{"id":"https://openalex.org/W7160486917","doi":"https://doi.org/10.48550/arxiv.2605.04993","title":"Federated Learning for Early Prediction of EV Charging Demand","display_name":"Federated Learning for Early Prediction of EV Charging Demand","publication_year":2026,"publication_date":"2026-05-06","ids":{"openalex":"https://openalex.org/W7160486917","doi":"https://doi.org/10.48550/arxiv.2605.04993"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.04993","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.04993","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.04993","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135589819","display_name":"Vasilis Perifanis","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Perifanis, Vasilis","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135619192","display_name":"Foteini Nikolaidou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nikolaidou, Foteini","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135571081","display_name":"Nikolaos Pavlidis","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pavlidis, Nikolaos","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135542349","display_name":"Panagiotis Thomakos","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Thomakos, Panagiotis","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5041109411","display_name":"Andreas Sendros","orcid":"https://orcid.org/0000-0002-6337-4320"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sendros, Andreas","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10768","display_name":"Electric Vehicles and Infrastructure","score":0.9860000014305115,"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":0.9860000014305115,"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/T10603","display_name":"Smart Grid Energy Management","score":0.0044999998062849045,"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/T11249","display_name":"Wireless Power Transfer Systems","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/session","display_name":"Session (web analytics)","score":0.7480999827384949},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6848000288009644},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.6157000064849854},{"id":"https://openalex.org/keywords/metadata","display_name":"Metadata","score":0.5467000007629395},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.5422000288963318},{"id":"https://openalex.org/keywords/analytics","display_name":"Analytics","score":0.4341999888420105},{"id":"https://openalex.org/keywords/grid","display_name":"Grid","score":0.39730000495910645}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7696999907493591},{"id":"https://openalex.org/C2779182362","wikidata":"https://www.wikidata.org/wiki/Q17126187","display_name":"Session (web analytics)","level":2,"score":0.7480999827384949},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6848000288009644},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.6157000064849854},{"id":"https://openalex.org/C93518851","wikidata":"https://www.wikidata.org/wiki/Q180160","display_name":"Metadata","level":2,"score":0.5467000007629395},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.5422000288963318},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.43720000982284546},{"id":"https://openalex.org/C79158427","wikidata":"https://www.wikidata.org/wiki/Q485396","display_name":"Analytics","level":2,"score":0.4341999888420105},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.412200003862381},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.4041000008583069},{"id":"https://openalex.org/C187691185","wikidata":"https://www.wikidata.org/wiki/Q2020720","display_name":"Grid","level":2,"score":0.39730000495910645},{"id":"https://openalex.org/C2779438525","wikidata":"https://www.wikidata.org/wiki/Q5255048","display_name":"Demand response","level":3,"score":0.3928999900817871},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.38999998569488525},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.3752000033855438},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.37459999322891235},{"id":"https://openalex.org/C47487241","wikidata":"https://www.wikidata.org/wiki/Q5227230","display_name":"Data access","level":2,"score":0.3407999873161316},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.3366999924182892},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.3188000023365021},{"id":"https://openalex.org/C2742236","wikidata":"https://www.wikidata.org/wiki/Q924713","display_name":"Efficient energy use","level":2,"score":0.2994999885559082},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.27320000529289246},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.25839999318122864}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.04993","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.04993","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.04993","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.04993","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.6501613259315491}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"forecasting":[1],"of":[2,26,29,36,53,68],"electric":[3],"vehicle":[4],"(EV)":[5],"charging":[6,17,30,91,104,153,186],"demand":[7,160],"is":[8,39,62,67,189],"critical":[9],"for":[10,71,184],"grid":[11],"stability,":[12],"infrastructure":[13],"planning,":[14],"and":[15,48,93,102,114,172,181],"real-time":[16],"optimization.":[18],"In":[19],"this":[20],"work,":[21],"we":[22,156],"study":[23],"the":[24,33,50,60,81,167],"problem":[25],"early":[27,165],"prediction":[28],"demand,":[31],"where":[32],"total":[34],"energy":[35],"a":[37,77,109,128,176],"session":[38,61,87,168],"estimated":[40],"using":[41],"only":[42],"information":[43],"available":[44,190],"at":[45,191],"plug-in":[46],"time":[47],"during":[49],"first":[51],"minutes":[52],"charging.":[54],"This":[55],"enables":[56],"actionable":[57],"decisions":[58],"while":[59,122,144],"still":[63],"in":[64,127,166],"progress,":[65],"which":[66],"direct":[69],"importance":[70],"EV":[72,185],"network":[73],"operators.":[74],"We":[75,106],"construct":[76],"session-level":[78],"dataset":[79],"from":[80],"Adaptive":[82],"Charging":[83],"Network":[84],"(ACN),":[85],"combining":[86],"metadata":[88],"with":[89,169],"early-window":[90],"measurements,":[92],"derive":[94],"tabular":[95],"features":[96],"capturing":[97],"user":[98],"intent,":[99],"temporal":[100],"patterns,":[101],"initial":[103],"behavior.":[105],"focus":[107],"on":[108],"single":[110],"operational":[111],"depot,":[112],"Caltech,":[113],"model":[115,125],"intra-depot":[116],"heterogeneity":[117],"through":[118],"station-level":[119],"client":[120],"partitions":[121],"evaluating":[123],"multiple":[124],"families":[126],"federated":[129,137],"learning":[130],"(FL)":[131],"setting.":[132],"Our":[133],"results":[134],"show":[135],"that":[136,158,173],"models":[138],"can":[139,162],"approach":[140],"centralized":[141],"predictive":[142],"performance":[143],"keeping":[145],"data":[146],"in-depot,":[147],"enabling":[148],"privacy-enhanced":[149],"training":[150],"across":[151],"distributed":[152],"infrastructures.":[154],"Overall,":[155],"demonstrate":[157],"reliable":[159],"estimates":[161],"be":[163],"obtained":[164],"minimal":[170],"data,":[171],"FL":[174],"provides":[175],"practical":[177],"pathway":[178],"toward":[179],"scalable":[180],"privacy-aware":[182],"analytics":[183],"networks.":[187],"Code":[188],"https://github.com/Indigma-Innovations/federated-learning-ev-charging-demand.":[192]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-05-08T00:00:00"}