2990279883"},"language":"en","primary_location":{"id":"doi:10.1109/ias.2019.8912379","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ias.2019.8912379","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE Industry Applications Society Annual Meeting","raw_type":"proceedings-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/A5102739579","display_name":"Zhaoyuan Fang","orcid":"https://orcid.org/0000-0002-6671-3279"},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Zhaoyuan Fang","raw_affiliation_strings":["Department of Electrical Engineering, University of Notre Dame, Notre Dame, IN, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, University of Notre Dame, Notre Dame, IN, USA","institution_ids":["https://openalex.org/I107639228"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057667244","display_name":"Dongbo Zhao","orcid":"https://orcid.org/0000-0003-4401-5792"},"institutions":[{"id":"https://openalex.org/I1282105669","display_name":"Argonne National Laboratory","ror":"https://ror.org/05gvnxz63","country_code":"US","type":"facility","lineage":["https://openalex.org/I1282105669","https://openalex.org/I1330989302","https://openalex.org/I39565521","https://openalex.org/I40347166"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dongbo Zhao","raw_affiliation_strings":["Energy Systems Division, Argonne National Laboratory, Argonne, IL, USA"],"affiliations":[{"raw_affiliation_string":"Energy Systems Division, Argonne National Laboratory, Argonne, IL, USA","institution_ids":["https://openalex.org/I1282105669"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100418499","display_name":"Chen Chen","orcid":"https://orcid.org/0000-0002-8364-5749"},"institutions":[{"id":"https://openalex.org/I1282105669","display_name":"Argonne National Laboratory","ror":"https://ror.org/05gvnxz63","country_code":"US","type":"facility","lineage":["https://openalex.org/I1282105669","https://openalex.org/I1330989302","https://openalex.org/I39565521","https://openalex.org/I40347166"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chen Chen","raw_affiliation_strings":["Energy Systems Division, Argonne National Laboratory, Argonne, IL, USA"],"affiliations":[{"raw_affiliation_string":"Energy Systems Division, Argonne National Laboratory, Argonne, IL, USA","institution_ids":["https://openalex.org/I1282105669"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100421591","display_name":"Yang Li","orcid":"https://orcid.org/0000-0002-6515-4567"},"institutions":[{"id":"https://openalex.org/I179060312","display_name":"Northeast Electric Power University","ror":"https://ror.org/00zqaxa34","country_code":"CN","type":"education","lineage":["https://openalex.org/I179060312"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yang Li","raw_affiliation_strings":["School of Electrical Engineering, Northeast Electric Power University, Jilin, China"],"affiliations":[{"raw_affiliation_string":"School of Electrical Engineering, Northeast Electric Power University, Jilin, China","institution_ids":["https://openalex.org/I179060312"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102937498","display_name":"Yuting Tian","orcid":"https://orcid.org/0000-0002-9851-0601"},"institutions":[{"id":"https://openalex.org/I1282105669","display_name":"Argonne National Laboratory","ror":"https://ror.org/05gvnxz63","country_code":"US","type":"facility","lineage":["https://openalex.org/I1282105669","https://openalex.org/I1330989302","https://openalex.org/I39565521","https://openalex.org/I40347166"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yuting Tian","raw_affiliation_strings":["Energy Systems Division, Argonne National Laboratory, Argonne, IL, USA"],"affiliations":[{"raw_affiliation_string":"Energy Systems Division, Argonne National Laboratory, Argonne, IL, USA","institution_ids":["https://openalex.org/I1282105669"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5102739579"],"corresponding_institution_ids":["https://openalex.org/I107639228"],"apc_list":null,"apc_paid":null,"fwci":1.331,"has_fulltext":false,"cited_by_count":20,"citation_normalized_percentile":{"value":0.81697296,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":"22","issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10603","display_name":"Smart Grid Energy Management","score":0.9998999834060669,"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":0.9998999834060669,"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/T14011","display_name":"Elevator Systems and Control","score":0.9871000051498413,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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":0.9807999730110168,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.8570026755332947},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8094586133956909},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.633672833442688},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5886790156364441},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.580605149269104},{"id":"https://openalex.org/keywords/oversampling","display_name":"Oversampling","score":0.4852309226989746},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4511144459247589},{"id":"https://openalex.org/keywords/aggregate","display_name":"Aggregate (composite)","score":0.44117262959480286},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.43052446842193604},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3201899230480194},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.17380237579345703}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.8570026755332947},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8094586133956909},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.633672833442688},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5886790156364441},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.580605149269104},{"id":"https://openalex.org/C197323446","wikidata":"https://www.wikidata.org/wiki/Q331222","display_name":"Oversampling","level":3,"score":0.4852309226989746},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4511144459247589},{"id":"https://openalex.org/C4679612","wikidata":"https://www.wikidata.org/wiki/Q866298","display_name":"Aggregate (composite)","level":2,"score":0.44117262959480286},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.43052446842193604},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3201899230480194},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.17380237579345703},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C2776257435","wikidata":"https://www.wikidata.org/wiki/Q1576430","display_name":"Bandwidth (computing)","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/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/ias.2019.8912379","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ias.2019.8912379","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE Industry Applications Society Annual Meeting","raw_type":"proceedings-article"},{"id":"mag:3092544980","is_oa":false,"landing_page_url":"https://jglobal.jst.go.jp/en/detail?JGLOBAL_ID=201902222373238806","pdf_url":null,"source":{"id":"https://openalex.org/S4306512817","display_name":"IEEE Conference Proceedings","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":"IEEE Conference Proceedings","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8999999761581421,"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W168840469","https://openalex.org/W1138573735","https://openalex.org/W1479651931","https://openalex.org/W1522301498","https://openalex.org/W1982401327","https://openalex.org/W2016897865","https://openalex.org/W2025423356","https://openalex.org/W2028257008","https://openalex.org/W2043279449","https://openalex.org/W2064675550","https://openalex.org/W2081314233","https://openalex.org/W2095693218","https://openalex.org/W2097949542","https://openalex.org/W2115033874","https://openalex.org/W2123910460","https://openalex.org/W2131634249","https://openalex.org/W2148143831","https://openalex.org/W2151729265","https://openalex.org/W2167866158","https://openalex.org/W2222435693","https://openalex.org/W2340432652","https://openalex.org/W2342835739","https://openalex.org/W2465358378","https://openalex.org/W2566212894","https://openalex.org/W2591811586","https://openalex.org/W2764060181","https://openalex.org/W2800788706","https://openalex.org/W2921676705","https://openalex.org/W2963030810","https://openalex.org/W2964121744","https://openalex.org/W3099873379","https://openalex.org/W3101020873","https://openalex.org/W3139672502","https://openalex.org/W6606938059","https://openalex.org/W6627345084","https://openalex.org/W6631190155","https://openalex.org/W6661243264","https://openalex.org/W6674577976","https://openalex.org/W6676993013","https://openalex.org/W6684619016","https://openalex.org/W6734162547"],"related_works":["https://openalex.org/W1574414179","https://openalex.org/W4362597605","https://openalex.org/W2766503024","https://openalex.org/W3009056573","https://openalex.org/W2922073769","https://openalex.org/W4297676672","https://openalex.org/W4389965896","https://openalex.org/W4387747402","https://openalex.org/W3119578451","https://openalex.org/W3099765033"],"abstract_inverted_index":{"Non-Intrusive":[0],"Load":[1],"Monitoring":[2],"(NILM)":[3],"is":[4,15,107,119,206],"a":[5,19,54,80,102],"technique":[6,117],"for":[7,144,147],"load":[8],"identification":[9],"and":[10,71,90,134,157,174],"energy":[11],"disaggregation.":[12],"The":[13,94],"problem":[14],"usually":[16],"formulated":[17],"as":[18,162],"single-channel":[20],"blind":[21],"source":[22],"separation.":[23],"NILM":[24,82,132],"algorithms":[25],"aim":[26],"to":[27,46,60,109,121,165,180,186,199,208],"identify":[28],"the":[29,123,131,154,159,163,166,176,188,191,210],"operating":[30],"characteristics":[31],"of":[32,98,171,190,212,215],"individual":[33,61],"appliances":[34],"from":[35,57,69],"aggregate":[36,58],"power":[37],"measurement.":[38],"Recent":[39],"advances":[40,86],"in":[41,87,126,130],"deep":[42],"learning":[43,53,92],"gave":[44],"rise":[45],"many":[47],"methods":[48,66],"that":[49,84,152],"mostly":[50],"focus":[51],"on":[52],"direct":[55],"mapping":[56],"measurement":[59],"appliance":[62,111],"power,":[63],"but":[64],"these":[65],"often":[67],"suffer":[68],"overfitting":[70],"don't":[72],"generalize":[73],"well.":[74],"In":[75],"this":[76],"paper,":[77],"we":[78],"propose":[79],"novel":[81],"method":[83,96,178],"leverages":[85],"both":[88],"supervised":[89],"unsupervised":[91],"techniques.":[93],"proposed":[95,177,192],"consists":[97],"three":[99],"stages:":[100],"a)":[101],"Bayesian":[103],"non-parametric":[104],"learning-based":[105],"approach":[106],"used":[108],"extract":[110],"states;":[112],"b)":[113],"synthetic":[114],"minority":[115],"oversampling":[116],"(SMOTE)":[118],"employed":[120,143],"mitigate":[122],"heavy":[124],"imbalance":[125],"switching":[127,160],"events":[128],"present":[129],"problem;":[133],"c)":[135],"lightweight":[136],"long":[137],"short-term":[138],"memory":[139],"(LSTM)":[140],"networks":[141,167],"are":[142,184],"status":[145],"classification":[146],"each":[148,213],"appliance.":[149],"We":[150],"argue":[151],"making":[153],"differences":[155],"before":[156],"after":[158],"event":[161],"input":[164],"can":[168],"reduce":[169],"complexity":[170],"network":[172],"training":[173],"makes":[175],"robust":[179],"multi-appliance":[181],"scenarios.":[182],"Experiments":[183],"conducted":[185,207],"demonstrate":[187,209],"effectiveness":[189,211],"method,":[193],"achieving":[194],"better":[195],"performance":[196],"when":[197],"compared":[198],"recent":[200],"methods.":[201],"Furthermore,":[202],"an":[203],"ablation":[204],"study":[205],"module":[214],"our":[216],"method.":[217]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":3}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}