{"id":"https://openalex.org/W2589634835","doi":"https://doi.org/10.1109/icmlc.2016.7860885","title":"Classification performance using gated recurrent unit recurrent neural network on energy disaggregation","display_name":"Classification performance using gated recurrent unit recurrent neural network on energy disaggregation","publication_year":2016,"publication_date":"2016-07-01","ids":{"openalex":"https://openalex.org/W2589634835","doi":"https://doi.org/10.1109/icmlc.2016.7860885","mag":"2589634835"},"language":"en","primary_location":{"id":"doi:10.1109/icmlc.2016.7860885","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmlc.2016.7860885","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International Conference on Machine Learning and Cybernetics (ICMLC)","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/A5004516421","display_name":"Thi-Thu-Huong Le","orcid":"https://orcid.org/0000-0002-8366-9396"},"institutions":[{"id":"https://openalex.org/I4921948","display_name":"Pusan National University","ror":"https://ror.org/01an57a31","country_code":"KR","type":"education","lineage":["https://openalex.org/I4921948"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Thi-Thu-Huong Le","raw_affiliation_strings":["Department of Computer Science and Engineering, Pusan National University, Busan, Republic of Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Pusan National University, Busan, Republic of Korea","institution_ids":["https://openalex.org/I4921948"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100462790","display_name":"Jihyun Kim","orcid":"https://orcid.org/0000-0002-9997-9372"},"institutions":[{"id":"https://openalex.org/I4921948","display_name":"Pusan National University","ror":"https://ror.org/01an57a31","country_code":"KR","type":"education","lineage":["https://openalex.org/I4921948"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jihyun Kim","raw_affiliation_strings":["Department of Computer Science and Engineering, Pusan National University, Busan, Republic of Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Pusan National University, Busan, Republic of Korea","institution_ids":["https://openalex.org/I4921948"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101975138","display_name":"Howon Kim","orcid":"https://orcid.org/0000-0001-8475-7294"},"institutions":[{"id":"https://openalex.org/I4921948","display_name":"Pusan National University","ror":"https://ror.org/01an57a31","country_code":"KR","type":"education","lineage":["https://openalex.org/I4921948"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Howon Kim","raw_affiliation_strings":["Department of Computer Science and Engineering, Pusan National University, Busan, Republic of Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Pusan National University, Busan, Republic of Korea","institution_ids":["https://openalex.org/I4921948"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4921948"],"apc_list":null,"apc_paid":null,"fwci":7.2653,"has_fulltext":false,"cited_by_count":91,"citation_normalized_percentile":{"value":0.98528067,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"105","last_page":"110"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10603","display_name":"Smart Grid Energy Management","score":0.9983999729156494,"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":0.9983999729156494,"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/T11052","display_name":"Energy Load and Power Forecasting","score":0.9904000163078308,"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/T12676","display_name":"Machine Learning and ELM","score":0.9891999959945679,"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/recurrent-neural-network","display_name":"Recurrent neural network","score":0.8154857158660889},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7527284622192383},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6881487965583801},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6815216541290283},{"id":"https://openalex.org/keywords/energy-consumption","display_name":"Energy consumption","score":0.5868939757347107},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5795395970344543},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5577453374862671},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5378472208976746},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.489999920129776},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.4716681241989136},{"id":"https://openalex.org/keywords/electricity","display_name":"Electricity","score":0.461650013923645},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.42865049839019775},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.14892816543579102},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.09783852100372314},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.07316169142723083}],"concepts":[{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.8154857158660889},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7527284622192383},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6881487965583801},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6815216541290283},{"id":"https://openalex.org/C2780165032","wikidata":"https://www.wikidata.org/wiki/Q16869822","display_name":"Energy consumption","level":2,"score":0.5868939757347107},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5795395970344543},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5577453374862671},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5378472208976746},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.489999920129776},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.4716681241989136},{"id":"https://openalex.org/C206658404","wikidata":"https://www.wikidata.org/wiki/Q12725","display_name":"Electricity","level":2,"score":0.461650013923645},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.42865049839019775},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.14892816543579102},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.09783852100372314},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.07316169142723083},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","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},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icmlc.2016.7860885","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmlc.2016.7860885","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International Conference on Machine Learning and Cybernetics (ICMLC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7","score":0.9100000262260437}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W56198201","https://openalex.org/W1138573735","https://openalex.org/W1542318146","https://openalex.org/W1575617279","https://openalex.org/W1806891645","https://openalex.org/W1982401327","https://openalex.org/W1996944908","https://openalex.org/W2107878631","https://openalex.org/W2123910460","https://openalex.org/W2142086120","https://openalex.org/W2144499799","https://openalex.org/W2150355110","https://openalex.org/W2161708246","https://openalex.org/W2167985623","https://openalex.org/W2170014209","https://openalex.org/W2255051834","https://openalex.org/W2921676705","https://openalex.org/W2964199361","https://openalex.org/W3102977103","https://openalex.org/W6627345084","https://openalex.org/W6684888087"],"related_works":["https://openalex.org/W4225394202","https://openalex.org/W4298287631","https://openalex.org/W2953061907","https://openalex.org/W1847088711","https://openalex.org/W3036642985","https://openalex.org/W3032952384","https://openalex.org/W3017902212","https://openalex.org/W2964335273","https://openalex.org/W2982145560","https://openalex.org/W3008584592"],"abstract_inverted_index":{"Energy":[0],"disaggregation":[1,51,101],"or":[2],"NILM":[3],"is":[4,122],"the":[5,25,103,113,118],"best":[6],"solution":[7],"to":[8,21,45,68,84],"reduce":[9],"our":[10,70,82],"consumption":[11],"of":[12,112],"electricity.":[13],"Many":[14],"algorithms":[15,30],"in":[16],"machine":[17],"learning":[18,54,120],"are":[19,31],"applied":[20],"this":[22,38,77],"field.":[23,55,78],"However,":[24],"classification":[26],"results":[27,111],"from":[28],"those":[29],"not":[32],"as":[33,35],"well":[34],"expected.":[36],"In":[37],"paper,":[39],"we":[40,80,94,115],"propose":[41],"a":[42,47],"new":[43],"approach":[44,83,121],"construct":[46],"classifier":[48],"for":[49,99,125],"energy":[50,88,100],"with":[52,102],"deep":[53,119],"We":[56],"apply":[57],"Gated":[58],"Recurrent":[59,64],"Unit":[60],"(GRU)":[61],"based":[62],"on":[63,76,87],"Neural":[65],"Network":[66],"(RNN)":[67],"train":[69],"model":[71],"using":[72],"UK":[73],"DALE":[74],"dataset":[75],"Besides,":[79],"compare":[81],"original":[85],"RNN":[86],"disaggregation.":[89],"By":[90],"applying":[91],"GRU":[92],"RRN,":[93],"achieve":[95],"accuracy":[96],"and":[97,106],"F-measure":[98],"ranges":[104],"[89%-98%]":[105],"[81%-98%]":[107],"respectively.":[108],"Through":[109],"these":[110],"experiment,":[114],"confirm":[116],"that":[117],"really":[123],"effective":[124],"NILM.":[126]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":16},{"year":2022,"cited_by_count":14},{"year":2021,"cited_by_count":19},{"year":2020,"cited_by_count":9},{"year":2019,"cited_by_count":11},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":3}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}