{"id":"https://openalex.org/W4388720048","doi":"https://doi.org/10.1109/iecon51785.2023.10312686","title":"Short-Term EV Charging Load Predicting Based on Adaptive VMD and LSTM Methods","display_name":"Short-Term EV Charging Load Predicting Based on Adaptive VMD and LSTM Methods","publication_year":2023,"publication_date":"2023-10-16","ids":{"openalex":"https://openalex.org/W4388720048","doi":"https://doi.org/10.1109/iecon51785.2023.10312686"},"language":"en","primary_location":{"id":"doi:10.1109/iecon51785.2023.10312686","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iecon51785.2023.10312686","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IECON 2023- 49th 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/A5026966155","display_name":"Quanxue Guan","orcid":"https://orcid.org/0000-0002-1379-620X"},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Quanxue Guan","raw_affiliation_strings":["School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-Sen University,Shenzhen,China","School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-Sen University, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-Sen University,Shenzhen,China","institution_ids":["https://openalex.org/I180726961"]},{"raw_affiliation_string":"School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-Sen University, Shenzhen, China","institution_ids":["https://openalex.org/I180726961"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114173956","display_name":"Qinhe Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qinhe Liu","raw_affiliation_strings":["School of Intelligent Systems Engineering Sun Yat-Sen University,Shenzhen,China","School of Intelligent Systems Engineering Sun Yat-Sen University, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Intelligent Systems Engineering Sun Yat-Sen University,Shenzhen,China","institution_ids":["https://openalex.org/I157773358"]},{"raw_affiliation_string":"School of Intelligent Systems Engineering Sun Yat-Sen University, Shenzhen, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066880251","display_name":"Di Zhou","orcid":"https://orcid.org/0000-0003-4815-3754"},"institutions":[{"id":"https://openalex.org/I4210158998","display_name":"Shenzhen Academy of Metrology and Quality Inspection","ror":"https://ror.org/04zv3rp09","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210158998"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Di Zhou","raw_affiliation_strings":["Shenzhen Academy of Metrology & Quality Inspection,Shenzhen,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shenzhen Academy of Metrology & Quality Inspection,Shenzhen,China","institution_ids":["https://openalex.org/I4210158998"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028612373","display_name":"Yunjian Xu","orcid":"https://orcid.org/0000-0003-0110-8032"},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"HK","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Yunjian Xu","raw_affiliation_strings":["The Chinese University of Hong Kong,Department of Mechanical and Automation Engineering,Hong Kong,China","Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Hong Kong, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Chinese University of Hong Kong,Department of Mechanical and Automation Engineering,Hong Kong,China","institution_ids":["https://openalex.org/I177725633"]},{"raw_affiliation_string":"Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Hong Kong, China","institution_ids":["https://openalex.org/I177725633"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101969861","display_name":"Xiaojun Tan","orcid":"https://orcid.org/0009-0009-3697-162X"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaojun Tan","raw_affiliation_strings":["School of Intelligent Systems Engineering Sun Yat-Sen University,Shenzhen,China","School of Intelligent Systems Engineering Sun Yat-Sen University, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Intelligent Systems Engineering Sun Yat-Sen University,Shenzhen,China","institution_ids":["https://openalex.org/I157773358"]},{"raw_affiliation_string":"School of Intelligent Systems Engineering Sun Yat-Sen University, Shenzhen, China","institution_ids":["https://openalex.org/I157773358"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.9303,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.74056604,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"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/T10663","display_name":"Advanced Battery Technologies Research","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T10663","display_name":"Advanced Battery Technologies Research","score":0.9998999834060669,"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/T10768","display_name":"Electric Vehicles and Infrastructure","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/T10808","display_name":"Electric and Hybrid Vehicle Technologies","score":0.9922999739646912,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.7193830013275146},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6076931953430176},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5540030002593994},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.5419155359268188},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.533656656742096},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5212924480438232},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.4896441102027893},{"id":"https://openalex.org/keywords/mode","display_name":"Mode (computer interface)","score":0.4722042977809906},{"id":"https://openalex.org/keywords/electrical-load","display_name":"Electrical load","score":0.46999868750572205},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.4263274371623993},{"id":"https://openalex.org/keywords/root-mean-square","display_name":"Root mean square","score":0.41161590814590454},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.4109550416469574},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3236422538757324},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3133063018321991},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2063153088092804},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.16112318634986877},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1492103636264801},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.07340565323829651}],"concepts":[{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.7193830013275146},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6076931953430176},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5540030002593994},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.5419155359268188},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.533656656742096},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5212924480438232},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.4896441102027893},{"id":"https://openalex.org/C48677424","wikidata":"https://www.wikidata.org/wiki/Q6888088","display_name":"Mode (computer interface)","level":2,"score":0.4722042977809906},{"id":"https://openalex.org/C77715397","wikidata":"https://www.wikidata.org/wiki/Q931447","display_name":"Electrical load","level":3,"score":0.46999868750572205},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.4263274371623993},{"id":"https://openalex.org/C71907059","wikidata":"https://www.wikidata.org/wiki/Q223323","display_name":"Root mean square","level":2,"score":0.41161590814590454},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.4109550416469574},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3236422538757324},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3133063018321991},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2063153088092804},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.16112318634986877},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1492103636264801},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.07340565323829651},{"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iecon51785.2023.10312686","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iecon51785.2023.10312686","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Society","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W2000982976","https://openalex.org/W2600172076","https://openalex.org/W2782126571","https://openalex.org/W2941906448","https://openalex.org/W2972837086","https://openalex.org/W2985287032","https://openalex.org/W2998796779","https://openalex.org/W3002014232","https://openalex.org/W3004802849","https://openalex.org/W3043725089","https://openalex.org/W3044436817","https://openalex.org/W3083957475","https://openalex.org/W3092642546","https://openalex.org/W3126238986","https://openalex.org/W3126866643","https://openalex.org/W3189940786","https://openalex.org/W3215514190","https://openalex.org/W3215693540","https://openalex.org/W4205845540","https://openalex.org/W4212993228","https://openalex.org/W4225769574","https://openalex.org/W4229025385","https://openalex.org/W4283743430","https://openalex.org/W4289950750"],"related_works":["https://openalex.org/W4225394202","https://openalex.org/W4298287631","https://openalex.org/W2953061907","https://openalex.org/W1847088711","https://openalex.org/W3036642985","https://openalex.org/W3032952384","https://openalex.org/W2964006806","https://openalex.org/W3011201302","https://openalex.org/W4386220996","https://openalex.org/W2100663672"],"abstract_inverted_index":{"The":[0,50,138],"uncoordinated":[1],"charging":[2,22,32,58],"of":[3,13,21,80,133],"large-scale":[4],"electric":[5,15,25],"vehicles":[6],"(EVs)":[7],"generally":[8],"deteriorates":[9],"the":[10,19,38,44,56,72,75,84,90,94,108,115,119,124,134,144,149,156,169],"peak-valley":[11],"difference":[12],"daily":[14],"demands.":[16],"To":[17,87],"facilitate":[18],"operation":[20],"stations":[23],"and":[24,43,83,93,123,155,164],"power":[26],"distributers,":[27],"this":[28],"work":[29],"proposes":[30],"a":[31],"load":[33,59],"prediction":[34,91,136],"algorithm":[35,117],"by":[36,162],"combining":[37],"Variational":[39],"Mode":[40],"Decomposition":[41],"(VMD)":[42],"Long":[45],"Short-Term":[46],"Memory":[47],"(LSTM)":[48],"methods.":[49],"VMD":[51,109,146],"is":[52,103],"adopted":[53],"to":[54,105,168],"extract":[55],"EV":[57],"features":[60],"at":[61],"different":[62],"time":[63],"scales,":[64],"obtaining":[65],"multiple":[66],"intrinsic":[67],"mode":[68],"functions":[69],"(IMFs).":[70],"Then":[71],"LSTM":[73,121,141,170],"establishes":[74],"dependencies":[76],"between":[77,89],"these":[78],"IMFs":[79],"historical":[81],"data":[82],"predicted":[85],"load.":[86],"trade-off":[88],"accuracy":[92],"computation":[95],"overhead,":[96],"an":[97],"additional":[98],"Snake":[99],"Optimization":[100],"(SO)":[101],"technique":[102],"applied":[104],"adaptively":[106],"optimize":[107],"parameters.":[110],"Experimental":[111],"results":[112],"show":[113],"that":[114],"proposed":[116,139],"outperforms":[118],"traditional":[120],"alone":[122,128],"Gate":[125],"Recurrent":[126],"Unit":[127],"neural":[129],"networks":[130],"in":[131],"terms":[132],"overall":[135],"accuracy.":[137],"parallel":[140],"structure":[142],"with":[143,166,172],"optimized":[145],"further":[147],"reduces":[148],"Root":[150],"Mean":[151,157],"Square":[152],"Error":[153,159],"(RMSE)":[154],"Absolute":[158],"(MAE)":[160],"significantly":[161],"55.1%":[163],"55.9%":[165],"respect":[167],"method":[171],"non-optimized":[173],"VMD.":[174]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":5}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}