{"id":"https://openalex.org/W2004618634","doi":"https://doi.org/10.1109/smc.2014.6973942","title":"Smart meter data analytics: Prediction of enrollment in residential energy efficiency programs","display_name":"Smart meter data analytics: Prediction of enrollment in residential energy efficiency programs","publication_year":2014,"publication_date":"2014-10-01","ids":{"openalex":"https://openalex.org/W2004618634","doi":"https://doi.org/10.1109/smc.2014.6973942","mag":"2004618634"},"language":"en","primary_location":{"id":"doi:10.1109/smc.2014.6973942","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc.2014.6973942","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5082457710","display_name":"Michael Zeifman","orcid":"https://orcid.org/0000-0003-2018-257X"},"institutions":[{"id":"https://openalex.org/I4210131592","display_name":"Fraunhofer USA Center for Sustainable Energy Systems","ror":"https://ror.org/03c3jbs89","country_code":"US","type":"facility","lineage":["https://openalex.org/I4210131592","https://openalex.org/I4210161623","https://openalex.org/I4923324"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Michael Zeifman","raw_affiliation_strings":["Building Energy Technologies, Fraunhofer Center for Sustainable Energy Systems, Boston, MA, United States of America"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Building Energy Technologies, Fraunhofer Center for Sustainable Energy Systems, Boston, MA, United States of America","institution_ids":["https://openalex.org/I4210131592"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5082457710"],"corresponding_institution_ids":["https://openalex.org/I4210131592"],"apc_list":null,"apc_paid":null,"fwci":2.4474,"has_fulltext":false,"cited_by_count":16,"citation_normalized_percentile":{"value":0.88518769,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"413","last_page":"416"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10603","display_name":"Smart Grid Energy Management","score":0.9994999766349792,"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":0.9994999766349792,"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/T11336","display_name":"Energy and Environment Impacts","score":0.989799976348877,"subfield":{"id":"https://openalex.org/subfields/2310","display_name":"Pollution"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10632","display_name":"Housing Market and Economics","score":0.9751999974250793,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/smart-meter","display_name":"Smart meter","score":0.779120683670044},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.7057345509529114},{"id":"https://openalex.org/keywords/electricity","display_name":"Electricity","score":0.5880851745605469},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"Logistic regression","score":0.5748308897018433},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5495594143867493},{"id":"https://openalex.org/keywords/household-income","display_name":"Household income","score":0.4788475036621094},{"id":"https://openalex.org/keywords/predictive-analytics","display_name":"Predictive analytics","score":0.45920780301094055},{"id":"https://openalex.org/keywords/energy-consumption","display_name":"Energy consumption","score":0.43202337622642517},{"id":"https://openalex.org/keywords/propensity-score-matching","display_name":"Propensity score matching","score":0.421988844871521},{"id":"https://openalex.org/keywords/environmental-economics","display_name":"Environmental economics","score":0.34197407960891724},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2508848309516907},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.23798558115959167},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.23597130179405212},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.139937162399292},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.08878061175346375}],"concepts":[{"id":"https://openalex.org/C2779510800","wikidata":"https://www.wikidata.org/wiki/Q1630602","display_name":"Smart meter","level":3,"score":0.779120683670044},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.7057345509529114},{"id":"https://openalex.org/C206658404","wikidata":"https://www.wikidata.org/wiki/Q12725","display_name":"Electricity","level":2,"score":0.5880851745605469},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.5748308897018433},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5495594143867493},{"id":"https://openalex.org/C2780892066","wikidata":"https://www.wikidata.org/wiki/Q1591167","display_name":"Household income","level":2,"score":0.4788475036621094},{"id":"https://openalex.org/C83209312","wikidata":"https://www.wikidata.org/wiki/Q1053367","display_name":"Predictive analytics","level":2,"score":0.45920780301094055},{"id":"https://openalex.org/C2780165032","wikidata":"https://www.wikidata.org/wiki/Q16869822","display_name":"Energy consumption","level":2,"score":0.43202337622642517},{"id":"https://openalex.org/C17923572","wikidata":"https://www.wikidata.org/wiki/Q7250160","display_name":"Propensity score matching","level":2,"score":0.421988844871521},{"id":"https://openalex.org/C134560507","wikidata":"https://www.wikidata.org/wiki/Q753291","display_name":"Environmental economics","level":1,"score":0.34197407960891724},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2508848309516907},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.23798558115959167},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.23597130179405212},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.139937162399292},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.08878061175346375},{"id":"https://openalex.org/C147176958","wikidata":"https://www.wikidata.org/wiki/Q77590","display_name":"Civil engineering","level":1,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/smc.2014.6973942","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc.2014.6973942","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"},{"id":"pmh:oai:publica.fraunhofer.de:publica/391972","is_oa":false,"landing_page_url":"https://publica.fraunhofer.de/handle/publica/391972","pdf_url":null,"source":{"id":"https://openalex.org/S4306400318","display_name":"Fraunhofer-Publica (Fraunhofer-Gesellschaft)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4923324","host_organization_name":"Fraunhofer-Gesellschaft","host_organization_lineage":["https://openalex.org/I4923324"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"conference paper"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy","score":0.8799999952316284}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":4,"referenced_works":["https://openalex.org/W1970954003","https://openalex.org/W2043279449","https://openalex.org/W2159065056","https://openalex.org/W2159805452"],"related_works":["https://openalex.org/W2026576563","https://openalex.org/W3196761963","https://openalex.org/W2036193982","https://openalex.org/W213628847","https://openalex.org/W4232168831","https://openalex.org/W4253956144","https://openalex.org/W3023923059","https://openalex.org/W2065417422","https://openalex.org/W2275925001","https://openalex.org/W2809544166"],"abstract_inverted_index":{"Massive":[0],"rollout":[1],"of":[2,22,31,78,102,131,163,168,175],"residential":[3,181],"smart":[4,23,144],"meters":[5],"has":[6,41],"spurred":[7],"interest":[8],"in":[9,54,172],"processing":[10],"the":[11,20,94,99,112],"highly":[12],"granular":[13],"data":[14,25,47,115,141],"available":[15,119],"from":[16,142],"these":[17,46,90],"devices.":[18],"Whereas":[19],"majority":[21],"meter":[24],"analytics":[26],"is":[27,105],"devoted":[28],"to":[29,44,48,52,96,120,134,157],"characterization":[30],"household":[32,50,65,70,74,114,132],"electric":[33],"appliances":[34],"and":[35,57,83,111],"their":[36],"operational":[37],"schedules,":[38],"little":[39],"work":[40],"been":[42],"done":[43],"leverage":[45],"predict":[49],"propensity":[51,133],"enroll":[53,135],"energy":[55,81],"efficiency":[56],"demand":[58],"response":[59],"programs.":[60],"The":[61,150],"state-of-the-art":[62],"methodology":[63],"for":[64,129],"enrollment":[66],"prediction":[67,100,130,161],"involves":[68],"measurable":[69],"characteristics":[71],"(e.g.,":[72],"age,":[73],"income,":[75],"education,":[76],"presence":[77],"children,":[79],"average":[80],"bill)":[82],"a":[84,126,176],"multivariate":[85],"logistic":[86],"regression":[87],"that":[88],"connects":[89],"predictor":[91],"variables":[92],"with":[93],"probability":[95],"enroll.":[97],"Unfortunately,":[98],"accuracy":[101,162,169],"this":[103],"method":[104,128,151],"just":[106],"slightly":[107],"better":[108],"than":[109],"50%,":[110],"required":[113],"are":[116],"not":[117],"freely":[118],"utilities/":[121],"program":[122],"contractors.":[123],"We":[124],"developed":[125],"new":[127],"using":[136],"only":[137],"hourly":[138],"electricity":[139],"consumption":[140],"households'":[143],"meters,":[145],"collected":[146],"over":[147],"twelve":[148],"months.":[149],"implements":[152],"advanced":[153],"machine":[154],"learning":[155],"algorithms":[156],"reach":[158],"an":[159],"unprecedented":[160],"about":[164],"90%.":[165],"This":[166],"level":[167],"was":[170],"obtained":[171],"our":[173],"study":[174],"US":[177],"West":[178],"Coast":[179],"behavior-based":[180],"program.":[182]},"counts_by_year":[{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2018,"cited_by_count":3},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":2},{"year":2015,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}