{"id":"https://openalex.org/W4415968438","doi":"https://doi.org/10.1109/iecon58223.2025.11221747","title":"Machine Learning Forecasting and GAN-Based Scenario Control for EV Charging and PV Integration","display_name":"Machine Learning Forecasting and GAN-Based Scenario Control for EV Charging and PV Integration","publication_year":2025,"publication_date":"2025-10-14","ids":{"openalex":"https://openalex.org/W4415968438","doi":"https://doi.org/10.1109/iecon58223.2025.11221747"},"language":null,"primary_location":{"id":"doi:10.1109/iecon58223.2025.11221747","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iecon58223.2025.11221747","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IECON 2025 \u2013 51st Annual Conference of the IEEE Industrial Electronics Society","raw_type":"proceedings-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/A5051954932","display_name":"Fatemeh Nasr Esfahani","orcid":"https://orcid.org/0000-0001-7107-2464"},"institutions":[{"id":"https://openalex.org/I67415387","display_name":"Lancaster University","ror":"https://ror.org/04f2nsd36","country_code":"GB","type":"education","lineage":["https://openalex.org/I67415387"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Fatemeh Nasr Esfahani","raw_affiliation_strings":["Lancaster University,School of Engineering,Lancaster,UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Lancaster University,School of Engineering,Lancaster,UK","institution_ids":["https://openalex.org/I67415387"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016133300","display_name":"Neeraj Suri","orcid":"https://orcid.org/0000-0003-1106-7668"},"institutions":[{"id":"https://openalex.org/I67415387","display_name":"Lancaster University","ror":"https://ror.org/04f2nsd36","country_code":"GB","type":"education","lineage":["https://openalex.org/I67415387"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Neeraj Suri","raw_affiliation_strings":["Lancaster University,School of Computing and Communications,Lancaster,UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Lancaster University,School of Computing and Communications,Lancaster,UK","institution_ids":["https://openalex.org/I67415387"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5067344099","display_name":"Xiandong Ma","orcid":"https://orcid.org/0000-0001-7363-9727"},"institutions":[{"id":"https://openalex.org/I67415387","display_name":"Lancaster University","ror":"https://ror.org/04f2nsd36","country_code":"GB","type":"education","lineage":["https://openalex.org/I67415387"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Xiandong Ma","raw_affiliation_strings":["Lancaster University,School of Engineering,Lancaster,UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Lancaster University,School of Engineering,Lancaster,UK","institution_ids":["https://openalex.org/I67415387"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.5352,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.72582594,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":98},"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":0.7678999900817871,"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":0.7678999900817871,"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/T10454","display_name":"Optimal Power Flow Distribution","score":0.049800001084804535,"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/T11052","display_name":"Energy Load and Power Forecasting","score":0.046799998730421066,"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/adaptability","display_name":"Adaptability","score":0.6406000256538391},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.6133000254631042},{"id":"https://openalex.org/keywords/flexibility","display_name":"Flexibility (engineering)","score":0.5911999940872192},{"id":"https://openalex.org/keywords/photovoltaic-system","display_name":"Photovoltaic system","score":0.5022000074386597},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.42590001225471497},{"id":"https://openalex.org/keywords/gradient-boosting","display_name":"Gradient boosting","score":0.3978999853134155},{"id":"https://openalex.org/keywords/demand-forecasting","display_name":"Demand forecasting","score":0.3846000134944916},{"id":"https://openalex.org/keywords/demand-response","display_name":"Demand response","score":0.35760000348091125}],"concepts":[{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.6406000256538391},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6398000121116638},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.6133000254631042},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.5911999940872192},{"id":"https://openalex.org/C41291067","wikidata":"https://www.wikidata.org/wiki/Q1897785","display_name":"Photovoltaic system","level":2,"score":0.5022000074386597},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4535999894142151},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.42590001225471497},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41110000014305115},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.3978999853134155},{"id":"https://openalex.org/C193809577","wikidata":"https://www.wikidata.org/wiki/Q3409300","display_name":"Demand forecasting","level":2,"score":0.3846000134944916},{"id":"https://openalex.org/C2779438525","wikidata":"https://www.wikidata.org/wiki/Q5255048","display_name":"Demand response","level":3,"score":0.35760000348091125},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.3476000130176544},{"id":"https://openalex.org/C2780150128","wikidata":"https://www.wikidata.org/wiki/Q21948731","display_name":"Extreme learning machine","level":3,"score":0.3409000039100647},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.33799999952316284},{"id":"https://openalex.org/C133731056","wikidata":"https://www.wikidata.org/wiki/Q4917288","display_name":"Control engineering","level":1,"score":0.32899999618530273},{"id":"https://openalex.org/C2777908891","wikidata":"https://www.wikidata.org/wiki/Q1806775","display_name":"Load profile","level":3,"score":0.3122999966144562},{"id":"https://openalex.org/C2742236","wikidata":"https://www.wikidata.org/wiki/Q924713","display_name":"Efficient energy use","level":2,"score":0.30000001192092896},{"id":"https://openalex.org/C77715397","wikidata":"https://www.wikidata.org/wiki/Q931447","display_name":"Electrical load","level":3,"score":0.2874999940395355},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.2791000008583069},{"id":"https://openalex.org/C7817414","wikidata":"https://www.wikidata.org/wiki/Q1779504","display_name":"Energy management","level":3,"score":0.27160000801086426},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.27129998803138733},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.26829999685287476},{"id":"https://openalex.org/C2780165032","wikidata":"https://www.wikidata.org/wiki/Q16869822","display_name":"Energy consumption","level":2,"score":0.2599000036716461},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.25699999928474426}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iecon58223.2025.11221747","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iecon58223.2025.11221747","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IECON 2025 \u2013 51st Annual Conference of the IEEE Industrial Electronics Society","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W2791871092","https://openalex.org/W3212933782","https://openalex.org/W4285506365","https://openalex.org/W4328007185","https://openalex.org/W4382204597","https://openalex.org/W4392877823","https://openalex.org/W4399039294","https://openalex.org/W4401113482","https://openalex.org/W4405717902","https://openalex.org/W4407404496","https://openalex.org/W4408281963","https://openalex.org/W4410966433","https://openalex.org/W4414003913"],"related_works":[],"abstract_inverted_index":{"The":[0,40,70,97],"increasing":[1],"integration":[2],"of":[3,94,136],"electric":[4],"vehicles":[5],"(EVs)":[6],"and":[7,52,61,83,90,112,149,160],"photovoltaic":[8],"(PV)":[9],"generation":[10,68,132],"introduces":[11],"significant":[12],"uncertainty":[13],"into":[14],"modern":[15],"distribution":[16,106],"grids.":[17],"This":[18],"paper":[19],"presents":[20],"a":[21,44,74,91,102],"dual-stage,":[22],"AI-driven":[23],"framework":[24,98],"for":[25,37,57,66],"resilient":[26],"energy":[27,158],"management":[28],"that":[29,117],"combines":[30],"machine":[31],"learning-based":[32],"forecasting":[33,46,120],"with":[34,108],"generative":[35,75],"modelling":[36],"scenario-based":[38],"control.":[39],"first":[41],"stage":[42,72],"uses":[43],"hybrid":[45],"architecture:":[47],"long":[48],"short-term":[49],"memory":[50],"(LSTM)":[51],"convolutional":[53],"LSTM":[54],"(ConvLSTM)":[55],"models":[56],"EV":[58,82,110],"demand":[59,161],"prediction":[60],"eXtreme":[62],"gradient":[63],"boosting":[64],"(XGBoost)":[65],"PV":[67,84],"forecasting.":[69],"second":[71],"employs":[73],"adversarial":[76],"network":[77],"(GAN)":[78],"to":[79],"produce":[80],"realistic":[81],"scenarios,":[85],"capturing":[86],"both":[87],"typical":[88],"variability":[89],"wide":[92],"range":[93],"operating":[95],"conditions.":[96],"is":[99],"validated":[100],"on":[101],"modified":[103],"IEEE":[104],"33-bus":[105],"system":[107],"integrated":[109],"charging":[111],"stationary":[113],"storage.":[114],"Results":[115],"show":[116],"the":[118,134],"dual-model":[119],"approach":[121],"achieves":[122],"high":[123],"accuracy":[124],"across":[125],"diverse":[126],"temporal":[127],"patterns,":[128],"while":[129],"GAN-based":[130],"scenario":[131],"improves":[133],"adaptability":[135],"control":[137,155],"decisions.":[138],"Scenario-based":[139],"optimisation":[140],"enhances":[141],"performance":[142],"under":[143],"uncertainty,":[144],"especially":[145],"at":[146],"high-variance":[147],"nodes,":[148],"offers":[150],"greater":[151],"flexibility":[152],"than":[153],"deterministic":[154],"in":[156],"balancing":[157],"cost":[159],"satisfaction.":[162]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-11-06T00:00:00"}