{"id":"https://openalex.org/W2118697335","doi":"https://doi.org/10.1145/2487575.2488203","title":"Heat pump detection from coarse grained smart meter data with positive and unlabeled learning","display_name":"Heat pump detection from coarse grained smart meter data with positive and unlabeled learning","publication_year":2013,"publication_date":"2013-08-11","ids":{"openalex":"https://openalex.org/W2118697335","doi":"https://doi.org/10.1145/2487575.2488203","mag":"2118697335"},"language":"en","primary_location":{"id":"doi:10.1145/2487575.2488203","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2487575.2488203","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining","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/A5068755696","display_name":"Hongliang Fei","orcid":null},"institutions":[{"id":"https://openalex.org/I4210114115","display_name":"IBM Research - Thomas J. Watson Research Center","ror":"https://ror.org/0265w5591","country_code":"US","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hongliang Fei","raw_affiliation_strings":["IBM T.J. Watson Research, Yorktown Heights, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM T.J. Watson Research, Yorktown Heights, NY, USA","institution_ids":["https://openalex.org/I4210114115"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100714925","display_name":"Young-Hun Kim","orcid":"https://orcid.org/0009-0008-8707-7401"},"institutions":[{"id":"https://openalex.org/I4210114115","display_name":"IBM Research - Thomas J. Watson Research Center","ror":"https://ror.org/0265w5591","country_code":"US","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Younghun Kim","raw_affiliation_strings":["IBM T.J. Watson Research, Yorktown Heights, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM T.J. Watson Research, Yorktown Heights, NY, USA","institution_ids":["https://openalex.org/I4210114115"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103360175","display_name":"Sambit Sahu","orcid":null},"institutions":[{"id":"https://openalex.org/I4210114115","display_name":"IBM Research - Thomas J. Watson Research Center","ror":"https://ror.org/0265w5591","country_code":"US","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sambit Sahu","raw_affiliation_strings":["IBM T.J. Watson Research, Yorktown Heights, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM T.J. Watson Research, Yorktown Heights, NY, USA","institution_ids":["https://openalex.org/I4210114115"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039299246","display_name":"Milind Naphade","orcid":null},"institutions":[{"id":"https://openalex.org/I4210114115","display_name":"IBM Research - Thomas J. Watson Research Center","ror":"https://ror.org/0265w5591","country_code":"US","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Milind Naphade","raw_affiliation_strings":["IBM T.J. Watson Research, Yorktown Heights, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM T.J. Watson Research, Yorktown Heights, NY, USA","institution_ids":["https://openalex.org/I4210114115"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087324076","display_name":"Sanjay K. Mamidipalli","orcid":null},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sanjay K. Mamidipalli","raw_affiliation_strings":["IBM Global Business Service, Atlanta, GA, USA","IBM Global Business Service, Atlanta, GA, USA#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Global Business Service, Atlanta, GA, USA","institution_ids":[]},{"raw_affiliation_string":"IBM Global Business Service, Atlanta, GA, USA#TAB#","institution_ids":["https://openalex.org/I1341412227"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5087436752","display_name":"John Hutchinson","orcid":"https://orcid.org/0000-0002-5927-6058"},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"John Hutchinson","raw_affiliation_strings":["IBM Global Business Service, Piscataway, NJ, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Global Business Service, Piscataway, NJ, USA","institution_ids":["https://openalex.org/I1341412227"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.2002,"has_fulltext":false,"cited_by_count":31,"citation_normalized_percentile":{"value":0.82409891,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1330","last_page":"1338"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10603","display_name":"Smart Grid Energy Management","score":0.9993000030517578,"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":0.9993000030517578,"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/T11220","display_name":"Water Systems and Optimization","score":0.9973999857902527,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural 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/T12761","display_name":"Data Stream Mining Techniques","score":0.9900000095367432,"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/smart-meter","display_name":"Smart meter","score":0.8500596284866333},{"id":"https://openalex.org/keywords/heat-pump","display_name":"Heat pump","score":0.7093513011932373},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6801601648330688},{"id":"https://openalex.org/keywords/metering-mode","display_name":"Metering mode","score":0.6784557104110718},{"id":"https://openalex.org/keywords/granularity","display_name":"Granularity","score":0.6420461535453796},{"id":"https://openalex.org/keywords/metre","display_name":"Metre","score":0.4771687090396881},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.46300792694091797},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.4145646095275879},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.3814981281757355},{"id":"https://openalex.org/keywords/smart-grid","display_name":"Smart grid","score":0.36882877349853516},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.359508216381073},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.3478386402130127},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.27924829721450806},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.20717212557792664},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.11745092272758484},{"id":"https://openalex.org/keywords/mechanical-engineering","display_name":"Mechanical engineering","score":0.08434346318244934}],"concepts":[{"id":"https://openalex.org/C2779510800","wikidata":"https://www.wikidata.org/wiki/Q1630602","display_name":"Smart meter","level":3,"score":0.8500596284866333},{"id":"https://openalex.org/C2776461528","wikidata":"https://www.wikidata.org/wiki/Q131313","display_name":"Heat pump","level":3,"score":0.7093513011932373},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6801601648330688},{"id":"https://openalex.org/C30905978","wikidata":"https://www.wikidata.org/wiki/Q815598","display_name":"Metering mode","level":2,"score":0.6784557104110718},{"id":"https://openalex.org/C177774035","wikidata":"https://www.wikidata.org/wiki/Q1246948","display_name":"Granularity","level":2,"score":0.6420461535453796},{"id":"https://openalex.org/C151011524","wikidata":"https://www.wikidata.org/wiki/Q11573","display_name":"Metre","level":2,"score":0.4771687090396881},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.46300792694091797},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4145646095275879},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3814981281757355},{"id":"https://openalex.org/C10558101","wikidata":"https://www.wikidata.org/wiki/Q689855","display_name":"Smart grid","level":2,"score":0.36882877349853516},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.359508216381073},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.3478386402130127},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.27924829721450806},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.20717212557792664},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.11745092272758484},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.08434346318244934},{"id":"https://openalex.org/C107706546","wikidata":"https://www.wikidata.org/wiki/Q189124","display_name":"Heat exchanger","level":2,"score":0.0},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2487575.2488203","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2487575.2488203","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W25531497","https://openalex.org/W1484459975","https://openalex.org/W1542318146","https://openalex.org/W1571258547","https://openalex.org/W1672197616","https://openalex.org/W1861993554","https://openalex.org/W1952331239","https://openalex.org/W1964711536","https://openalex.org/W1967727758","https://openalex.org/W1992579300","https://openalex.org/W2001103857","https://openalex.org/W2013000896","https://openalex.org/W2016210016","https://openalex.org/W2022019933","https://openalex.org/W2038553416","https://openalex.org/W2079195815","https://openalex.org/W2081895431","https://openalex.org/W2123958887","https://openalex.org/W2124187902","https://openalex.org/W2132951075","https://openalex.org/W2133227149","https://openalex.org/W2134510195","https://openalex.org/W2148603752","https://openalex.org/W2153635508","https://openalex.org/W2166452903","https://openalex.org/W2167985623","https://openalex.org/W2183698200","https://openalex.org/W2189152632","https://openalex.org/W2254485336","https://openalex.org/W2341733312","https://openalex.org/W2518946037","https://openalex.org/W2911964244","https://openalex.org/W3120421331","https://openalex.org/W4285719527","https://openalex.org/W6684031858","https://openalex.org/W7071374342"],"related_works":["https://openalex.org/W4312869071","https://openalex.org/W2028978355","https://openalex.org/W1990698919","https://openalex.org/W3134956423","https://openalex.org/W2225080964","https://openalex.org/W2077996342","https://openalex.org/W185120663","https://openalex.org/W2561882072","https://openalex.org/W2499925746","https://openalex.org/W1136749283"],"abstract_inverted_index":{"Recent":[0],"advances":[1],"in":[2,204],"smart":[3,16,46,81,110,143],"metering":[4],"technology":[5],"enable":[6],"utility":[7,22],"companies":[8,23],"to":[9,12,26,38,102,138,180],"have":[10],"access":[11],"tremendous":[13],"amount":[14],"of":[15,125,156,195,233],"meter":[17,47,82,111,144],"data,":[18,112],"from":[19,43,142],"which":[20,88],"the":[21,123,150,164,193,209,222,244,247],"are":[24,135,160],"eager":[25],"gain":[27],"more":[28],"insight":[29],"about":[30],"their":[31],"customers.":[32,219],"In":[33,119],"this":[34],"paper,":[35],"we":[36,130,162],"aim":[37],"detect":[39,103,230],"electric":[40,211],"heat":[41,51,104,126,139,157,240],"pumps":[42,105],"coarse":[44],"grained":[45],"data":[48,115,145,171,190],"for":[49,217],"a":[50,59,64,78,99,154,167,188,205,214],"pump":[52,127,140,158,235,241],"marketing":[53],"campaign.":[54],"However,":[55],"appliance":[56],"detection":[57,226],"is":[58,89],"challenging":[60],"task,":[61],"especially":[62],"given":[63],"very":[65],"low":[66,108],"granularity":[67,80,109],"and":[68,116,146,169,173],"partial":[69],"labeled":[70],"even":[71],"unlabeled":[72,170],"data.":[73,118,148],"Traditional":[74],"methods":[75],"install":[76],"either":[77,90],"high":[79],"or":[83,93],"sensors":[84],"at":[85],"every":[86],"appliance,":[87],"too":[91],"expensive":[92],"requires":[94],"technical":[95],"expertise.":[96],"We":[97],"propose":[98],"novel":[100,132],"approach":[101],"that":[106,134,152],"utilizes":[107],"prior":[113,245],"sales":[114],"weather":[117,147],"particular,":[120],"motivated":[121],"by":[122],"characteristics":[124],"consumption":[128],"pattern,":[129],"extract":[131],"features":[133],"highly":[136],"relevant":[137],"usage":[141],"Under":[149],"constraint":[151],"only":[153],"subset":[155],"users":[159,236,242],"available,":[161],"formalize":[163],"problem":[165],"into":[166],"positive":[168],"classification":[172],"apply":[174],"biased":[175],"Support":[176],"Vector":[177],"Machine":[178],"(BSVM)":[179],"our":[181,196,199,225],"extracted":[182],"features.":[183],"Our":[184],"empirical":[185],"study":[186],"on":[187,221],"real-world":[189],"set":[191],"demonstrates":[192],"effectiveness":[194],"method.":[197],"Furthermore,":[198],"method":[200],"has":[201],"been":[202],"deployed":[203],"real-life":[206],"setting":[207],"where":[208],"partner":[210],"company":[212,248],"runs":[213],"targeted":[215],"campaign":[216],"292,496":[218],"Based":[220],"initial":[223],"feedback,":[224],"algorithm":[227,246],"can":[228],"successfully":[229],"substantial":[231],"number":[232],"non-heat":[234],"who":[237],"were":[238],"identified":[239],"with":[243],"had":[249],"used.":[250]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":2},{"year":2015,"cited_by_count":3},{"year":2014,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}