{"id":"https://openalex.org/W3173769060","doi":"https://doi.org/10.1145/3447555.3464865","title":"Electricity Demand Activation Extraction","display_name":"Electricity Demand Activation Extraction","publication_year":2021,"publication_date":"2021-06-22","ids":{"openalex":"https://openalex.org/W3173769060","doi":"https://doi.org/10.1145/3447555.3464865","mag":"3173769060"},"language":"en","primary_location":{"id":"doi:10.1145/3447555.3464865","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3447555.3464865","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twelfth ACM International Conference on Future Energy Systems","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/A5018193006","display_name":"Pauline Laviron","orcid":null},"institutions":[{"id":"https://openalex.org/I4210143116","display_name":"\u00c9lectricit\u00e9 de France (France)","ror":"https://ror.org/03wb8xz10","country_code":"FR","type":"company","lineage":["https://openalex.org/I4210143116"]}],"countries":["FR"],"is_corresponding":true,"raw_author_name":"Pauline Laviron","raw_affiliation_strings":["EDF, Paris, France"],"affiliations":[{"raw_affiliation_string":"EDF, Paris, France","institution_ids":["https://openalex.org/I4210143116"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055405081","display_name":"Xueqi Dai","orcid":null},"institutions":[{"id":"https://openalex.org/I47720641","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53","country_code":"CN","type":"education","lineage":["https://openalex.org/I47720641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xueqi Dai","raw_affiliation_strings":["Huazhong University of Science and Technology, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"Huazhong University of Science and Technology, Wuhan, China","institution_ids":["https://openalex.org/I47720641"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041121500","display_name":"B\u00e9r\u00e9nice Huquet","orcid":null},"institutions":[{"id":"https://openalex.org/I4210143116","display_name":"\u00c9lectricit\u00e9 de France (France)","ror":"https://ror.org/03wb8xz10","country_code":"FR","type":"company","lineage":["https://openalex.org/I4210143116"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"B\u00e9r\u00e9nice Huquet","raw_affiliation_strings":["EDF, Paris, France"],"affiliations":[{"raw_affiliation_string":"EDF, Paris, France","institution_ids":["https://openalex.org/I4210143116"]}]},{"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/I121303486","display_name":"University of London Institute in Paris","ror":"https://ror.org/045svrn73","country_code":"FR","type":"education","lineage":["https://openalex.org/I121303486","https://openalex.org/I124357947"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Themis Palpanas","raw_affiliation_strings":["University of Paris, French University Institute (IUF), Paris, France"],"affiliations":[{"raw_affiliation_string":"University of Paris, French University Institute (IUF), Paris, France","institution_ids":["https://openalex.org/I121303486","https://openalex.org/I204730241"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5018193006"],"corresponding_institution_ids":["https://openalex.org/I4210143116"],"apc_list":null,"apc_paid":null,"fwci":1.0027,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.75314726,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"148","last_page":"159"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10603","display_name":"Smart Grid Energy Management","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9968000054359436,"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/T11052","display_name":"Energy Load and Power Forecasting","score":0.9908000230789185,"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/computer-science","display_name":"Computer science","score":0.8139053583145142},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.7447522282600403},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.7019479274749756},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6504297256469727},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5607777237892151},{"id":"https://openalex.org/keywords/smart-meter","display_name":"Smart meter","score":0.538148045539856},{"id":"https://openalex.org/keywords/energy-consumption","display_name":"Energy consumption","score":0.5135172009468079},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4354281425476074},{"id":"https://openalex.org/keywords/electricity","display_name":"Electricity","score":0.4323575794696808},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.4231465458869934},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.39108407497406006},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.13022524118423462},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09468898177146912}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8139053583145142},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7447522282600403},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.7019479274749756},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6504297256469727},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5607777237892151},{"id":"https://openalex.org/C2779510800","wikidata":"https://www.wikidata.org/wiki/Q1630602","display_name":"Smart meter","level":3,"score":0.538148045539856},{"id":"https://openalex.org/C2780165032","wikidata":"https://www.wikidata.org/wiki/Q16869822","display_name":"Energy consumption","level":2,"score":0.5135172009468079},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4354281425476074},{"id":"https://openalex.org/C206658404","wikidata":"https://www.wikidata.org/wiki/Q12725","display_name":"Electricity","level":2,"score":0.4323575794696808},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.4231465458869934},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39108407497406006},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.13022524118423462},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09468898177146912},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","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/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3447555.3464865","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3447555.3464865","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twelfth ACM International Conference on Future Energy Systems","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy","score":0.9200000166893005}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1479651931","https://openalex.org/W1996944908","https://openalex.org/W2038281934","https://openalex.org/W2097481773","https://openalex.org/W2123910460","https://openalex.org/W2266934531","https://openalex.org/W2594197605","https://openalex.org/W2798290452","https://openalex.org/W2798421897","https://openalex.org/W2804306644","https://openalex.org/W2890573771","https://openalex.org/W2896497283","https://openalex.org/W2902708880","https://openalex.org/W2985788863","https://openalex.org/W2994986075","https://openalex.org/W2998655947","https://openalex.org/W3013240994","https://openalex.org/W3020819267","https://openalex.org/W3039427982","https://openalex.org/W3099873379","https://openalex.org/W3143708618","https://openalex.org/W3174602507","https://openalex.org/W3196447082"],"related_works":["https://openalex.org/W3006513224","https://openalex.org/W2555926712","https://openalex.org/W2389214306","https://openalex.org/W2499612753","https://openalex.org/W3111802945","https://openalex.org/W2946096271","https://openalex.org/W2295423552","https://openalex.org/W1598471830","https://openalex.org/W3107369729","https://openalex.org/W4362737468"],"abstract_inverted_index":{"A":[0],"powerful":[1],"tool":[2],"for":[3,175,198],"reducing":[4],"energy":[5,8],"consumption":[6,31,34,75],"is":[7,19,91,141,171,185,191],"disaggregation":[9],"(also":[10],"called":[11],"NILM":[12,50],"Non-Intrusive":[13],"Load":[14],"Monitoring),":[15],"where":[16],"the":[17,22,33,49,84,154,159,213,217,240,269],"goal":[18],"to":[20,32,47,55,106,233,275],"disaggregate":[21],"smart":[23],"meter":[24],"readings":[25,76],"of":[26,35,63,88,153,164,181,201,244,272],"a":[27,60,173,193,234,261],"household's":[28,37],"total":[29],"electricity":[30,74],"that":[36,132,143,148,265],"individual":[38,78],"appliances.":[39],"State-of-the-art":[40],"machine":[41],"learning":[42],"methods":[43,101,128,238],"are":[44,66,102],"widely":[45],"used":[46],"solve":[48,107],"problem,":[51],"but":[52],"in":[53,204,248],"order":[54],"generalize":[56],"well":[57],"they":[58],"require":[59],"large":[61,205,250],"amount":[62],"data,":[64],"which":[65,184],"not":[67],"readily":[68],"available.":[69],"We":[70],"thus":[71],"need":[72],"labeled":[73],"from":[77,111,212],"appliance":[79,166,245],"activations.":[80],"Though,":[81],"manually":[82],"annotating":[83],"start":[85,160],"and":[86,94,119,156,161,226,242],"end":[87,162],"single-appliance":[89],"activations":[90,214],"extremely":[92],"laborious":[93],"time":[95,134,251],"consuming.":[96],"Therefore,":[97],"automated":[98],"activation":[99,246],"extraction":[100,247],"needed.":[103],"Earlier":[104],"approaches":[105],"this":[108,122],"problem":[109],"suffer":[110],"limitations,":[112],"such":[113],"as":[114],"incomplete":[115],"signatures,":[116,118],"double":[117],"outliers.":[120],"In":[121],"work,":[123],"we":[124,258],"introduce":[125],"three":[126],"scalable":[127],"based":[129,196],"on":[130,145,150],"techniques":[131],"use":[133],"series":[135,252],"similarity":[136,194],"search.":[137],"The":[138,168,188],"first":[139],"method":[140,170,174,190,197],"Cartesio":[142],"(improves":[144],"earlier":[146],"work":[147],"relies":[149],"known":[151,202],"features":[152],"appliance)":[155],"separately":[157],"detects":[158],"times":[163],"an":[165],"activation.":[167],"second":[169],"ValmA,":[172],"identifying":[176],"previously":[177],"unknown":[178],"candidate":[179],"signatures":[180,203,208],"variable":[182],"length,":[183],"essentially":[186],"parameter-free.":[187],"third":[189],"SimBA,":[192],"search":[195],"efficient":[199],"detection":[200],"datasets.":[206],"These":[207],"can":[209],"be":[210],"computed":[211],"extracted":[215],"using":[216],"previous":[218],"methods.":[219],"Our":[220],"experimental":[221],"results":[222],"with":[223],"real":[224],"6":[225],"10":[227],"seconds-sampling":[228],"data":[229],"demonstrate":[230],"that,":[231],"compared":[232],"state-of-the-art":[235],"solution,":[236],"our":[237],"improve":[239],"accuracy":[241,263],"robustness":[243],"very":[249],"collections.":[253],"To":[254],"compare":[255],"these":[256],"methods,":[257],"also":[259],"describe":[260],"new":[262],"measure":[264],"takes":[266],"into":[267],"account":[268],"special":[270],"characteristics":[271],"subsequences,":[273],"leading":[274],"more":[276],"precise":[277],"performance":[278],"evaluation":[279],"results.":[280]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
