{"id":"https://openalex.org/W4417480010","doi":"https://doi.org/10.1063/5.0302761","title":"Improving short-term power load forecasting with a domain adversarial transfer network","display_name":"Improving short-term power load forecasting with a domain adversarial transfer network","publication_year":2025,"publication_date":"2025-12-01","ids":{"openalex":"https://openalex.org/W4417480010","doi":"https://doi.org/10.1063/5.0302761"},"language":"en","primary_location":{"id":"doi:10.1063/5.0302761","doi":"https://doi.org/10.1063/5.0302761","is_oa":true,"landing_page_url":"https://doi.org/10.1063/5.0302761","pdf_url":null,"source":{"id":"https://openalex.org/S189917590","display_name":"AIP Advances","issn_l":"2158-3226","issn":["2158-3226"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","dongbi-d","jufo-1","ki-jl-1","norway-1","poland-70","russia-white-list-2","vabb-shw"],"host_organization":"https://openalex.org/P4310320257","host_organization_name":"American Institute of Physics","host_organization_lineage":["https://openalex.org/P4310320257"],"host_organization_lineage_names":["American Institute of Physics"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"AIP Advances","raw_type":"journal-article"},"volume":null,"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1063/5.0302761","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Yuliang Zhang","orcid":"https://orcid.org/0009-0004-5988-4362","observed_orcids":["https://orcid.org/0009-0004-5988-4362"]},"institutions":[{"id":"https://openalex.org/I4210155952","display_name":"Qingdao Huanghai University","ror":"https://ror.org/05e1zbn94","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210155952"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuliang Zhang","raw_affiliation_strings":["College of Intelligent Manufacturing, Qingdao Huanghai University 1 , Qingdao, Shandong 266427,"],"raw_orcid":"https://orcid.org/0009-0004-5988-4362","affiliations":[{"raw_affiliation_string":"College of Intelligent Manufacturing, Qingdao Huanghai University 1 , Qingdao, Shandong 266427,","institution_ids":["https://openalex.org/I4210155952"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Zhengbo You","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhengbo You","raw_affiliation_strings":["2 Jiangsu Anfor Power Technology Co., LTD., Taizhou, Jiangsu 225300, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"2 Jiangsu Anfor Power Technology Co., LTD., Taizhou, Jiangsu 225300, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083119189","display_name":"Haichun Niu","orcid":"https://orcid.org/0000-0003-1614-4888","observed_orcids":["https://orcid.org/0000-0003-1614-4888"]},"institutions":[{"id":"https://openalex.org/I4210155952","display_name":"Qingdao Huanghai University","ror":"https://ror.org/05e1zbn94","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210155952"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haichun Niu","raw_affiliation_strings":["College of Intelligent Manufacturing, Qingdao Huanghai University 1 , Qingdao, Shandong 266427,"],"raw_orcid":"https://orcid.org/0000-0003-1614-4888","affiliations":[{"raw_affiliation_string":"College of Intelligent Manufacturing, Qingdao Huanghai University 1 , Qingdao, Shandong 266427,","institution_ids":["https://openalex.org/I4210155952"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Feifei An","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4210155952","display_name":"Qingdao Huanghai University","ror":"https://ror.org/05e1zbn94","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210155952"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Feifei An","raw_affiliation_strings":["School of Languages and Cultures, Qingdao Huanghai University 3 , Qingdao, Shandong 266427,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Languages and Cultures, Qingdao Huanghai University 3 , Qingdao, Shandong 266427,","institution_ids":["https://openalex.org/I4210155952"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5090930124","display_name":"Liang Hou","orcid":"https://orcid.org/0009-0008-1491-087X","observed_orcids":["https://orcid.org/0009-0008-1491-087X"]},"institutions":[{"id":"https://openalex.org/I4210155952","display_name":"Qingdao Huanghai University","ror":"https://ror.org/05e1zbn94","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210155952"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liang Hou","raw_affiliation_strings":["College of Intelligent Manufacturing, Qingdao Huanghai University 1 , Qingdao, Shandong 266427,"],"raw_orcid":"https://orcid.org/0009-0008-1491-087X","affiliations":[{"raw_affiliation_string":"College of Intelligent Manufacturing, Qingdao Huanghai University 1 , Qingdao, Shandong 266427,","institution_ids":["https://openalex.org/I4210155952"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1350,"currency":"USD","value_usd":1350},"apc_paid":{"value":1350,"currency":"USD","value_usd":1350},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.20730707,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"15","issue":"12","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":0.9984999895095825,"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":0.9984999895095825,"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":0.0003000000142492354,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.00019999999494757503,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/short-term-load-forecasting","display_name":"short-term load forecasting","score":0.8999999761581421},{"id":"https://openalex.org/keywords/transformer-feature-extraction","display_name":"Transformer feature extraction","score":0.652999997138977},{"id":"https://openalex.org/keywords/domain-invariant-features","display_name":"domain-invariant features","score":0.8610000014305115},{"id":"https://openalex.org/keywords/distribution-shift","display_name":"distribution shift","score":0.6290000081062317},{"id":"https://openalex.org/keywords/multi-kernel-maximum-mean-discrepancy","display_name":"multi-kernel maximum mean discrepancy","score":0.8759999871253967},{"id":"https://openalex.org/keywords/correlation-alignment","display_name":"correlation alignment","score":0.9890000224113464},{"id":"https://openalex.org/keywords/small-sample-learning","display_name":"small sample learning","score":0.6620000004768372},{"id":"https://openalex.org/keywords/industrial-electricity-consumption","display_name":"industrial electricity consumption","score":0.699999988079071},{"id":"https://openalex.org/keywords/load-forecasting","display_name":"load forecasting","score":0.7229999899864197}],"concepts":[{"id":"https://openalex.org/C2779803651","wikidata":"https://www.wikidata.org/wiki/Q5282088","display_name":"Discriminator","level":3,"score":0.8629999756813049},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.7730000019073486},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7592999935150146},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.7200999855995178},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.5645999908447266},{"id":"https://openalex.org/C206658404","wikidata":"https://www.wikidata.org/wiki/Q12725","display_name":"Electricity","level":2,"score":0.5152999758720398},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.49630001187324524},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.4368000030517578},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4318000078201294},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4296000003814697},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42410001158714294},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4101000130176544},{"id":"https://openalex.org/C2780165032","wikidata":"https://www.wikidata.org/wiki/Q16869822","display_name":"Energy consumption","level":2,"score":0.38659998774528503},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3456999957561493},{"id":"https://openalex.org/C2776175482","wikidata":"https://www.wikidata.org/wiki/Q1195816","display_name":"Transfer (computing)","level":2,"score":0.3228999972343445},{"id":"https://openalex.org/C2984118289","wikidata":"https://www.wikidata.org/wiki/Q29954","display_name":"Power consumption","level":3,"score":0.321399986743927},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.3199999928474426},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.3172000050544739},{"id":"https://openalex.org/C103824480","wikidata":"https://www.wikidata.org/wiki/Q185889","display_name":"Time domain","level":2,"score":0.3089999854564667},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.30410000681877136},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.2922999858856201},{"id":"https://openalex.org/C67186554","wikidata":"https://www.wikidata.org/wiki/Q17103352","display_name":"Maximum power transfer theorem","level":3,"score":0.27970001101493835},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.27900001406669617},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.27559998631477356},{"id":"https://openalex.org/C423512","wikidata":"https://www.wikidata.org/wiki/Q383973","display_name":"Electricity generation","level":3,"score":0.2727000117301941},{"id":"https://openalex.org/C77715397","wikidata":"https://www.wikidata.org/wiki/Q931447","display_name":"Electrical load","level":3,"score":0.25870001316070557},{"id":"https://openalex.org/C2777908891","wikidata":"https://www.wikidata.org/wiki/Q1806775","display_name":"Load profile","level":3,"score":0.25519999861717224}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1063/5.0302761","doi":"https://doi.org/10.1063/5.0302761","is_oa":true,"landing_page_url":"https://doi.org/10.1063/5.0302761","pdf_url":null,"source":{"id":"https://openalex.org/S189917590","display_name":"AIP Advances","issn_l":"2158-3226","issn":["2158-3226"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","dongbi-d","jufo-1","ki-jl-1","norway-1","poland-70","russia-white-list-2","vabb-shw"],"host_organization":"https://openalex.org/P4310320257","host_organization_name":"American Institute of Physics","host_organization_lineage":["https://openalex.org/P4310320257"],"host_organization_lineage_names":["American Institute of Physics"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"AIP Advances","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:c3ee555bf12a46d2a5d32203e9ec7264","doi":null,"is_oa":true,"landing_page_url":"https://doaj.org/article/c3ee555bf12a46d2a5d32203e9ec7264","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"AIP Advances, Vol 15, Iss 12, Pp - (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1063/5.0302761","doi":"https://doi.org/10.1063/5.0302761","is_oa":true,"landing_page_url":"https://doi.org/10.1063/5.0302761","pdf_url":null,"source":{"id":"https://openalex.org/S189917590","display_name":"AIP Advances","issn_l":"2158-3226","issn":["2158-3226"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","dongbi-d","jufo-1","ki-jl-1","norway-1","poland-70","russia-white-list-2","vabb-shw"],"host_organization":"https://openalex.org/P4310320257","host_organization_name":"American Institute of Physics","host_organization_lineage":["https://openalex.org/P4310320257"],"host_organization_lineage_names":["American Institute of Physics"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"AIP Advances","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","score":0.9090999960899353,"display_name":"Affordable and clean energy"},{"id":"https://metadata.un.org/sdg/9","score":0.7771999835968018,"display_name":"Industry, innovation and infrastructure"}],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W2584886900","https://openalex.org/W2786559811","https://openalex.org/W2792046648","https://openalex.org/W2807831972","https://openalex.org/W2979509742","https://openalex.org/W2990917850","https://openalex.org/W2999491213","https://openalex.org/W3041133507","https://openalex.org/W3215693540","https://openalex.org/W4200240729","https://openalex.org/W4221138878","https://openalex.org/W4226020959","https://openalex.org/W4286250636","https://openalex.org/W4303427409","https://openalex.org/W4316664875","https://openalex.org/W4321793448","https://openalex.org/W4394876220","https://openalex.org/W4399026743"],"related_works":[],"abstract_inverted_index":{"Data-driven":[0],"approaches":[1],"have":[2],"been":[3],"extensively":[4],"utilized":[5],"in":[6,143,162],"power":[7,137],"load":[8,44,73,78],"forecasting":[9,45],"to":[10,65,83,124],"enhance":[11],"predictive":[12],"performance.":[13],"Nevertheless,":[14],"when":[15],"electricity":[16,89],"retailers":[17],"integrate":[18],"new":[19],"consumers,":[20],"the":[21,29,77,96,100,103,106,152],"absence":[22],"of":[23,31,87,108],"historical":[24],"consumption":[25,138],"data":[26,139],"often":[27],"restricts":[28],"effectiveness":[30],"traditional":[32],"data-driven":[33],"techniques.":[34],"To":[35],"tackle":[36],"this":[37,39],"challenge,":[38],"study":[40],"introduces":[41],"a":[42,49,57,144],"short-term":[43],"approach":[46],"based":[47],"on":[48],"domain":[50,97],"adversarial":[51,93],"transfer":[52],"network.":[53],"The":[54],"model":[55,104],"adopts":[56],"Transformer":[58],"structure":[59],"as":[60,114],"its":[61],"feature":[62,101],"extraction":[63],"module":[64],"capture":[66],"dynamic":[67],"patterns":[68],"and":[69,99,119,130,158],"temporal":[70],"relationships":[71],"within":[72],"sequences.":[74],"Following":[75],"this,":[76],"predictor":[79],"leverages":[80],"these":[81],"features":[82],"generate":[84],"precise":[85],"forecasts":[86],"future":[88],"demand.":[90],"By":[91],"implementing":[92],"training":[94],"between":[95,128],"discriminator":[98],"extractor,":[102],"promotes":[105],"learning":[107],"domain-invariant":[109],"representations.":[110],"Moreover,":[111],"techniques":[112],"such":[113],"multi-kernel":[115],"maximum":[116],"mean":[117],"discrepancy":[118],"correlation":[120],"alignment":[121],"are":[122],"integrated":[123],"minimize":[125],"distribution":[126],"shifts":[127],"source":[129],"target":[131],"domains.":[132],"Validation":[133],"was":[134],"conducted":[135],"using":[136],"from":[140],"industrial":[141],"users":[142],"southern":[145],"Chinese":[146],"province.":[147],"Experimental":[148],"outcomes":[149],"indicate":[150],"that":[151],"proposed":[153],"method":[154],"attains":[155],"satisfactory":[156],"accuracy":[157],"exhibits":[159],"robust":[160],"adaptability":[161],"small-sample":[163],"scenarios.":[164]},"counts_by_year":[],"updated_date":"2026-10-14T16:39:01.820549","created_date":"2025-12-19T00:00:00"}
