{"id":"https://openalex.org/W4200534521","doi":"https://doi.org/10.1080/01969722.2021.2008679","title":"Comparison of Deep Learning Architectures for Short-Term Electrical Load Forecasting Based on Multi-Modal Data","display_name":"Comparison of Deep Learning Architectures for Short-Term Electrical Load Forecasting Based on Multi-Modal Data","publication_year":2021,"publication_date":"2021-12-01","ids":{"openalex":"https://openalex.org/W4200534521","doi":"https://doi.org/10.1080/01969722.2021.2008679"},"language":"en","primary_location":{"id":"doi:10.1080/01969722.2021.2008679","is_oa":false,"landing_page_url":"https://doi.org/10.1080/01969722.2021.2008679","pdf_url":null,"source":{"id":"https://openalex.org/S117436046","display_name":"Cybernetics & Systems","issn_l":"0196-9722","issn":["0196-9722","1087-6553"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Cybernetics and Systems","raw_type":"journal-article"},"type":"article","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/A5067322540","display_name":"Chelabi Hiba","orcid":"https://orcid.org/0000-0001-7199-6341"},"institutions":[{"id":"https://openalex.org/I108754898","display_name":"Badji Mokhtar University","ror":"https://ror.org/03sf55932","country_code":"DZ","type":"education","lineage":["https://openalex.org/I108754898"]}],"countries":["DZ"],"is_corresponding":true,"raw_author_name":"H. Chelabi","raw_affiliation_strings":["Laboratoire de Gestion Electronique de Documents, Universit\u00e9 Badji Mokhtar \u2013 Annaba, Annaba, Algeria"],"affiliations":[{"raw_affiliation_string":"Laboratoire de Gestion Electronique de Documents, Universit\u00e9 Badji Mokhtar \u2013 Annaba, Annaba, Algeria","institution_ids":["https://openalex.org/I108754898"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111523299","display_name":"Mohamed Tarek Khadir","orcid":null},"institutions":[{"id":"https://openalex.org/I108754898","display_name":"Badji Mokhtar University","ror":"https://ror.org/03sf55932","country_code":"DZ","type":"education","lineage":["https://openalex.org/I108754898"]}],"countries":["DZ"],"is_corresponding":false,"raw_author_name":"M. T. Khadir","raw_affiliation_strings":["Laboratoire de Gestion Electronique de Documents, Universit\u00e9 Badji Mokhtar \u2013 Annaba, Annaba, Algeria"],"affiliations":[{"raw_affiliation_string":"Laboratoire de Gestion Electronique de Documents, Universit\u00e9 Badji Mokhtar \u2013 Annaba, Annaba, Algeria","institution_ids":["https://openalex.org/I108754898"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089920361","display_name":"B. Chikhaoui","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"B. Chikhaoui","raw_affiliation_strings":["LICEF Research Institute, D\u00e9partement Science et Technologie Universit\u00e9, Montr\u00e9al, Qu\u00e9bec, Canada"],"affiliations":[{"raw_affiliation_string":"LICEF Research Institute, D\u00e9partement Science et Technologie Universit\u00e9, Montr\u00e9al, Qu\u00e9bec, Canada","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5036269521","display_name":"Achraf Jabeur Telmoudi","orcid":"https://orcid.org/0000-0001-5823-4641"},"institutions":[{"id":"https://openalex.org/I4210131288","display_name":"National Engineering School of Tunis","ror":"https://ror.org/03b1zjt31","country_code":"TN","type":"education","lineage":["https://openalex.org/I4210131288","https://openalex.org/I63596082"]}],"countries":["TN"],"is_corresponding":false,"raw_author_name":"A. J. Telmoudi","raw_affiliation_strings":["LISIER Laboratory, The National Higher Engineering School of Tunis (ENSIT), University of Tunis, Tunisia"],"affiliations":[{"raw_affiliation_string":"LISIER Laboratory, The National Higher Engineering School of Tunis (ENSIT), University of Tunis, Tunisia","institution_ids":["https://openalex.org/I4210131288"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5067322540"],"corresponding_institution_ids":["https://openalex.org/I108754898"],"apc_list":null,"apc_paid":null,"fwci":0.8022,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.72547576,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":97},"biblio":{"volume":"53","issue":"1","first_page":"186","last_page":"207"},"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9973000288009644,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9951000213623047,"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/computer-science","display_name":"Computer science","score":0.7312626838684082},{"id":"https://openalex.org/keywords/modality","display_name":"Modality (human\u2013computer interaction)","score":0.6786710023880005},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6656489372253418},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6503275632858276},{"id":"https://openalex.org/keywords/modalities","display_name":"Modalities","score":0.6154822111129761},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5667832493782043},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5325108170509338},{"id":"https://openalex.org/keywords/modal","display_name":"Modal","score":0.5312924981117249},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5183014273643494},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.513489305973053},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.5047565698623657},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.4207044243812561}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7312626838684082},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.6786710023880005},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6656489372253418},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6503275632858276},{"id":"https://openalex.org/C2779903281","wikidata":"https://www.wikidata.org/wiki/Q6888026","display_name":"Modalities","level":2,"score":0.6154822111129761},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5667832493782043},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5325108170509338},{"id":"https://openalex.org/C71139939","wikidata":"https://www.wikidata.org/wiki/Q910194","display_name":"Modal","level":2,"score":0.5312924981117249},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5183014273643494},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.513489305973053},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.5047565698623657},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.4207044243812561},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C188027245","wikidata":"https://www.wikidata.org/wiki/Q750446","display_name":"Polymer chemistry","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/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/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C36289849","wikidata":"https://www.wikidata.org/wiki/Q34749","display_name":"Social science","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/01969722.2021.2008679","is_oa":false,"landing_page_url":"https://doi.org/10.1080/01969722.2021.2008679","pdf_url":null,"source":{"id":"https://openalex.org/S117436046","display_name":"Cybernetics & Systems","issn_l":"0196-9722","issn":["0196-9722","1087-6553"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Cybernetics and Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320335007","display_name":"Direction G\u00e9n\u00e9rale de la Recherche Scientifique et du D\u00e9veloppement Technologique","ror":"https://ror.org/04jpfrv51"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1498436455","https://openalex.org/W1569512666","https://openalex.org/W1985867508","https://openalex.org/W1994197834","https://openalex.org/W2092867182","https://openalex.org/W2100495367","https://openalex.org/W2122048126","https://openalex.org/W2136166259","https://openalex.org/W2147800946","https://openalex.org/W2468430020","https://openalex.org/W2474403319","https://openalex.org/W2619383789","https://openalex.org/W2761393103","https://openalex.org/W2784272322","https://openalex.org/W2788553534","https://openalex.org/W2905388713","https://openalex.org/W2917222467","https://openalex.org/W2946528716","https://openalex.org/W2964296212","https://openalex.org/W2978360381","https://openalex.org/W2979947165","https://openalex.org/W3015209634","https://openalex.org/W3016462725","https://openalex.org/W3024080193","https://openalex.org/W3046994390","https://openalex.org/W3090499788","https://openalex.org/W3092642546","https://openalex.org/W3100777112"],"related_works":["https://openalex.org/W73545470","https://openalex.org/W4224266612","https://openalex.org/W2383394264","https://openalex.org/W4320153225","https://openalex.org/W4293261942","https://openalex.org/W3125968744","https://openalex.org/W203959209","https://openalex.org/W2110287964","https://openalex.org/W2167701463","https://openalex.org/W4307407935"],"abstract_inverted_index":{"Short-term":[0],"load":[1,35,59,109,196],"forecasting":[2,36,60,110],"is":[3,11,55,111,160],"a":[4,154],"topic":[5],"of":[6,12,65,93,97,107,167,192],"considerable":[7],"interest":[8],"as":[9,40,132,153],"it":[10],"major":[13],"importance":[14],"for":[15],"specifying":[16],"and":[17,21,72,79,85,118,124,177,184],"managing":[18],"power":[19],"resources":[20],"needs.":[22],"In":[23,49],"the":[24,52,58,63,67,102,121,133,164,189],"literature,":[25],"Deep":[26,169],"Neural":[27,46,179],"Networks":[28,47],"have":[29],"been":[30],"successfully":[31],"recently":[32],"applied":[33,100],"in":[34,81,127],"using":[37,94,120],"single":[38,185],"modalities":[39,186],"an":[41],"improvement":[42],"to":[43,56,83,101,129],"traditional":[44],"Artificial":[45],"(ANN).":[48],"this":[50],"paper,":[51],"main":[53],"objective":[54],"tackle":[57],"problem":[61,106],"with":[62,182],"intention":[64],"enhancing":[66],"prediction":[68],"performance":[69],"by":[70,162],"combining":[71],"testing":[73],"different":[74,168],"multi-modal":[75],"deep":[76],"learning":[77],"approaches":[78],"architectures":[80,170],"order":[82],"process":[84],"relate":[86],"information":[87],"from":[88],"multiple":[89,183],"modalities.":[90],"The":[91,114,136,157],"benefits":[92],"multi-modality":[95],"instead":[96],"one":[98,193],"modality,":[99],"time":[103],"series":[104],"modeling":[105],"short-term":[108],"therefore":[112],"investigated.":[113],"models":[115],"are":[116],"trained":[117],"evaluated":[119],"hourly":[122],"temperature":[123],"electrical":[125],"consumption":[126],"addition":[128],"auto-regressive":[130],"variables":[131],"first":[134],"modality.":[135,156],"day":[137,194],"type":[138],"characteristic,":[139],"such":[140],"as:":[141],"weekends,":[142],"week":[143],"days,":[144],"bank":[145],"holidays,":[146],"religious":[147],"holidays":[148],"etc.,":[149],"may":[150],"be":[151],"considered":[152],"second":[155],"approach":[158],"liability":[159],"tested":[161],"comparing":[163],"empirical":[165],"results":[166],"namely:":[171],"Stacked":[172],"Denoising":[173],"Auto-":[174],"Encoders":[175],"(SDAEs)":[176],"Convolutional":[178],"Network":[180],"(CNN),":[181],"where":[187],"processing":[188],"same":[190],"task":[191],"ahead":[195],"forecasting.":[197]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}