{"id":"https://openalex.org/W4416183297","doi":"https://doi.org/10.1109/mt-its68460.2025.11223527","title":"A Multi-Source and Data-Driven Deep Learning Framework for Forecasting EV Charging Demand","display_name":"A Multi-Source and Data-Driven Deep Learning Framework for Forecasting EV Charging Demand","publication_year":2025,"publication_date":"2025-09-08","ids":{"openalex":"https://openalex.org/W4416183297","doi":"https://doi.org/10.1109/mt-its68460.2025.11223527"},"language":"en","primary_location":{"id":"doi:10.1109/mt-its68460.2025.11223527","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mt-its68460.2025.11223527","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 9th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://orbilu.uni.lu/bitstream/10993/67356/1/LSTM-MT-ITS-2025.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102744581","display_name":"Seyed Hassan Hosseini","orcid":null},"institutions":[{"id":"https://openalex.org/I186903577","display_name":"University of Luxembourg","ror":"https://ror.org/036x5ad56","country_code":"LU","type":"education","lineage":["https://openalex.org/I186903577"]},{"id":"https://openalex.org/I4210161935","display_name":"Arbed (Luxembourg)","ror":"https://ror.org/03wgsss62","country_code":"LU","type":"company","lineage":["https://openalex.org/I4210161935","https://openalex.org/I50754188"]}],"countries":["LU"],"is_corresponding":false,"raw_author_name":"Seyed Hassan Hosseini","raw_affiliation_strings":["University of Luxembourg,Faculty of Science, Technology and Medicine, Mobilab Transport Research Group,Esch-sur-Alzette,Luxembourg"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Luxembourg,Faculty of Science, Technology and Medicine, Mobilab Transport Research Group,Esch-sur-Alzette,Luxembourg","institution_ids":["https://openalex.org/I186903577","https://openalex.org/I4210161935"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042955923","display_name":"Federico Bigi","orcid":null},"institutions":[{"id":"https://openalex.org/I186903577","display_name":"University of Luxembourg","ror":"https://ror.org/036x5ad56","country_code":"LU","type":"education","lineage":["https://openalex.org/I186903577"]},{"id":"https://openalex.org/I4210161935","display_name":"Arbed (Luxembourg)","ror":"https://ror.org/03wgsss62","country_code":"LU","type":"company","lineage":["https://openalex.org/I4210161935","https://openalex.org/I50754188"]}],"countries":["LU"],"is_corresponding":false,"raw_author_name":"Federico Bigi","raw_affiliation_strings":["University of Luxembourg,Faculty of Science, Technology and Medicine, Mobilab Transport Research Group,Esch-sur-Alzette,Luxembourg"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Luxembourg,Faculty of Science, Technology and Medicine, Mobilab Transport Research Group,Esch-sur-Alzette,Luxembourg","institution_ids":["https://openalex.org/I186903577","https://openalex.org/I4210161935"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5042705900","display_name":"Francesco Viti","orcid":"https://orcid.org/0000-0003-1803-4527"},"institutions":[{"id":"https://openalex.org/I186903577","display_name":"University of Luxembourg","ror":"https://ror.org/036x5ad56","country_code":"LU","type":"education","lineage":["https://openalex.org/I186903577"]},{"id":"https://openalex.org/I4210161935","display_name":"Arbed (Luxembourg)","ror":"https://ror.org/03wgsss62","country_code":"LU","type":"company","lineage":["https://openalex.org/I4210161935","https://openalex.org/I50754188"]}],"countries":["LU"],"is_corresponding":false,"raw_author_name":"Francesco Viti","raw_affiliation_strings":["University of Luxembourg,Faculty of Science, Technology and Medicine, Mobilab Transport Research Group,Esch-sur-Alzette,Luxembourg"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Luxembourg,Faculty of Science, Technology and Medicine, Mobilab Transport Research Group,Esch-sur-Alzette,Luxembourg","institution_ids":["https://openalex.org/I186903577","https://openalex.org/I4210161935"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.41035554,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10768","display_name":"Electric Vehicles and Infrastructure","score":0.982200026512146,"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/T10768","display_name":"Electric Vehicles and Infrastructure","score":0.982200026512146,"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/T11942","display_name":"Transportation and Mobility Innovations","score":0.005200000014156103,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive 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/T10603","display_name":"Smart Grid Energy Management","score":0.0015999999595806003,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5795999765396118},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.5181000232696533},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4797999858856201},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4767000079154968},{"id":"https://openalex.org/keywords/popularity","display_name":"Popularity","score":0.4593000113964081},{"id":"https://openalex.org/keywords/demand-forecasting","display_name":"Demand forecasting","score":0.4546000063419342},{"id":"https://openalex.org/keywords/mean-absolute-percentage-error","display_name":"Mean absolute percentage error","score":0.4138000011444092},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.39879998564720154},{"id":"https://openalex.org/keywords/charging-station","display_name":"Charging station","score":0.39809998869895935}],"concepts":[{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5795999765396118},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5288000106811523},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.5181000232696533},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4797999858856201},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4767000079154968},{"id":"https://openalex.org/C2780586970","wikidata":"https://www.wikidata.org/wiki/Q1357284","display_name":"Popularity","level":2,"score":0.4593000113964081},{"id":"https://openalex.org/C193809577","wikidata":"https://www.wikidata.org/wiki/Q3409300","display_name":"Demand forecasting","level":2,"score":0.4546000063419342},{"id":"https://openalex.org/C150217764","wikidata":"https://www.wikidata.org/wiki/Q6803607","display_name":"Mean absolute percentage error","level":3,"score":0.4138000011444092},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.39879998564720154},{"id":"https://openalex.org/C2779607880","wikidata":"https://www.wikidata.org/wiki/Q2140665","display_name":"Charging station","level":4,"score":0.39809998869895935},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.39010000228881836},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38690000772476196},{"id":"https://openalex.org/C2776422217","wikidata":"https://www.wikidata.org/wiki/Q13629441","display_name":"Electric vehicle","level":3,"score":0.3833000063896179},{"id":"https://openalex.org/C160331591","wikidata":"https://www.wikidata.org/wiki/Q7075743","display_name":"Occupancy","level":2,"score":0.3781000077724457},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3714999854564667},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36809998750686646},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.36340001225471497},{"id":"https://openalex.org/C188154048","wikidata":"https://www.wikidata.org/wiki/Q6803609","display_name":"Mean absolute error","level":3,"score":0.35030001401901245},{"id":"https://openalex.org/C146778888","wikidata":"https://www.wikidata.org/wiki/Q836862","display_name":"Installation","level":2,"score":0.34940001368522644},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.3077999949455261},{"id":"https://openalex.org/C110593043","wikidata":"https://www.wikidata.org/wiki/Q7300787","display_name":"Real-time data","level":2,"score":0.30329999327659607},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.298799991607666},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.2865999937057495},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.27250000834465027},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.27149999141693115},{"id":"https://openalex.org/C120330832","wikidata":"https://www.wikidata.org/wiki/Q166656","display_name":"Supply and demand","level":2,"score":0.27070000767707825},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.26840001344680786},{"id":"https://openalex.org/C167085575","wikidata":"https://www.wikidata.org/wiki/Q6803654","display_name":"Mean squared prediction error","level":2,"score":0.26460000872612},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2624000012874603},{"id":"https://openalex.org/C2985733770","wikidata":"https://www.wikidata.org/wiki/Q1233007","display_name":"Travel time","level":2,"score":0.2581000030040741},{"id":"https://openalex.org/C539828613","wikidata":"https://www.wikidata.org/wiki/Q178512","display_name":"Public transport","level":2,"score":0.2556000053882599}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/mt-its68460.2025.11223527","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mt-its68460.2025.11223527","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 9th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS)","raw_type":"proceedings-article"},{"id":"pmh:oai:orbilu.uni.lu:10993/67356","is_oa":true,"landing_page_url":"https://orbilu.uni.lu/handle/10993/67356","pdf_url":"https://orbilu.uni.lu/bitstream/10993/67356/1/LSTM-MT-ITS-2025.pdf","source":{"id":"https://openalex.org/S4306401815","display_name":"Open Repository and Bibliography (University of Luxembourg)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I186903577","host_organization_name":"University of Luxembourg","host_organization_lineage":["https://openalex.org/I186903577"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"2025 9th International Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2025, 1-6 (2025-10-08); 2025 9th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), Luxembourg, Lux [Lux], 08-09-2025 => 10-09-2025","raw_type":"http://purl.org/coar/resource_type/c_5794"}],"best_oa_location":{"id":"pmh:oai:orbilu.uni.lu:10993/67356","is_oa":true,"landing_page_url":"https://orbilu.uni.lu/handle/10993/67356","pdf_url":"https://orbilu.uni.lu/bitstream/10993/67356/1/LSTM-MT-ITS-2025.pdf","source":{"id":"https://openalex.org/S4306401815","display_name":"Open Repository and Bibliography (University of Luxembourg)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I186903577","host_organization_name":"University of Luxembourg","host_organization_lineage":["https://openalex.org/I186903577"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"2025 9th International Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2025, 1-6 (2025-10-08); 2025 9th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), Luxembourg, Lux [Lux], 08-09-2025 => 10-09-2025","raw_type":"http://purl.org/coar/resource_type/c_5794"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2765202425","display_name":null,"funder_award_id":"2021-2027","funder_id":"https://openalex.org/F4320335322","funder_display_name":"European Regional Development Fund"}],"funders":[{"id":"https://openalex.org/F4320335322","display_name":"European Regional Development Fund","ror":"https://ror.org/00k4n6c32"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4416183297.pdf","grobid_xml":"https://content.openalex.org/works/W4416183297.grobid-xml"},"referenced_works_count":18,"referenced_works":["https://openalex.org/W2015315453","https://openalex.org/W2507562171","https://openalex.org/W2801231775","https://openalex.org/W2901072570","https://openalex.org/W2943584607","https://openalex.org/W2952348579","https://openalex.org/W2962444989","https://openalex.org/W3130706706","https://openalex.org/W3137115390","https://openalex.org/W3196178504","https://openalex.org/W4200302868","https://openalex.org/W4206647748","https://openalex.org/W4220851862","https://openalex.org/W4240592325","https://openalex.org/W4309734258","https://openalex.org/W4318957106","https://openalex.org/W4366779614","https://openalex.org/W4394781296"],"related_works":[],"abstract_inverted_index":{"Urban":[0],"mobility":[1],"is":[2,33,126],"experiencing":[3],"a":[4,98,177,181,193,201],"major":[5],"shift":[6],"with":[7,137,160],"the":[8,17,30,81,112,115,119,138,146,150,167,214],"rising":[9],"popularity":[10],"of":[11,68,118,140,149,199,206,211],"electric":[12],"vehicles":[13],"(EVs).":[14],"To":[15],"meet":[16],"growing":[18],"demand":[19,104],"for":[20,101,128],"EVs,":[21],"charging":[22,53,62,88,103,121,135,143,170],"stations":[23,144],"must":[24],"offer":[25],"sufficient":[26],"coverage,":[27],"therefore":[28],"analyzing":[29],"current":[31],"infrastructure":[32],"key":[34],"to":[35,38,51,165,189],"be":[36],"able":[37],"forecast":[39],"future":[40],"demand.":[41,151],"This":[42],"study":[43,80],"presents":[44],"an":[45,209],"innovative":[46],"data-driven":[47],"deep":[48],"learning":[49,162],"framework":[50],"predict":[52,166],"demand,":[54],"integrating":[55,105],"data":[56,85,117,183],"from":[57,114],"public":[58],"parking":[59,90,106],"lot":[60],"occupancy,":[61,91],"station":[63],"usage,":[64],"and":[65,89,92,108,179,208],"crowdsourced":[66,93,109],"point":[67],"interest":[69],"(POI)":[70],"popularity.":[71],"Our":[72],"paper":[73],"provides":[74],"two":[75],"significant":[76],"contributions:":[77],"first,":[78],"we":[79,96],"correlation":[82],"between":[83],"three":[84],"types":[86],"-":[87],"data.":[94,216],"Secondly,":[95],"propose":[97],"predictive":[99],"model":[100,125],"EV":[102],"occupancy":[107],"data,":[110],"disconnecting":[111],"prediction":[113],"historical":[116],"selected":[120],"stations.":[122],"Consequently,":[123],"this":[124],"wellsuited":[127],"application":[129],"where":[130],"there":[131],"are":[132],"areas":[133],"lacking":[134],"infrastructure,":[136],"objective":[139],"installing":[141],"new":[142],"without":[145],"prior":[147],"knowledge":[148],"Sequence-to-Sequence":[152],"Recurrent":[153],"Neural":[154],"Networks":[155],"(seq2seq":[156],"RNNs)":[157],"were":[158],"applied":[159],"various":[161],"time":[163],"windows":[164],"next":[168],"24-hour":[169],"usage":[171],"pattern.":[172],"The":[173],"model,":[174],"trained":[175],"on":[176,213],"zonelevel":[178],"used":[180],"72-hour":[182],"window,":[184],"demonstrated":[185],"better":[186],"performance":[187],"compared":[188],"other":[190],"models,":[191],"achieving":[192],"Root":[194],"Mean":[195,202],"Squared":[196],"Error":[197,204],"(RMSE)":[198],"6.75,":[200],"Absolute":[203],"(MAE)":[205],"5.02,":[207],"R2score":[210],"0.80":[212],"test":[215]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-11-11T00:00:00"}
