{"id":"https://openalex.org/W4390832730","doi":"https://doi.org/10.14778/3632093.3632115","title":"ADF &amp; TransApp: A Transformer-Based Framework for Appliance Detection Using Smart Meter Consumption Series","display_name":"ADF &amp; TransApp: A Transformer-Based Framework for Appliance Detection Using Smart Meter Consumption Series","publication_year":2023,"publication_date":"2023-11-01","ids":{"openalex":"https://openalex.org/W4390832730","doi":"https://doi.org/10.14778/3632093.3632115"},"language":"en","primary_location":{"id":"doi:10.14778/3632093.3632115","is_oa":false,"landing_page_url":"https://doi.org/10.14778/3632093.3632115","pdf_url":null,"source":{"id":"https://openalex.org/S4210226185","display_name":"Proceedings of the VLDB Endowment","issn_l":"2150-8097","issn":["2150-8097"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the VLDB Endowment","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2401.05381","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5091982955","display_name":"Adrien Petralia","orcid":"https://orcid.org/0000-0003-2987-9111"},"institutions":[{"id":"https://openalex.org/I204730241","display_name":"Universit\u00e9 Paris Cit\u00e9","ror":"https://ror.org/05f82e368","country_code":"FR","type":"education","lineage":["https://openalex.org/I204730241"]},{"id":"https://openalex.org/I4210091437","display_name":"Sorbonne Paris Cit\u00e9","ror":"https://ror.org/001z21q04","country_code":"FR","type":"other","lineage":["https://openalex.org/I4210091437"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Adrien Petralia","raw_affiliation_strings":["EDF R&amp;D - Universit\u00e9 Paris Cit\u00e9"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"EDF R&amp;D - Universit\u00e9 Paris Cit\u00e9","institution_ids":["https://openalex.org/I204730241","https://openalex.org/I4210091437"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5104668483","display_name":"Philippe Charpentier","orcid":null},"institutions":[{"id":"https://openalex.org/I187846814","display_name":"EDF Energy (United Kingdom)","ror":"https://ror.org/04bs0e027","country_code":"GB","type":"company","lineage":["https://openalex.org/I187846814"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Philippe Charpentier","raw_affiliation_strings":["EDF R&amp;D"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"EDF R&amp;D","institution_ids":["https://openalex.org/I187846814"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5053726723","display_name":"Themis Palpanas","orcid":"https://orcid.org/0000-0002-8031-0265"},"institutions":[{"id":"https://openalex.org/I204730241","display_name":"Universit\u00e9 Paris Cit\u00e9","ror":"https://ror.org/05f82e368","country_code":"FR","type":"education","lineage":["https://openalex.org/I204730241"]},{"id":"https://openalex.org/I4210091437","display_name":"Sorbonne Paris Cit\u00e9","ror":"https://ror.org/001z21q04","country_code":"FR","type":"other","lineage":["https://openalex.org/I4210091437"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Themis Palpanas","raw_affiliation_strings":["Universit\u00e9 Paris Cit\u00e9 - IUF"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universit\u00e9 Paris Cit\u00e9 - IUF","institution_ids":["https://openalex.org/I204730241","https://openalex.org/I4210091437"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3326,"has_fulltext":true,"cited_by_count":4,"citation_normalized_percentile":{"value":0.51446856,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":98},"biblio":{"volume":"17","issue":"3","first_page":"553","last_page":"562"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10603","display_name":"Smart Grid Energy Management","score":0.9987000226974487,"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":0.9987000226974487,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9908000230789185,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","score":0.9799000024795532,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7208901047706604},{"id":"https://openalex.org/keywords/smart-meter","display_name":"Smart meter","score":0.644417405128479},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.5916787385940552},{"id":"https://openalex.org/keywords/electricity","display_name":"Electricity","score":0.5895266532897949},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5871961712837219},{"id":"https://openalex.org/keywords/power-consumption","display_name":"Power consumption","score":0.48791953921318054},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4308887720108032},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.42953240871429443},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.40201812982559204},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.133544921875}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7208901047706604},{"id":"https://openalex.org/C2779510800","wikidata":"https://www.wikidata.org/wiki/Q1630602","display_name":"Smart meter","level":3,"score":0.644417405128479},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.5916787385940552},{"id":"https://openalex.org/C206658404","wikidata":"https://www.wikidata.org/wiki/Q12725","display_name":"Electricity","level":2,"score":0.5895266532897949},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5871961712837219},{"id":"https://openalex.org/C2984118289","wikidata":"https://www.wikidata.org/wiki/Q29954","display_name":"Power consumption","level":3,"score":0.48791953921318054},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4308887720108032},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.42953240871429443},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40201812982559204},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.133544921875},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.14778/3632093.3632115","is_oa":false,"landing_page_url":"https://doi.org/10.14778/3632093.3632115","pdf_url":null,"source":{"id":"https://openalex.org/S4210226185","display_name":"Proceedings of the VLDB Endowment","issn_l":"2150-8097","issn":["2150-8097"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the VLDB Endowment","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:2401.05381","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2401.05381","pdf_url":"https://arxiv.org/pdf/2401.05381","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:HAL:hal-04705732v1","is_oa":true,"landing_page_url":"https://hal.science/hal-04705732","pdf_url":"https://hal.science/hal-04705732v1/file/p732-petralia.pdf","source":{"id":"https://openalex.org/S4306402512","display_name":"HAL (Le Centre pour la Communication Scientifique Directe)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1294671590","host_organization_name":"Centre National de la Recherche Scientifique","host_organization_lineage":["https://openalex.org/I1294671590"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Proceedings of the VLDB Endowment (PVLDB), 2024, 17 (3), pp.553-562. &#x27E8;10.14778/3632093.3632115&#x27E9;","raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2401.05381","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2401.05381","pdf_url":"https://arxiv.org/pdf/2401.05381","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy","score":0.8899999856948853}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4390832730.pdf","grobid_xml":"https://content.openalex.org/works/W4390832730.grobid-xml"},"referenced_works_count":49,"referenced_works":["https://openalex.org/W1836465849","https://openalex.org/W1984674851","https://openalex.org/W2028348615","https://openalex.org/W2046723397","https://openalex.org/W2052466751","https://openalex.org/W2122111042","https://openalex.org/W2139035425","https://openalex.org/W2246071452","https://openalex.org/W2465358378","https://openalex.org/W2471118808","https://openalex.org/W2615044227","https://openalex.org/W2756365660","https://openalex.org/W2792764867","https://openalex.org/W2892035503","https://openalex.org/W2896457183","https://openalex.org/W2899663614","https://openalex.org/W2949650786","https://openalex.org/W2951213053","https://openalex.org/W2972810968","https://openalex.org/W3015766084","https://openalex.org/W3083891030","https://openalex.org/W3094502228","https://openalex.org/W3107101618","https://openalex.org/W3111507638","https://openalex.org/W3141396188","https://openalex.org/W3155691593","https://openalex.org/W3171087525","https://openalex.org/W3173769060","https://openalex.org/W3212890323","https://openalex.org/W3215635737","https://openalex.org/W4223987956","https://openalex.org/W4225494949","https://openalex.org/W4226043485","https://openalex.org/W4226206033","https://openalex.org/W4282554444","https://openalex.org/W4287645965","https://openalex.org/W4290755293","https://openalex.org/W4292779060","https://openalex.org/W4294577070","https://openalex.org/W4295312788","https://openalex.org/W4297725807","https://openalex.org/W4298900392","https://openalex.org/W4300435436","https://openalex.org/W4313178187","https://openalex.org/W4313334427","https://openalex.org/W4317767705","https://openalex.org/W4319335604","https://openalex.org/W4386128207","https://openalex.org/W4394642372"],"related_works":["https://openalex.org/W2555926712","https://openalex.org/W2170300599","https://openalex.org/W2605098336","https://openalex.org/W2770437045","https://openalex.org/W3008749860","https://openalex.org/W2890405262","https://openalex.org/W2110869205","https://openalex.org/W1664361434","https://openalex.org/W2475429996","https://openalex.org/W1967909866"],"abstract_inverted_index":{"Over":[0],"the":[1,38,52,58,76,91,98,104,131,183],"past":[2],"decade,":[3],"millions":[4],"of":[5,22,37,54,94,103,124,133],"smart":[6],"meters":[7],"have":[8],"been":[9],"installed":[10],"by":[11],"electricity":[12,23],"suppliers":[13,42],"worldwide,":[14],"allowing":[15],"them":[16,66],"to":[17,46,50,72,129,152,195],"collect":[18],"a":[19,29,85,111,119,125,139,149,169],"large":[20,92,178],"amount":[21,93],"consumption":[24,105,127],"data,":[25],"albeit":[26],"sampled":[27],"at":[28],"low":[30],"frequency":[31],"(one":[32],"point":[33],"every":[34],"30min).":[35],"One":[36],"important":[39],"challenges":[40,108],"these":[41,48],"face":[43],"is":[44,145],"how":[45],"utilize":[47],"data":[49,95],"detect":[51,130],"presence/absence":[53,132],"different":[55],"appliances":[56],"in":[57,148],"customers'":[59],"households.":[60],"This":[61],"valuable":[62],"information":[63],"can":[64,81],"help":[65,73],"provide":[67],"personalized":[68],"offers":[69],"and":[70,100],"recommendations":[71],"customers":[74],"towards":[75],"energy":[77],"transition.":[78],"Appliance":[79],"detection":[80,158],"be":[82],"cast":[83],"as":[84],"time":[86,141,191],"series":[87,106,128,142,192],"classification":[88],"problem.":[89],"However,":[90],"combined":[96],"with":[97,176],"long":[99],"variable":[101],"length":[102],"pose":[107],"when":[109],"training":[110],"classifier.":[112],"In":[113],"this":[114],"paper,":[115],"we":[116],"propose":[117],"ADF,":[118],"framework":[120],"that":[121,144,182],"uses":[122],"subsequences":[123],"client":[126],"appliances.":[134],"We":[135,160],"also":[136],"introduce":[137],"TransApp,":[138],"Transformer-based":[140],"classifier":[143],"first":[146],"pretrained":[147],"self-supervised":[150],"way":[151],"enhance":[153],"its":[154],"performance":[155],"on":[156,164],"appliance":[157,196],"tasks.":[159],"test":[161],"our":[162],"approach":[163,185],"two":[165,177],"real":[166,179],"datasets,":[167],"including":[168,189],"publicly":[170],"available":[171],"one.":[172],"The":[173],"experimental":[174],"results":[175],"datasets":[180],"show":[181],"proposed":[184],"outperforms":[186],"current":[187],"solutions,":[188],"state-of-the-art":[190],"classifiers":[193],"applied":[194],"detection.":[197]},"counts_by_year":[{"year":2025,"cited_by_count":4}],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2024-01-13T00:00:00"}
