{"id":"https://openalex.org/W4388827632","doi":"https://doi.org/10.1007/s41019-023-00233-8","title":"Graph Neural Network-Based Short\u2011Term Load Forecasting with Temporal Convolution","display_name":"Graph Neural Network-Based Short\u2011Term Load Forecasting with Temporal Convolution","publication_year":2023,"publication_date":"2023-11-20","ids":{"openalex":"https://openalex.org/W4388827632","doi":"https://doi.org/10.1007/s41019-023-00233-8"},"language":"en","primary_location":{"id":"doi:10.1007/s41019-023-00233-8","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s41019-023-00233-8","pdf_url":"https://link.springer.com/content/pdf/10.1007/s41019-023-00233-8.pdf","source":{"id":"https://openalex.org/S2486411021","display_name":"Data Science and Engineering","issn_l":"2364-1185","issn":["2364-1185","2364-1541"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Data Science and Engineering","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://link.springer.com/content/pdf/10.1007/s41019-023-00233-8.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100771483","display_name":"Chenchen Sun","orcid":"https://orcid.org/0000-0002-9990-0425"},"institutions":[{"id":"https://openalex.org/I136765683","display_name":"Tianjin University of Technology","ror":"https://ror.org/00zbe0w13","country_code":"CN","type":"education","lineage":["https://openalex.org/I136765683"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Chenchen Sun","raw_affiliation_strings":["School of Computer Science and Engineering, Tianjin University of Technology, Tianjin, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Tianjin University of Technology, Tianjin, China","institution_ids":["https://openalex.org/I136765683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101890829","display_name":"Ning Yan","orcid":"https://orcid.org/0000-0003-3371-1709"},"institutions":[{"id":"https://openalex.org/I136765683","display_name":"Tianjin University of Technology","ror":"https://ror.org/00zbe0w13","country_code":"CN","type":"education","lineage":["https://openalex.org/I136765683"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yan Ning","raw_affiliation_strings":["School of Computer Science and Engineering, Tianjin University of Technology, Tianjin, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Tianjin University of Technology, Tianjin, China","institution_ids":["https://openalex.org/I136765683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015251362","display_name":"Derong Shen","orcid":"https://orcid.org/0000-0003-0310-6372"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Derong Shen","raw_affiliation_strings":["School of Computer Science and Engineering, Northeastern University, Shenyang, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Northeastern University, Shenyang, China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102979521","display_name":"Tiezheng Nie","orcid":"https://orcid.org/0000-0002-0166-1324"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tiezheng Nie","raw_affiliation_strings":["School of Computer Science and Engineering, Northeastern University, Shenyang, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Northeastern University, Shenyang, China","institution_ids":["https://openalex.org/I9224756"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100771483"],"corresponding_institution_ids":["https://openalex.org/I136765683"],"apc_list":null,"apc_paid":null,"fwci":4.3563,"has_fulltext":true,"cited_by_count":33,"citation_normalized_percentile":{"value":0.95196626,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"9","issue":"2","first_page":"113","last_page":"132"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9995999932289124,"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/T13955","display_name":"Evaluation Methods in Various Fields","score":0.9739000201225281,"subfield":{"id":"https://openalex.org/subfields/2302","display_name":"Ecological Modeling"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7875722646713257},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.7650783658027649},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.6994504928588867},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.6472815275192261},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.4929218292236328},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.45696088671684265},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4337720572948456},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4210500717163086},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4159959554672241},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.37565457820892334},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.32858771085739136},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.2957719564437866}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7875722646713257},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.7650783658027649},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.6994504928588867},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.6472815275192261},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.4929218292236328},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.45696088671684265},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4337720572948456},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4210500717163086},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4159959554672241},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.37565457820892334},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.32858771085739136},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2957719564437866},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1007/s41019-023-00233-8","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s41019-023-00233-8","pdf_url":"https://link.springer.com/content/pdf/10.1007/s41019-023-00233-8.pdf","source":{"id":"https://openalex.org/S2486411021","display_name":"Data Science and Engineering","issn_l":"2364-1185","issn":["2364-1185","2364-1541"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Data Science and Engineering","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:2614dcb9549340c892512dd372cf00eb","is_oa":true,"landing_page_url":"https://doaj.org/article/2614dcb9549340c892512dd372cf00eb","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Data Science and Engineering, Vol 9, Iss 2, Pp 113-132 (2023)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1007/s41019-023-00233-8","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s41019-023-00233-8","pdf_url":"https://link.springer.com/content/pdf/10.1007/s41019-023-00233-8.pdf","source":{"id":"https://openalex.org/S2486411021","display_name":"Data Science and Engineering","issn_l":"2364-1185","issn":["2364-1185","2364-1541"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Data Science and Engineering","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Decent work and economic growth","score":0.4000000059604645,"id":"https://metadata.un.org/sdg/8"}],"awards":[{"id":"https://openalex.org/G1425812762","display_name":null,"funder_award_id":"62172082","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2087396116","display_name":null,"funder_award_id":"China","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3064489609","display_name":null,"funder_award_id":"62002262","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3317480652","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5505926156","display_name":null,"funder_award_id":"62072084","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5994120800","display_name":null,"funder_award_id":"Natural","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7932691611","display_name":null,"funder_award_id":"62072086","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":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4388827632.pdf"},"referenced_works_count":32,"referenced_works":["https://openalex.org/W1967690950","https://openalex.org/W1991414280","https://openalex.org/W2053944611","https://openalex.org/W2139073438","https://openalex.org/W2159118434","https://openalex.org/W2534287718","https://openalex.org/W2597560131","https://openalex.org/W2597866042","https://openalex.org/W2599285715","https://openalex.org/W2756203131","https://openalex.org/W2809971249","https://openalex.org/W2893798702","https://openalex.org/W2895861596","https://openalex.org/W2901504064","https://openalex.org/W2921022731","https://openalex.org/W2922329508","https://openalex.org/W2965341826","https://openalex.org/W2974449012","https://openalex.org/W2998559444","https://openalex.org/W3043685378","https://openalex.org/W3090499788","https://openalex.org/W3103720336","https://openalex.org/W3134220793","https://openalex.org/W3162100006","https://openalex.org/W3163603179","https://openalex.org/W3177318507","https://openalex.org/W3180293405","https://openalex.org/W4205322729","https://openalex.org/W4213423320","https://openalex.org/W4285122835","https://openalex.org/W4294391209","https://openalex.org/W4312789021"],"related_works":["https://openalex.org/W2560215812","https://openalex.org/W2949601986","https://openalex.org/W4293226380","https://openalex.org/W2788972299","https://openalex.org/W2521347458","https://openalex.org/W2498789492","https://openalex.org/W2729981612","https://openalex.org/W2964954556","https://openalex.org/W3019910406","https://openalex.org/W4300237897"],"abstract_inverted_index":{"Abstract":[0],"An":[1],"accurate":[2],"short-term":[3,19,70,192],"load":[4,20,71,178,193],"forecasting":[5,21,72,179],"plays":[6],"an":[7,198],"important":[8],"role":[9],"in":[10,40,138],"modern":[11],"power":[12],"system\u2019s":[13],"operation":[14],"and":[15,27,79,108,211,224],"economic":[16],"development.":[17],"However,":[18],"is":[22,43,145],"affected":[23],"by":[24,206],"multiple":[25],"factors,":[26,36],"due":[28],"to":[29,90,97,122,140,157,185,214],"the":[30,33,37,46,58,86,93,150,162,171,177,187,200,215,238],"complexity":[31],"of":[32,129,153,161,165,189,203],"relationships":[34,60,94],"between":[35,61,95],"graph":[38,76,87,102,151,167],"structure":[39],"this":[41,65],"task":[42],"unknown.":[44],"On":[45],"other":[47],"hand,":[48],"existing":[49,239],"methods":[50],"do":[51],"not":[52,146],"fully":[53],"aggregating":[54],"data":[55,128,143,163],"information":[56,144,164],"through":[57,111,176],"inherent":[59],"various":[62],"factors.":[63],"In":[64,114,132,191],"paper,":[66],"we":[67],"propose":[68],"a":[69],"framework":[73],"based":[74],"on":[75,219],"neural":[77],"networks":[78],"dilated":[80,120],"1D-CNN,":[81],"called":[82],"GLFN-TC.":[83,190],"GLFN-TC":[84,104,118,148,204,231],"uses":[85,119,149],"learning":[88],"module":[89],"automatically":[91],"learn":[92],"variables":[96],"solve":[98],"problem":[99],"with":[100],"unknown":[101],"structure.":[103],"effectively":[105],"handles":[106],"temporal":[107,115,124],"spatial":[109],"dependencies":[110,125],"two":[112],"modules.":[113],"convolution":[116,136,152,168],"module,":[117,137],"1D-CNN":[121],"extract":[123],"from":[126],"historical":[127],"each":[130,166],"node.":[131],"densely":[133,154],"connected":[134,155],"residual":[135,156],"order":[139],"ensure":[141],"that":[142,230],"lost,":[147],"make":[158],"full":[159],"use":[160],"layer.":[169],"Finally,":[170],"predicted":[172],"values":[173],"are":[174],"obtained":[175],"module.":[180],"We":[181],"conducted":[182],"five":[183],"studies":[184],"verify":[186],"outperformance":[188],"forecasting,":[194],"using":[195],"MSE":[196],"as":[197],"example,":[199],"experimental":[201],"results":[202],"decreased":[205],"0.0396,":[207],"0.0137,":[208],"0.0358,":[209],"0.0213":[210],"0.0337":[212],"compared":[213],"optimal":[216],"baseline":[217],"method":[218],"ISO-NE,":[220],"AT,":[221],"AP,":[222],"SH":[223],"NCENT":[225],"datasets,":[226],"respectively.":[227],"Results":[228],"show":[229],"can":[232],"achieve":[233],"higher":[234],"prediction":[235],"accuracy":[236],"than":[237],"common":[240],"methods.":[241]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":18},{"year":2024,"cited_by_count":10}],"updated_date":"2026-03-18T14:38:29.013473","created_date":"2025-10-10T00:00:00"}
