{"id":"https://openalex.org/W7116752830","doi":"https://doi.org/10.1109/tsg.2025.3646664","title":"Closed-Loop Probabilistic Forecasting Method of Short-Term Spatial Load Considering Corrupted Data and Field Verification","display_name":"Closed-Loop Probabilistic Forecasting Method of Short-Term Spatial Load Considering Corrupted Data and Field Verification","publication_year":2025,"publication_date":"2025-12-22","ids":{"openalex":"https://openalex.org/W7116752830","doi":"https://doi.org/10.1109/tsg.2025.3646664"},"language":null,"primary_location":{"id":"doi:10.1109/tsg.2025.3646664","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsg.2025.3646664","pdf_url":null,"source":{"id":"https://openalex.org/S59604973","display_name":"IEEE Transactions on Smart Grid","issn_l":"1949-3053","issn":["1949-3053","1949-3061"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Smart Grid","raw_type":"journal-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/A5030283976","display_name":"Siyi Wang","orcid":"https://orcid.org/0000-0003-2527-3542"},"institutions":[{"id":"https://openalex.org/I153473198","display_name":"North China Electric Power University","ror":"https://ror.org/04qr5t414","country_code":"CN","type":"education","lineage":["https://openalex.org/I153473198"]},{"id":"https://openalex.org/I4392738113","display_name":"China Electric Power Research Institute","ror":"https://ror.org/05ehpzy81","country_code":null,"type":"facility","lineage":["https://openalex.org/I17442442","https://openalex.org/I4392738113"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Siyi Wang","raw_affiliation_strings":["China Electric Power Research Institute, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-2527-3542","affiliations":[{"raw_affiliation_string":"China Electric Power Research Institute, Beijing, China","institution_ids":["https://openalex.org/I153473198","https://openalex.org/I4392738113"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110523177","display_name":"Wanxing Sheng","orcid":null},"institutions":[{"id":"https://openalex.org/I153473198","display_name":"North China Electric Power University","ror":"https://ror.org/04qr5t414","country_code":"CN","type":"education","lineage":["https://openalex.org/I153473198"]},{"id":"https://openalex.org/I4392738113","display_name":"China Electric Power Research Institute","ror":"https://ror.org/05ehpzy81","country_code":null,"type":"facility","lineage":["https://openalex.org/I17442442","https://openalex.org/I4392738113"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wanxing Sheng","raw_affiliation_strings":["China Electric Power Research Institute, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"China Electric Power Research Institute, Beijing, China","institution_ids":["https://openalex.org/I153473198","https://openalex.org/I4392738113"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5120955266","display_name":"Yuwei Shang","orcid":null},"institutions":[{"id":"https://openalex.org/I153473198","display_name":"North China Electric Power University","ror":"https://ror.org/04qr5t414","country_code":"CN","type":"education","lineage":["https://openalex.org/I153473198"]},{"id":"https://openalex.org/I4392738113","display_name":"China Electric Power Research Institute","ror":"https://ror.org/05ehpzy81","country_code":null,"type":"facility","lineage":["https://openalex.org/I17442442","https://openalex.org/I4392738113"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuwei Shang","raw_affiliation_strings":["China Electric Power Research Institute, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-1976-1470","affiliations":[{"raw_affiliation_string":"China Electric Power Research Institute, Beijing, China","institution_ids":["https://openalex.org/I153473198","https://openalex.org/I4392738113"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017252332","display_name":"Qing Duan","orcid":"https://orcid.org/0000-0003-4467-7024"},"institutions":[{"id":"https://openalex.org/I153473198","display_name":"North China Electric Power University","ror":"https://ror.org/04qr5t414","country_code":"CN","type":"education","lineage":["https://openalex.org/I153473198"]},{"id":"https://openalex.org/I4392738113","display_name":"China Electric Power Research Institute","ror":"https://ror.org/05ehpzy81","country_code":null,"type":"facility","lineage":["https://openalex.org/I17442442","https://openalex.org/I4392738113"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qing Duan","raw_affiliation_strings":["China Electric Power Research Institute, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"China Electric Power Research Institute, Beijing, China","institution_ids":["https://openalex.org/I153473198","https://openalex.org/I4392738113"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121010536","display_name":"Haotian Ma","orcid":null},"institutions":[{"id":"https://openalex.org/I153473198","display_name":"North China Electric Power University","ror":"https://ror.org/04qr5t414","country_code":"CN","type":"education","lineage":["https://openalex.org/I153473198"]},{"id":"https://openalex.org/I4392738113","display_name":"China Electric Power Research Institute","ror":"https://ror.org/05ehpzy81","country_code":null,"type":"facility","lineage":["https://openalex.org/I17442442","https://openalex.org/I4392738113"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haotian Ma","raw_affiliation_strings":["China Electric Power Research Institute, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"China Electric Power Research Institute, Beijing, China","institution_ids":["https://openalex.org/I153473198","https://openalex.org/I4392738113"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5018256089","display_name":"Shuning Wu","orcid":null},"institutions":[{"id":"https://openalex.org/I153473198","display_name":"North China Electric Power University","ror":"https://ror.org/04qr5t414","country_code":"CN","type":"education","lineage":["https://openalex.org/I153473198"]},{"id":"https://openalex.org/I4392738113","display_name":"China Electric Power Research Institute","ror":"https://ror.org/05ehpzy81","country_code":null,"type":"facility","lineage":["https://openalex.org/I17442442","https://openalex.org/I4392738113"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuning Wu","raw_affiliation_strings":["China Electric Power Research Institute, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"China Electric Power Research Institute, Beijing, China","institution_ids":["https://openalex.org/I153473198","https://openalex.org/I4392738113"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.50492756,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"17","issue":"3","first_page":"2517","last_page":"2529"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9605000019073486,"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":0.9605000019073486,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.007199999876320362,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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/T11941","display_name":"Power System Reliability and Maintenance","score":0.003100000089034438,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"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/robustness","display_name":"Robustness (evolution)","score":0.6747000217437744},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5667999982833862},{"id":"https://openalex.org/keywords/probabilistic-forecasting","display_name":"Probabilistic forecasting","score":0.5504999756813049},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.5404000282287598},{"id":"https://openalex.org/keywords/load-profile","display_name":"Load profile","score":0.4140999913215637},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.39649999141693115},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.3898000121116638},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.38679999113082886},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.38350000977516174}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6762999892234802},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6747000217437744},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5667999982833862},{"id":"https://openalex.org/C122282355","wikidata":"https://www.wikidata.org/wiki/Q7246855","display_name":"Probabilistic forecasting","level":3,"score":0.5504999756813049},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5404000282287598},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5},{"id":"https://openalex.org/C2777908891","wikidata":"https://www.wikidata.org/wiki/Q1806775","display_name":"Load profile","level":3,"score":0.4140999913215637},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.39649999141693115},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3898000121116638},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.38679999113082886},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.38350000977516174},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.3449999988079071},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.3425999879837036},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34209999442100525},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.33180001378059387},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.3165000081062317},{"id":"https://openalex.org/C91602232","wikidata":"https://www.wikidata.org/wiki/Q756115","display_name":"Volatility (finance)","level":2,"score":0.29350000619888306},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.28610000014305115},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.28600001335144043},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.28540000319480896},{"id":"https://openalex.org/C87007009","wikidata":"https://www.wikidata.org/wiki/Q210832","display_name":"Statistical hypothesis testing","level":2,"score":0.28119999170303345},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.2802000045776367},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.2784999907016754},{"id":"https://openalex.org/C150060386","wikidata":"https://www.wikidata.org/wiki/Q7574054","display_name":"Spatial correlation","level":2,"score":0.2766000032424927},{"id":"https://openalex.org/C206729178","wikidata":"https://www.wikidata.org/wiki/Q2271896","display_name":"Scheduling (production processes)","level":2,"score":0.26669999957084656},{"id":"https://openalex.org/C115901376","wikidata":"https://www.wikidata.org/wiki/Q184199","display_name":"Automation","level":2,"score":0.26510000228881836},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2556000053882599},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.2533999979496002}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tsg.2025.3646664","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsg.2025.3646664","pdf_url":null,"source":{"id":"https://openalex.org/S59604973","display_name":"IEEE Transactions on Smart Grid","issn_l":"1949-3053","issn":["1949-3053","1949-3061"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Smart Grid","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G7192696750","display_name":null,"funder_award_id":"U23B6007","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0,93,130,155],"rapid":[1],"development":[2],"of":[3,40,45,61,97,112,118,144,182,195,204],"flexible":[4],"load":[5,13,52,89,146,193],"technologies":[6],"brings":[7],"new":[8],"challenges":[9],"to":[10,28,106,140,161,173],"short-term":[11],"spatial":[12],"forecasting":[14,72,157,184],"(SSLF)":[15],"in":[16,104,174,197],"distribution":[17],"networks":[18,127],"(DNs).":[19],"Conventionally,":[20],"as":[21],"an":[22],"open-loop":[23],"method,":[24,137],"SSLF":[25,76],"is":[26,133,138,159],"unable":[27],"dynamically":[29],"improve":[30,176],"the":[31,35,43,46,48,59,62,75,79,87,109,116,119,121,142,145,149,163,169,177,180,183,191,202,205],"forecast":[32],"results.":[33,185],"However,":[34],"nonstationary":[36],"and":[37,55,86,102,147,166,179],"volatility":[38],"features":[39],"loads":[41],"reduce":[42,58],"accuracy":[44,178],"SSLF,":[47,120],"errors":[49],"caused":[50,151],"by":[51,77,135,152,168],"data":[53,194],"loss":[54],"distortion":[56],"further":[57],"robustness":[60,181],"forecasting.":[63],"In":[64],"this":[65],"paper,":[66],"we":[67],"propose":[68],"a":[69],"closed-loop":[70,156],"probabilistic":[71],"method":[73],"for":[74],"integrating":[78],"modified":[80,94,164],"Spatio-Temporal":[81],"Graph":[82],"Convolutional":[83],"network":[84],"(ST-GCN)":[85],"statistical":[88,136],"baseline":[90],"profile":[91],"(SLBP).":[92],"ST-GCN":[95,165],"consists":[96],"GCN-Gate":[98],"Recurrent":[99],"Unit":[100],"(GCN-GRU)":[101],"GCN":[103],"parallel":[105],"simultaneously":[107],"learn":[108],"spatio-temporal":[110],"correlation":[111],"loads.":[113],"To":[114],"capture":[115],"uncertainty":[117],"concrete":[122],"dropout":[123],"enabled":[124],"Bayesian":[125],"neural":[126],"are":[128,188],"applied.":[129],"SLBP,":[131],"which":[132],"calculated":[134],"used":[139],"extract":[141],"periodicity":[143],"mitigate":[148],"impact":[150],"corrupted":[153],"data.":[154],"architecture":[158],"devised":[160],"integrate":[162],"SLBP":[167],"dynamic":[170],"clustering":[171],"technique,":[172],"turn":[175],"Numerical":[186],"tests":[187],"conducted":[189],"using":[190],"real":[192],"DNs":[196],"China.":[198],"Test":[199],"results":[200],"confirmed":[201],"superiority":[203],"proposed":[206],"method.":[207]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-12-22T00:00:00"}