{"id":"https://openalex.org/W4411994565","doi":"https://doi.org/10.1007/s11227-025-07564-5","title":"Short-term electricity consumption forecasting with deep learning","display_name":"Short-term electricity consumption forecasting with deep learning","publication_year":2025,"publication_date":"2025-07-03","ids":{"openalex":"https://openalex.org/W4411994565","doi":"https://doi.org/10.1007/s11227-025-07564-5"},"language":"en","primary_location":{"id":"doi:10.1007/s11227-025-07564-5","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11227-025-07564-5","pdf_url":"https://link.springer.com/content/pdf/10.1007/s11227-025-07564-5.pdf","source":{"id":"https://openalex.org/S32326811","display_name":"The Journal of Supercomputing","issn_l":"0920-8542","issn":["0920-8542","1573-0484"],"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":"The Journal of Supercomputing","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/s11227-025-07564-5.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5059391208","display_name":"Emrah Demir","orcid":"https://orcid.org/0000-0002-4726-2556"},"institutions":[{"id":"https://openalex.org/I4210091955","display_name":"Eskisehir Technical University","ror":"https://ror.org/00gcgqv39","country_code":"TR","type":"education","lineage":["https://openalex.org/I4210091955"]}],"countries":["TR"],"is_corresponding":true,"raw_author_name":"Emrah Demir","raw_affiliation_strings":["Department of Computer Engineering, Eskisehir Technical University, Eskisehir, T\u00fcrkiye"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Engineering, Eskisehir Technical University, Eskisehir, T\u00fcrkiye","institution_ids":["https://openalex.org/I4210091955"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003391938","display_name":"Serkan G\u00fcnal","orcid":"https://orcid.org/0000-0002-9691-1575"},"institutions":[{"id":"https://openalex.org/I4210091955","display_name":"Eskisehir Technical University","ror":"https://ror.org/00gcgqv39","country_code":"TR","type":"education","lineage":["https://openalex.org/I4210091955"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Serkan Gunal","raw_affiliation_strings":["Department of Computer Engineering, Eskisehir Technical University, Eskisehir, T\u00fcrkiye"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Engineering, Eskisehir Technical University, Eskisehir, T\u00fcrkiye","institution_ids":["https://openalex.org/I4210091955"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5059391208"],"corresponding_institution_ids":["https://openalex.org/I4210091955"],"apc_list":{"value":2390,"currency":"EUR","value_usd":2990},"apc_paid":{"value":2390,"currency":"EUR","value_usd":2990},"fwci":6.7146,"has_fulltext":true,"cited_by_count":13,"citation_normalized_percentile":{"value":0.97063249,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":"81","issue":"10","first_page":null,"last_page":null},"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9883000254631042,"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/T11918","display_name":"Forecasting Techniques and Applications","score":0.9818999767303467,"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":0.8956499099731445},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.7696342468261719},{"id":"https://openalex.org/keywords/electricity","display_name":"Electricity","score":0.6433122158050537},{"id":"https://openalex.org/keywords/consumption","display_name":"Consumption (sociology)","score":0.5952906012535095},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4630802571773529},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.41053104400634766},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3495590090751648},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.10094672441482544}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8956499099731445},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.7696342468261719},{"id":"https://openalex.org/C206658404","wikidata":"https://www.wikidata.org/wiki/Q12725","display_name":"Electricity","level":2,"score":0.6433122158050537},{"id":"https://openalex.org/C30772137","wikidata":"https://www.wikidata.org/wiki/Q5164762","display_name":"Consumption (sociology)","level":2,"score":0.5952906012535095},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4630802571773529},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.41053104400634766},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3495590090751648},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.10094672441482544},{"id":"https://openalex.org/C36289849","wikidata":"https://www.wikidata.org/wiki/Q34749","display_name":"Social science","level":1,"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/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/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1007/s11227-025-07564-5","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11227-025-07564-5","pdf_url":"https://link.springer.com/content/pdf/10.1007/s11227-025-07564-5.pdf","source":{"id":"https://openalex.org/S32326811","display_name":"The Journal of Supercomputing","issn_l":"0920-8542","issn":["0920-8542","1573-0484"],"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":"The Journal of Supercomputing","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1007/s11227-025-07564-5","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11227-025-07564-5","pdf_url":"https://link.springer.com/content/pdf/10.1007/s11227-025-07564-5.pdf","source":{"id":"https://openalex.org/S32326811","display_name":"The Journal of Supercomputing","issn_l":"0920-8542","issn":["0920-8542","1573-0484"],"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":"The Journal of Supercomputing","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G544913962","display_name":null,"funder_award_id":"22LOT247","funder_id":"https://openalex.org/F4320318147","funder_display_name":"Eski\u015fehir Teknik \u00dcniversitesi"}],"funders":[{"id":"https://openalex.org/F4320318147","display_name":"Eski\u015fehir Teknik \u00dcniversitesi","ror":"https://ror.org/00gcgqv39"},{"id":"https://openalex.org/F4320322626","display_name":"T\u00fcrkiye Bilimsel ve Teknolojik Ara\u015ft\u0131rma Kurumu","ror":"https://ror.org/04w9kkr77"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4411994565.pdf","grobid_xml":"https://content.openalex.org/works/W4411994565.grobid-xml"},"referenced_works_count":23,"referenced_works":["https://openalex.org/W1966897524","https://openalex.org/W1968112823","https://openalex.org/W2031054313","https://openalex.org/W2102148524","https://openalex.org/W2903802301","https://openalex.org/W2980994438","https://openalex.org/W3111532652","https://openalex.org/W3159352807","https://openalex.org/W3171186964","https://openalex.org/W3171884590","https://openalex.org/W4213021104","https://openalex.org/W4221017571","https://openalex.org/W4296021822","https://openalex.org/W4387940855","https://openalex.org/W4390892031","https://openalex.org/W4391361670","https://openalex.org/W4392907788","https://openalex.org/W4398151605","https://openalex.org/W4400111503","https://openalex.org/W4404180911","https://openalex.org/W4406089223","https://openalex.org/W4406126046","https://openalex.org/W4408223349"],"related_works":["https://openalex.org/W4375867731","https://openalex.org/W2980611886","https://openalex.org/W42295635","https://openalex.org/W1973996291","https://openalex.org/W2611989081","https://openalex.org/W2330575325","https://openalex.org/W2361441833","https://openalex.org/W3200447293","https://openalex.org/W3136869357","https://openalex.org/W4380075502"],"abstract_inverted_index":{"Abstract":[0],"Global":[1],"electricity":[2,28,50,61,84,165,186],"demand":[3],"is":[4,52],"surging":[5],"due":[6,32],"to":[7,33,154],"population":[8],"growth,":[9],"industrialization,":[10],"and":[11,36,43,77,87,94,98,118,140,182,198,204],"technological":[12],"advancements.":[13],"While":[14],"renewable":[15],"energy":[16,45,202],"sources":[17],"are":[18],"expanding,":[19],"fossil":[20],"fuels":[21],"still":[22],"remain":[23],"the":[24,115,158,171],"primary":[25],"source":[26],"of":[27,49,149,160,173],"generation,":[29],"posing":[30],"challenges":[31,42],"resource":[34],"limitations":[35],"environmental":[37],"concerns.":[38],"To":[39],"address":[40],"these":[41,190],"optimize":[44],"use,":[46],"accurate":[47],"prediction":[48,155],"consumption":[51,62,85,187],"crucial.":[53],"Therefore,":[54],"this":[55],"work":[56],"introduces":[57],"novel":[58],"short-term":[59,69],"(24-hour)":[60],"forecasting":[63,122,166],"models":[64,81,109],"based":[65],"on":[66,100],"customized":[67,174],"long":[68],"memory":[70],"(LSTM)":[71],"networks,":[72],"convolutional":[73],"neural":[74],"networks":[75],"(CNNs),":[76],"their":[78],"ensemble.":[79],"The":[80,147,168],"utilize":[82],"time-series":[83],"data":[86],"meteorological":[88,150],"features,":[89],"including":[90],"temperature,":[91],"relative":[92],"humidity,":[93],"wind":[95],"speed.":[96],"Trained":[97],"evaluated":[99],"two":[101],"geographically":[102],"distinct":[103],"datasets":[104],"spanning":[105],"2.5":[106],"years,":[107],"our":[108],"utilizing":[110],"appropriate":[111],"feature":[112],"sets":[113],"surpass":[114],"recent":[116],"studies":[117],"achieve":[119],"significantly":[120],"high":[121],"performance":[123],"with":[124],"normalized":[125,133],"root":[126],"mean":[127,134,141],"square":[128],"error":[129,136,144],"(N-RMSE)":[130],"reaching":[131,138,145],"0.16,":[132],"absolute":[135,142],"(N-MAE)":[137],"0.13,":[139],"percentage":[143],"4%.":[146],"inclusion":[148],"features":[151,163],"contributed":[152],"notably":[153],"performance,":[156],"demonstrating":[157],"benefit":[159],"integrating":[161],"external":[162],"in":[164,178,201],"models.":[167],"results":[169],"highlight":[170],"effectiveness":[172],"deep":[175],"learning":[176],"architectures":[177],"capturing":[179],"complex":[180],"temporal":[181],"contextual":[183],"dependencies":[184],"within":[185],"data.":[188],"Also,":[189],"findings":[191],"offer":[192],"valuable":[193],"insights":[194],"for":[195],"future":[196],"research":[197],"practical":[199],"applications":[200],"management":[203],"grid":[205],"optimization.":[206]},"counts_by_year":[{"year":2026,"cited_by_count":8},{"year":2025,"cited_by_count":5}],"updated_date":"2026-06-19T17:40:00.097472","created_date":"2025-10-10T00:00:00"}
