{"id":"https://openalex.org/W3177177354","doi":"https://doi.org/10.1109/access.2021.3093053","title":"Short-Term Energy Forecasting Framework Using an Ensemble Deep Learning Approach","display_name":"Short-Term Energy Forecasting Framework Using an Ensemble Deep Learning Approach","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W3177177354","doi":"https://doi.org/10.1109/access.2021.3093053","mag":"3177177354"},"language":"en","primary_location":{"id":"doi:10.1109/access.2021.3093053","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3093053","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9312710/09466850.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/9312710/09466850.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5108128790","display_name":"Mustaqeem Mustaqeem","orcid":null},"institutions":[{"id":"https://openalex.org/I28777354","display_name":"Sejong University","ror":"https://ror.org/00aft1q37","country_code":"KR","type":"education","lineage":["https://openalex.org/I28777354"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Mustaqeem","raw_affiliation_strings":["National Interaction Technology Laboratory, Sejong University, Seoul, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"National Interaction Technology Laboratory, Sejong University, Seoul, Republic of Korea","institution_ids":["https://openalex.org/I28777354"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003506555","display_name":"Muhammad Ishaq","orcid":"https://orcid.org/0000-0003-1520-5642"},"institutions":[{"id":"https://openalex.org/I28777354","display_name":"Sejong University","ror":"https://ror.org/00aft1q37","country_code":"KR","type":"education","lineage":["https://openalex.org/I28777354"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Muhammad Ishaq","raw_affiliation_strings":["National Interaction Technology Laboratory, Sejong University, Seoul, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"National Interaction Technology Laboratory, Sejong University, Seoul, Republic of Korea","institution_ids":["https://openalex.org/I28777354"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101836757","display_name":"Soonil Kwon","orcid":"https://orcid.org/0000-0001-5451-8815"},"institutions":[{"id":"https://openalex.org/I28777354","display_name":"Sejong University","ror":"https://ror.org/00aft1q37","country_code":"KR","type":"education","lineage":["https://openalex.org/I28777354"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Soonil Kwon","raw_affiliation_strings":["National Interaction Technology Laboratory, Sejong University, Seoul, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"National Interaction Technology Laboratory, Sejong University, Seoul, Republic of Korea","institution_ids":["https://openalex.org/I28777354"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5108128790"],"corresponding_institution_ids":["https://openalex.org/I28777354"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":6.0954,"has_fulltext":true,"cited_by_count":79,"citation_normalized_percentile":{"value":0.97069808,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":"9","issue":null,"first_page":"94262","last_page":"94271"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9997000098228455,"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/T11052","display_name":"Energy Load and Power Forecasting","score":0.9997000098228455,"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/T10121","display_name":"Building Energy and Comfort Optimization","score":0.9984999895095825,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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/T11954","display_name":"Energy Efficiency and Management","score":0.9945999979972839,"subfield":{"id":"https://openalex.org/subfields/2105","display_name":"Renewable Energy, Sustainability and the Environment"},"field":{"id":"https://openalex.org/fields/21","display_name":"Energy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7928621768951416},{"id":"https://openalex.org/keywords/energy-consumption","display_name":"Energy consumption","score":0.5860586166381836},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5688074231147766},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5384660959243774},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5324526429176331},{"id":"https://openalex.org/keywords/ensemble-forecasting","display_name":"Ensemble forecasting","score":0.4586832821369171},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4376843273639679},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.43547362089157104},{"id":"https://openalex.org/keywords/normalization","display_name":"Normalization (sociology)","score":0.434145450592041},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.43286508321762085},{"id":"https://openalex.org/keywords/demand-forecasting","display_name":"Demand forecasting","score":0.4272494614124298},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.41491854190826416},{"id":"https://openalex.org/keywords/operations-research","display_name":"Operations research","score":0.13326916098594666},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.12951570749282837}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7928621768951416},{"id":"https://openalex.org/C2780165032","wikidata":"https://www.wikidata.org/wiki/Q16869822","display_name":"Energy consumption","level":2,"score":0.5860586166381836},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5688074231147766},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5384660959243774},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5324526429176331},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.4586832821369171},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4376843273639679},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43547362089157104},{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.434145450592041},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.43286508321762085},{"id":"https://openalex.org/C193809577","wikidata":"https://www.wikidata.org/wiki/Q3409300","display_name":"Demand forecasting","level":2,"score":0.4272494614124298},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.41491854190826416},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.13326916098594666},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.12951570749282837},{"id":"https://openalex.org/C19165224","wikidata":"https://www.wikidata.org/wiki/Q23404","display_name":"Anthropology","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2021.3093053","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3093053","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9312710/09466850.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:54311835741d447584cf6486c92e8339","is_oa":true,"landing_page_url":"https://doaj.org/article/54311835741d447584cf6486c92e8339","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 9, Pp 94262-94271 (2021)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2021.3093053","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3093053","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9312710/09466850.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","score":0.8799999952316284,"display_name":"Affordable and clean energy"}],"awards":[{"id":"https://openalex.org/G2438603286","display_name":null,"funder_award_id":"2019M3F2A1073179","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"},{"id":"https://openalex.org/G3034753964","display_name":null,"funder_award_id":"grant","funder_id":"https://openalex.org/F4320320671","funder_display_name":"National Research Foundation"},{"id":"https://openalex.org/G342704958","display_name":null,"funder_award_id":"funded","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"},{"id":"https://openalex.org/G3942910960","display_name":null,"funder_award_id":"(NRF) grant","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"},{"id":"https://openalex.org/G5984556439","display_name":null,"funder_award_id":"2019M3F2A1073179","funder_id":"https://openalex.org/F4320322030","funder_display_name":"Ministry of Science, ICT and Future Planning"},{"id":"https://openalex.org/G626505518","display_name":null,"funder_award_id":"No. 201","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"},{"id":"https://openalex.org/G6715807384","display_name":null,"funder_award_id":"2019M3F2A1073","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"}],"funders":[{"id":"https://openalex.org/F4320320671","display_name":"National Research Foundation","ror":"https://ror.org/05s0g1g46"},{"id":"https://openalex.org/F4320322030","display_name":"Ministry of Science, ICT and Future Planning","ror":"https://ror.org/032e49973"},{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"},{"id":"https://openalex.org/F4320328359","display_name":"Ministry of Science and ICT, South Korea","ror":"https://ror.org/01wpjm123"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3177177354.pdf","grobid_xml":"https://content.openalex.org/works/W3177177354.grobid-xml"},"referenced_works_count":66,"referenced_works":["https://openalex.org/W575847903","https://openalex.org/W1989866797","https://openalex.org/W2000164913","https://openalex.org/W2003955416","https://openalex.org/W2045045282","https://openalex.org/W2046933993","https://openalex.org/W2049751389","https://openalex.org/W2051607409","https://openalex.org/W2064469609","https://openalex.org/W2064675550","https://openalex.org/W2066269320","https://openalex.org/W2069143585","https://openalex.org/W2070376178","https://openalex.org/W2079735306","https://openalex.org/W2091693228","https://openalex.org/W2092317789","https://openalex.org/W2129959438","https://openalex.org/W2156636680","https://openalex.org/W2163167422","https://openalex.org/W2170756173","https://openalex.org/W2185963009","https://openalex.org/W2335322260","https://openalex.org/W2342107842","https://openalex.org/W2543643230","https://openalex.org/W2601171548","https://openalex.org/W2605864735","https://openalex.org/W2774874592","https://openalex.org/W2774966631","https://openalex.org/W2776741657","https://openalex.org/W2793517127","https://openalex.org/W2799827709","https://openalex.org/W2805797750","https://openalex.org/W2807466282","https://openalex.org/W2915736901","https://openalex.org/W2939277340","https://openalex.org/W2944851425","https://openalex.org/W2948490758","https://openalex.org/W2965525929","https://openalex.org/W2970203949","https://openalex.org/W2979947165","https://openalex.org/W2989690862","https://openalex.org/W2989779206","https://openalex.org/W2998227980","https://openalex.org/W3000951064","https://openalex.org/W3010192349","https://openalex.org/W3014649138","https://openalex.org/W3016472358","https://openalex.org/W3022013598","https://openalex.org/W3027930470","https://openalex.org/W3033955638","https://openalex.org/W3043685378","https://openalex.org/W3082558305","https://openalex.org/W3083796308","https://openalex.org/W3095648847","https://openalex.org/W3100071843","https://openalex.org/W3104996215","https://openalex.org/W3106886931","https://openalex.org/W3109961563","https://openalex.org/W3115735165","https://openalex.org/W3134450397","https://openalex.org/W3165809733","https://openalex.org/W3169085302","https://openalex.org/W3204616723","https://openalex.org/W4240044353","https://openalex.org/W4307492541","https://openalex.org/W6802797615"],"related_works":["https://openalex.org/W2965546495","https://openalex.org/W4389116644","https://openalex.org/W2153315159","https://openalex.org/W3103844505","https://openalex.org/W259157601","https://openalex.org/W4205463238","https://openalex.org/W2761785940","https://openalex.org/W2110523656","https://openalex.org/W1482209366","https://openalex.org/W1617617605"],"abstract_inverted_index":{"Industrial":[0],"and":[1,12,43,69,72,95,99,113,142,163,187,207,214,226,242],"building":[2,208,230],"sectors":[3,209],"demand":[4,49,57,98,162],"efficient":[5,13],"smart":[6],"energy":[7,18,38,56,97,161,203],"strategies,":[8],"techniques":[9],"of":[10,178,192,246,255],"optimization,":[11],"management":[14],"for":[15,62],"reducing":[16],"global":[17],"consumption":[19,100,164,204],"due":[20],"to":[21,35,46,66,93,115,126,130,158,185,201,210,237],"the":[22,109,117,121,155,160,171,176,189,193,218,239,251,256],"increasing":[23],"world":[24],"population.":[25],"Nowadays,":[26],"various":[27,244],"artificial":[28],"intelligence":[29],"(AI)":[30],"based":[31,50,181],"methods":[32,45],"are":[33],"utilized":[34,175],"perform":[36],"optimal":[37],"forecasting,":[39],"different":[40,167,234],"simulation":[41],"tools,":[42],"engineering":[44],"predict":[47,94],"future":[48],"on":[51,80,154,182],"historical":[52,81,156],"data.":[53],"Nevertheless,":[54],"nonlinear":[55],"modeling":[58],"is":[59,78,124],"still":[60],"unfledged":[61],"a":[63,166],"better":[64],"solution":[65],"handle":[67],"short-term":[68],"long-term":[70],"dependencies":[71],"avoid":[73],"static":[74],"nature":[75],"because":[76],"it":[77],"purely":[79],"data-driven.":[82],"In":[83,170],"this":[84],"paper,":[85],"we":[86,174],"propose":[87],"an":[88],"ensemble":[89,128],"deep":[90],"learning-based":[91],"approach":[92],"forecast":[96,159],"by":[101,135,221],"using":[102,136,222],"chronological":[103],"dependencies.":[104],"Our":[105],"system":[106,153,197,220],"initially":[107],"processes":[108],"data,":[110],"cleaning,":[111],"normalization,":[112],"transformation":[114],"ensure":[116,186],"model":[118,129],"performance.":[119],"Furthermore,":[120],"preprocess":[122],"data":[123,157],"fed":[125],"proposed":[127,152,172,196,219,257],"extract":[131],"hybrid":[132],"discriminative":[133],"features":[134],"convolution":[137],"neural":[138],"network":[139],"(CNN),":[140],"stacked,":[141],"bi-directional":[143],"long-short":[144],"term":[145],"memory":[146],"(LSTM)":[147],"architectures.":[148],"We":[149,216,232],"trained":[150],"our":[151],"with":[165],"time":[168],"interval.":[169],"technique,":[173],"concept":[177],"active":[179],"learning":[180],"moving":[183],"windows":[184],"improve":[188],"prediction":[190],"performance":[191],"system.":[194,258],"The":[195],"could":[198],"be":[199],"applicable":[200],"employ":[202],"in":[205],"industrial":[206],"demonstrate":[211],"their":[212],"significance":[213],"effectiveness.":[215],"evaluated":[217],"benchmark,":[223],"residential":[224],"UCI,":[225],"local":[227],"Korean":[228],"commercial":[229],"datasets.":[231],"conducted":[233],"extensive":[235],"experimentation":[236],"show":[238],"error":[240,253],"rate":[241,254],"used":[243],"kinds":[245],"evaluation":[247],"matrices,":[248],"which":[249],"indicate":[250],"lower":[252]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":15},{"year":2024,"cited_by_count":26},{"year":2023,"cited_by_count":18},{"year":2022,"cited_by_count":10},{"year":2021,"cited_by_count":6}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}
