{"id":"https://openalex.org/W4383681686","doi":"https://doi.org/10.1007/s00521-023-08777-6","title":"A multivariate ensemble learning method for medium-term energy forecasting","display_name":"A multivariate ensemble learning method for medium-term energy forecasting","publication_year":2023,"publication_date":"2023-07-10","ids":{"openalex":"https://openalex.org/W4383681686","doi":"https://doi.org/10.1007/s00521-023-08777-6"},"language":"en","primary_location":{"id":"doi:10.1007/s00521-023-08777-6","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s00521-023-08777-6","pdf_url":"https://link.springer.com/content/pdf/10.1007/s00521-023-08777-6.pdf","source":{"id":"https://openalex.org/S147897268","display_name":"Neural Computing and Applications","issn_l":"0941-0643","issn":["0941-0643","1433-3058"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neural Computing and Applications","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s00521-023-08777-6.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5045145081","display_name":"Charalampos M. Liapis","orcid":"https://orcid.org/0000-0002-4717-031X"},"institutions":[{"id":"https://openalex.org/I174878644","display_name":"University of Patras","ror":"https://ror.org/017wvtq80","country_code":"GR","type":"education","lineage":["https://openalex.org/I174878644"]}],"countries":["GR"],"is_corresponding":true,"raw_author_name":"Charalampos M. Liapis","raw_affiliation_strings":["Department of Mathematics, University of Patras, 26504, Rio, Patras, Greece"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mathematics, University of Patras, 26504, Rio, Patras, Greece","institution_ids":["https://openalex.org/I174878644"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088798284","display_name":"Aikaterini Karanikola","orcid":"https://orcid.org/0009-0006-4226-6597"},"institutions":[{"id":"https://openalex.org/I174878644","display_name":"University of Patras","ror":"https://ror.org/017wvtq80","country_code":"GR","type":"education","lineage":["https://openalex.org/I174878644"]}],"countries":["GR"],"is_corresponding":true,"raw_author_name":"Aikaterini Karanikola","raw_affiliation_strings":["Department of Mathematics, University of Patras, 26504, Rio, Patras, Greece"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mathematics, University of Patras, 26504, Rio, Patras, Greece","institution_ids":["https://openalex.org/I174878644"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5066370772","display_name":"Sotiris Kotsiantis","orcid":"https://orcid.org/0000-0002-2247-3082"},"institutions":[{"id":"https://openalex.org/I174878644","display_name":"University of Patras","ror":"https://ror.org/017wvtq80","country_code":"GR","type":"education","lineage":["https://openalex.org/I174878644"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"Sotiris Kotsiantis","raw_affiliation_strings":["Department of Mathematics, University of Patras, 26504, Rio, Patras, Greece"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mathematics, University of Patras, 26504, Rio, Patras, Greece","institution_ids":["https://openalex.org/I174878644"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5045145081","https://openalex.org/A5088798284"],"corresponding_institution_ids":["https://openalex.org/I174878644"],"apc_list":{"value":2390,"currency":"EUR","value_usd":2990},"apc_paid":{"value":2390,"currency":"EUR","value_usd":2990},"fwci":1.4691,"has_fulltext":true,"cited_by_count":12,"citation_normalized_percentile":{"value":0.82021354,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"35","issue":"29","first_page":"21479","last_page":"21497"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9998999834060669,"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":0.9998999834060669,"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/T11918","display_name":"Forecasting Techniques and Applications","score":0.9914000034332275,"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"}},{"id":"https://openalex.org/T12368","display_name":"Grey System Theory Applications","score":0.9908000230789185,"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/computer-science","display_name":"Computer science","score":0.7583068609237671},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.6065560579299927},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.6044267416000366},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.549380898475647},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.5299341082572937},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5294793844223022},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5118662714958191},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4939468502998352},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.4486527442932129},{"id":"https://openalex.org/keywords/ensemble-forecasting","display_name":"Ensemble forecasting","score":0.4333811402320862},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.43110769987106323},{"id":"https://openalex.org/keywords/production","display_name":"Production (economics)","score":0.42420512437820435},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4156377911567688},{"id":"https://openalex.org/keywords/energy-consumption","display_name":"Energy consumption","score":0.41436347365379333},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3946118950843811},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1791207492351532},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1395983099937439}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7583068609237671},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.6065560579299927},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.6044267416000366},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.549380898475647},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.5299341082572937},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5294793844223022},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5118662714958191},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4939468502998352},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.4486527442932129},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.4333811402320862},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.43110769987106323},{"id":"https://openalex.org/C2778348673","wikidata":"https://www.wikidata.org/wiki/Q739302","display_name":"Production (economics)","level":2,"score":0.42420512437820435},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4156377911567688},{"id":"https://openalex.org/C2780165032","wikidata":"https://www.wikidata.org/wiki/Q16869822","display_name":"Energy consumption","level":2,"score":0.41436347365379333},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3946118950843811},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1791207492351532},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1395983099937439},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","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},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","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/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C139719470","wikidata":"https://www.wikidata.org/wiki/Q39680","display_name":"Macroeconomics","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1007/s00521-023-08777-6","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s00521-023-08777-6","pdf_url":"https://link.springer.com/content/pdf/10.1007/s00521-023-08777-6.pdf","source":{"id":"https://openalex.org/S147897268","display_name":"Neural Computing and Applications","issn_l":"0941-0643","issn":["0941-0643","1433-3058"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neural Computing and Applications","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1007/s00521-023-08777-6","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s00521-023-08777-6","pdf_url":"https://link.springer.com/content/pdf/10.1007/s00521-023-08777-6.pdf","source":{"id":"https://openalex.org/S147897268","display_name":"Neural Computing and Applications","issn_l":"0941-0643","issn":["0941-0643","1433-3058"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neural Computing and Applications","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.8999999761581421,"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320310968","display_name":"University of Patras","ror":"https://ror.org/017wvtq80"}],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4383681686.pdf"},"referenced_works_count":56,"referenced_works":["https://openalex.org/W176909285","https://openalex.org/W221263235","https://openalex.org/W1594031697","https://openalex.org/W1678356000","https://openalex.org/W1771450670","https://openalex.org/W1964357740","https://openalex.org/W2024548552","https://openalex.org/W2053834050","https://openalex.org/W2056132907","https://openalex.org/W2061554433","https://openalex.org/W2063978378","https://openalex.org/W2091085232","https://openalex.org/W2092260586","https://openalex.org/W2100090926","https://openalex.org/W2122825543","https://openalex.org/W2135046866","https://openalex.org/W2139073438","https://openalex.org/W2177019876","https://openalex.org/W2275088575","https://openalex.org/W2295598076","https://openalex.org/W2543643230","https://openalex.org/W2742473260","https://openalex.org/W2754252319","https://openalex.org/W2774966631","https://openalex.org/W2782902016","https://openalex.org/W2788553534","https://openalex.org/W2790204532","https://openalex.org/W2809317444","https://openalex.org/W2902285311","https://openalex.org/W2911293880","https://openalex.org/W2911495555","https://openalex.org/W2911546748","https://openalex.org/W2911964244","https://openalex.org/W2914859268","https://openalex.org/W2921144914","https://openalex.org/W2941944264","https://openalex.org/W2950986934","https://openalex.org/W2956207189","https://openalex.org/W2971270198","https://openalex.org/W2972961631","https://openalex.org/W2981785157","https://openalex.org/W2996374824","https://openalex.org/W3008314711","https://openalex.org/W3008533347","https://openalex.org/W3011699874","https://openalex.org/W3028678424","https://openalex.org/W3043519653","https://openalex.org/W3043685378","https://openalex.org/W3049185583","https://openalex.org/W3104996215","https://openalex.org/W3209695977","https://openalex.org/W4220662822","https://openalex.org/W4241727697","https://openalex.org/W4285203659","https://openalex.org/W4289754032","https://openalex.org/W6792885866"],"related_works":["https://openalex.org/W2794896638","https://openalex.org/W1807784185","https://openalex.org/W4390905871","https://openalex.org/W3202800081","https://openalex.org/W1909207154","https://openalex.org/W3124390867","https://openalex.org/W3101614107","https://openalex.org/W3204228978","https://openalex.org/W1514365828","https://openalex.org/W4390971112"],"abstract_inverted_index":{"Abstract":[0],"In":[1,145],"the":[2,22,38,58,67,96,119,152,158,174,180],"contemporary":[3],"context,":[4],"both":[5],"production":[6,78],"and":[7,49,91,118,141],"consumption":[8],"of":[9,17,25,60,66,95,137,176],"energy,":[10],"being":[11,30],"concepts":[12],"intertwined":[13],"through":[14],"a":[15,31,138],"condition":[16],"synchronicity,":[18],"are":[19],"pivotal":[20],"for":[21,45,63],"orderly":[23],"functioning":[24],"society,":[26],"with":[27,157],"their":[28],"management":[29],"building":[32],"block":[33],"in":[34],"maintaining":[35],"regularity.":[36],"Hence,":[37],"pursuit":[39,155],"to":[40,116,121,126,162],"develop":[41],"reliable":[42],"computational":[43],"tools":[44],"modeling":[46],"such":[47],"serial":[48],"time-dependent":[50],"phenomena":[51],"becomes":[52],"similarly":[53],"crucial.":[54],"This":[55],"paper":[56],"investigates":[57],"use":[59,175],"ensemble":[61,143,148],"learners":[62],"medium-term":[64],"forecasting":[65],"Greek":[68],"energy":[69,77],"system":[70],"load":[71],"using":[72],"additional":[73],"information":[74],"from":[75,79],"injected":[76],"various":[80],"sources.":[81],"Through":[82],"an":[83,132,147,163],"extensive":[84],"experimental":[85],"process,":[86],"over":[87,101],"435":[88],"regression":[89],"schemes":[90],"64":[92],"different":[93,103],"modifications":[94],"feature":[97,122],"inputs":[98],"were":[99],"tested":[100],"five":[102],"prediction":[104],"time":[105],"frames,":[106],"creating":[107],"comparative":[108],"rankings":[109],"regarding":[110],"two":[111],"case":[112],"studies:":[113],"one":[114],"related":[115],"methods":[117],"other":[120],"setups.":[123],"Evaluations":[124],"according":[125,161],"six":[127],"widely":[128],"used":[129],"metrics":[130],"indicate":[131],"aggregate":[133],"but":[134],"clear":[135],"dominance":[136],"specific":[139],"efficient":[140],"low-cost":[142],"layout.":[144],"particular,":[146],"method":[149],"that":[150,173],"incorporates":[151],"orthogonal":[153],"matching":[154],"together":[156],"Huber":[159],"regressor":[160],"averaged":[164],"combinatorial":[165],"scheme":[166],"is":[167,171],"proposed.":[168],"Moreover,":[169],"it":[170],"shown":[172],"multivariate":[177],"setups":[178],"improves":[179],"derived":[181],"predictions.":[182]},"counts_by_year":[{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":1}],"updated_date":"2026-06-13T06:13:01.061226","created_date":"2025-10-10T00:00:00"}
