{"id":"https://openalex.org/W3207677724","doi":"https://doi.org/10.3390/sym13101942","title":"Hybrid Ensemble Deep Learning-Based Approach for Time Series Energy Prediction","display_name":"Hybrid Ensemble Deep Learning-Based Approach for Time Series Energy Prediction","publication_year":2021,"publication_date":"2021-10-15","ids":{"openalex":"https://openalex.org/W3207677724","doi":"https://doi.org/10.3390/sym13101942","mag":"3207677724"},"language":"en","primary_location":{"id":"doi:10.3390/sym13101942","is_oa":true,"landing_page_url":"https://doi.org/10.3390/sym13101942","pdf_url":"https://www.mdpi.com/2073-8994/13/10/1942/pdf?version=1634794590","source":{"id":"https://openalex.org/S190787756","display_name":"Symmetry","issn_l":"2073-8994","issn":["2073-8994"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Symmetry","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2073-8994/13/10/1942/pdf?version=1634794590","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5073642703","display_name":"Pyae Pyae Phyo","orcid":null},"institutions":[{"id":"https://openalex.org/I83202590","display_name":"Jeju National University","ror":"https://ror.org/05hnb4n85","country_code":"KR","type":"education","lineage":["https://openalex.org/I83202590"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Pyae Pyae Phyo","raw_affiliation_strings":["Department of Computer Engineering, Jeju National University, Jeju-si 63243, Korea"],"raw_orcid":"https://orcid.org/0000-0001-7864-2044","affiliations":[{"raw_affiliation_string":"Department of Computer Engineering, Jeju National University, Jeju-si 63243, Korea","institution_ids":["https://openalex.org/I83202590"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5110809957","display_name":"Yung-Cheol Byun","orcid":"https://orcid.org/0000-0003-1107-9941"},"institutions":[{"id":"https://openalex.org/I83202590","display_name":"Jeju National University","ror":"https://ror.org/05hnb4n85","country_code":"KR","type":"education","lineage":["https://openalex.org/I83202590"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Yung-Cheol Byun","raw_affiliation_strings":["Department of Computer Engineering, Jeju National University, Jeju-si 63243, Korea"],"raw_orcid":"https://orcid.org/0000-0003-1107-9941","affiliations":[{"raw_affiliation_string":"Department of Computer Engineering, Jeju National University, Jeju-si 63243, Korea","institution_ids":["https://openalex.org/I83202590"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5110809957"],"corresponding_institution_ids":["https://openalex.org/I83202590"],"apc_list":{"value":2000,"currency":"CHF","value_usd":2227},"apc_paid":{"value":2000,"currency":"CHF","value_usd":2227},"fwci":2.1682,"has_fulltext":false,"cited_by_count":39,"citation_normalized_percentile":{"value":0.87866155,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"13","issue":"10","first_page":"1942","last_page":"1942"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9998000264167786,"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/T11052","display_name":"Energy Load and Power Forecasting","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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9846000075340271,"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.982699990272522,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/mean-absolute-percentage-error","display_name":"Mean absolute percentage error","score":0.8490109443664551},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7492321729660034},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.7177439332008362},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5929425954818726},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5723350644111633},{"id":"https://openalex.org/keywords/multilayer-perceptron","display_name":"Multilayer perceptron","score":0.5655515789985657},{"id":"https://openalex.org/keywords/ensemble-forecasting","display_name":"Ensemble forecasting","score":0.5429280996322632},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.5176597237586975},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5086439847946167},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5083300471305847},{"id":"https://openalex.org/keywords/perceptron","display_name":"Perceptron","score":0.4832333028316498},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.48250889778137207},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.46588438749313354},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.4280237555503845},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4225337505340576},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.20847776532173157},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09546124935150146}],"concepts":[{"id":"https://openalex.org/C150217764","wikidata":"https://www.wikidata.org/wiki/Q6803607","display_name":"Mean absolute percentage error","level":3,"score":0.8490109443664551},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7492321729660034},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.7177439332008362},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5929425954818726},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5723350644111633},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.5655515789985657},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.5429280996322632},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.5176597237586975},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5086439847946167},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5083300471305847},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.4832333028316498},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.48250889778137207},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.46588438749313354},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.4280237555503845},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4225337505340576},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.20847776532173157},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09546124935150146},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/sym13101942","is_oa":true,"landing_page_url":"https://doi.org/10.3390/sym13101942","pdf_url":"https://www.mdpi.com/2073-8994/13/10/1942/pdf?version=1634794590","source":{"id":"https://openalex.org/S190787756","display_name":"Symmetry","issn_l":"2073-8994","issn":["2073-8994"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Symmetry","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:efc996b5b76d42c38a5873c97dc2e15d","is_oa":true,"landing_page_url":"https://doaj.org/article/efc996b5b76d42c38a5873c97dc2e15d","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Symmetry, Vol 13, Iss 10, p 1942 (2021)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2073-8994/13/10/1942/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/sym13101942","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Symmetry","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/sym13101942","is_oa":true,"landing_page_url":"https://doi.org/10.3390/sym13101942","pdf_url":"https://www.mdpi.com/2073-8994/13/10/1942/pdf?version=1634794590","source":{"id":"https://openalex.org/S190787756","display_name":"Symmetry","issn_l":"2073-8994","issn":["2073-8994"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Symmetry","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7","score":0.8899999856948853}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W3207677724.pdf"},"referenced_works_count":37,"referenced_works":["https://openalex.org/W1995341919","https://openalex.org/W2026430219","https://openalex.org/W2059698575","https://openalex.org/W2064675550","https://openalex.org/W2076063813","https://openalex.org/W2094054185","https://openalex.org/W2154579312","https://openalex.org/W2573526403","https://openalex.org/W2742473260","https://openalex.org/W2743680082","https://openalex.org/W2754252319","https://openalex.org/W2766843231","https://openalex.org/W2791252587","https://openalex.org/W2809317444","https://openalex.org/W2906033034","https://openalex.org/W2907724083","https://openalex.org/W2915043045","https://openalex.org/W2919115771","https://openalex.org/W2944681516","https://openalex.org/W2960576676","https://openalex.org/W2999605850","https://openalex.org/W3022643593","https://openalex.org/W3024619383","https://openalex.org/W3028294430","https://openalex.org/W3032423120","https://openalex.org/W3040784485","https://openalex.org/W3087412073","https://openalex.org/W3090661556","https://openalex.org/W3095469347","https://openalex.org/W3132782787","https://openalex.org/W3158610431","https://openalex.org/W3187628238","https://openalex.org/W4225674127","https://openalex.org/W6682751323","https://openalex.org/W6772616639","https://openalex.org/W6780829424","https://openalex.org/W6783760793"],"related_works":["https://openalex.org/W2099182244","https://openalex.org/W1580150424","https://openalex.org/W2891633941","https://openalex.org/W2325482571","https://openalex.org/W2076543106","https://openalex.org/W3175454233","https://openalex.org/W3202800081","https://openalex.org/W4241032203","https://openalex.org/W2127236386","https://openalex.org/W2523437662"],"abstract_inverted_index":{"The":[0,133],"energy":[1,11,15,22,87,199],"manufacturers":[2],"are":[3,68,89,116,126,141,161],"required":[4],"to":[5,60,120,192,208],"produce":[6],"an":[7,25],"accurate":[8],"amount":[9],"of":[10,136,205],"by":[12,143],"meeting":[13],"the":[14,18,29,38,62,122,137,209],"requirements":[16],"at":[17,187],"end-user":[19],"side.":[20],"Consequently,":[21],"prediction":[23],"becomes":[24],"essential":[26],"role":[27],"in":[28,129],"electric":[30],"industrial":[31],"zone.":[32],"In":[33],"this":[34],"paper,":[35],"we":[36],"propose":[37],"hybrid":[39,58],"ensemble":[40,139,175],"deep":[41],"learning":[42,76],"model,":[43],"which":[44,125,203],"combines":[45],"multilayer":[46],"perceptron":[47],"(MLP),":[48],"convolutional":[49],"neural":[50],"network":[51],"(CNN),":[52],"long":[53],"short-term":[54],"memory":[55],"(LSTM),":[56],"and":[57,71,96,112,118,153,172,189,200],"CNN-LSTM":[59],"improve":[61],"forecasting":[63,134,182,197],"performance.":[64],"These":[65],"DL":[66,131,164],"architectures":[67],"more":[69],"popular":[70],"better":[72,178],"than":[73,180],"other":[74,181],"machine":[75],"(ML)":[77],"models":[78,166],"for":[79,98,196],"time":[80],"series":[81],"electrical":[82],"load":[83],"prediction.":[84],"Therefore,":[85],"hourly-based":[86],"data":[88,115],"collected":[90],"from":[91],"Jeju":[92],"Island,":[93],"South":[94],"Korea,":[95],"applied":[97],"forecasting.":[99],"We":[100],"considered":[101],"external":[102],"features":[103],"associated":[104],"with":[105,163],"meteorological":[106],"conditions":[107],"affecting":[108],"energy.":[109],"Two-year":[110],"training":[111],"one-year":[113],"testing":[114],"preprocessed":[117],"arranged":[119],"reform":[121],"times":[123],"series,":[124],"then":[127],"trained":[128],"each":[130],"model.":[132],"results":[135],"proposed":[138],"model":[140,176],"evaluated":[142],"using":[144],"mean":[145,149,154],"square":[146],"error":[147,151,157],"(MSE),":[148],"absolute":[150,155],"(MAE),":[152],"percentage":[156],"(MAPE).":[158],"Error":[159],"metrics":[160],"compared":[162],"stand-alone":[165],"such":[167],"as":[168],"MLP,":[169],"CNN,":[170],"LSTM,":[171],"CNN-LSTM.":[173],"Our":[174],"provides":[177],"performance":[179],"models,":[183],"providing":[184],"minimum":[185],"MAPE":[186],"0.75%,":[188],"was":[190],"proven":[191],"be":[193],"inherently":[194],"symmetric":[195],"time-series":[198],"demand":[201],"data,":[202],"is":[204],"utmost":[206],"concern":[207],"power":[210],"system":[211],"sector.":[212]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":10},{"year":2022,"cited_by_count":9}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}
