{"id":"https://openalex.org/W4409249083","doi":"https://doi.org/10.1109/tnse.2025.3558193","title":"Short-Term Load Forecasting Based on Graph Convolution and Dendritic Deep Learning","display_name":"Short-Term Load Forecasting Based on Graph Convolution and Dendritic Deep Learning","publication_year":2025,"publication_date":"2025-04-08","ids":{"openalex":"https://openalex.org/W4409249083","doi":"https://doi.org/10.1109/tnse.2025.3558193"},"language":"en","primary_location":{"id":"doi:10.1109/tnse.2025.3558193","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnse.2025.3558193","pdf_url":null,"source":{"id":"https://openalex.org/S2484352698","display_name":"IEEE Transactions on Network Science and Engineering","issn_l":"2327-4697","issn":["2327-4697","2334-329X"],"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 Network Science and Engineering","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/A5100602557","display_name":"Chunyang Zhang","orcid":"https://orcid.org/0009-0007-4046-7026"},"institutions":[{"id":"https://openalex.org/I41198531","display_name":"Nanjing University of Posts and Telecommunications","ror":"https://ror.org/043bpky34","country_code":"CN","type":"education","lineage":["https://openalex.org/I41198531"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Chunyang Zhang","raw_affiliation_strings":["College of Automation &amp; College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"College of Automation &amp; College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing, China","institution_ids":["https://openalex.org/I41198531"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065219991","display_name":"Yang Yu","orcid":"https://orcid.org/0000-0002-4724-9933"},"institutions":[{"id":"https://openalex.org/I41198531","display_name":"Nanjing University of Posts and Telecommunications","ror":"https://ror.org/043bpky34","country_code":"CN","type":"education","lineage":["https://openalex.org/I41198531"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yang Yu","raw_affiliation_strings":["College of Automation &amp; College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"College of Automation &amp; College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing, China","institution_ids":["https://openalex.org/I41198531"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030436726","display_name":"Tengfei Zhang","orcid":"https://orcid.org/0000-0002-2503-7024"},"institutions":[{"id":"https://openalex.org/I41198531","display_name":"Nanjing University of Posts and Telecommunications","ror":"https://ror.org/043bpky34","country_code":"CN","type":"education","lineage":["https://openalex.org/I41198531"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tengfei Zhang","raw_affiliation_strings":["College of Automation &amp; College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"College of Automation &amp; College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing, China","institution_ids":["https://openalex.org/I41198531"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048807636","display_name":"Keyu Song","orcid":"https://orcid.org/0009-0008-1385-102X"},"institutions":[{"id":"https://openalex.org/I41198531","display_name":"Nanjing University of Posts and Telecommunications","ror":"https://ror.org/043bpky34","country_code":"CN","type":"education","lineage":["https://openalex.org/I41198531"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Keyu Song","raw_affiliation_strings":["College of Automation &amp; College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"College of Automation &amp; College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing, China","institution_ids":["https://openalex.org/I41198531"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006282634","display_name":"Yirui Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I109935558","display_name":"Ningbo University","ror":"https://ror.org/03et85d35","country_code":"CN","type":"education","lineage":["https://openalex.org/I109935558"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yirui Wang","raw_affiliation_strings":["Faculty of Electrical Engineering and Computer Science, Zhejiang Key Laboratory of Mobile Network Application Technology, Ningbo University, Zhejiang, China"],"affiliations":[{"raw_affiliation_string":"Faculty of Electrical Engineering and Computer Science, Zhejiang Key Laboratory of Mobile Network Application Technology, Ningbo University, Zhejiang, China","institution_ids":["https://openalex.org/I109935558"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010245958","display_name":"Shangce Gao","orcid":"https://orcid.org/0000-0001-5042-3261"},"institutions":[{"id":"https://openalex.org/I42766147","display_name":"University of Toyama","ror":"https://ror.org/0445phv87","country_code":"JP","type":"education","lineage":["https://openalex.org/I42766147"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Shangce Gao","raw_affiliation_strings":["Faculty of Engineering, University of Toyama, Toyama-shi, Japan"],"affiliations":[{"raw_affiliation_string":"Faculty of Engineering, University of Toyama, Toyama-shi, Japan","institution_ids":["https://openalex.org/I42766147"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5100602557"],"corresponding_institution_ids":["https://openalex.org/I41198531"],"apc_list":null,"apc_paid":null,"fwci":2.4834,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.86927644,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":96},"biblio":{"volume":"12","issue":"4","first_page":"3221","last_page":"3233"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T14392","display_name":"Geoscience and Mining Technology","score":0.9706000089645386,"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"}},"topics":[{"id":"https://openalex.org/T14392","display_name":"Geoscience and Mining Technology","score":0.9706000089645386,"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"}},{"id":"https://openalex.org/T13734","display_name":"Advanced Computational Techniques and Applications","score":0.9584000110626221,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12451","display_name":"Smart Grid and Power Systems","score":0.9538000226020813,"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/convolution","display_name":"Convolution (computer science)","score":0.633350670337677},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.6157769560813904},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6087746620178223},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5306153893470764},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4790290892124176},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.39685043692588806},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.21448519825935364},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.1842738389968872},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.14369001984596252}],"concepts":[{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.633350670337677},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.6157769560813904},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6087746620178223},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5306153893470764},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4790290892124176},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39685043692588806},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.21448519825935364},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.1842738389968872},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.14369001984596252},{"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/tnse.2025.3558193","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnse.2025.3558193","pdf_url":null,"source":{"id":"https://openalex.org/S2484352698","display_name":"IEEE Transactions on Network Science and Engineering","issn_l":"2327-4697","issn":["2327-4697","2334-329X"],"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 Network Science and Engineering","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7400000095367432,"id":"https://metadata.un.org/sdg/13","display_name":"Climate action"}],"awards":[{"id":"https://openalex.org/G4988898295","display_name":null,"funder_award_id":"JP25K03179","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"},{"id":"https://openalex.org/G8132733043","display_name":null,"funder_award_id":"62203238","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8710939160","display_name":null,"funder_award_id":"62073173","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"},{"id":"https://openalex.org/F4320334764","display_name":"Japan Society for the Promotion of Science","ror":"https://ror.org/00hhkn466"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":44,"referenced_works":["https://openalex.org/W1498436455","https://openalex.org/W1792666317","https://openalex.org/W1967301099","https://openalex.org/W1977686954","https://openalex.org/W2053944611","https://openalex.org/W2064675550","https://openalex.org/W2323256195","https://openalex.org/W2402395425","https://openalex.org/W2561339368","https://openalex.org/W2585123518","https://openalex.org/W2740570963","https://openalex.org/W2754252319","https://openalex.org/W2829536470","https://openalex.org/W2904458925","https://openalex.org/W2923573337","https://openalex.org/W2946084162","https://openalex.org/W2948490758","https://openalex.org/W2985107936","https://openalex.org/W2998412226","https://openalex.org/W3012947152","https://openalex.org/W3045004532","https://openalex.org/W3133618741","https://openalex.org/W3142394151","https://openalex.org/W3152577255","https://openalex.org/W3183654863","https://openalex.org/W3205959825","https://openalex.org/W3213302843","https://openalex.org/W3217665937","https://openalex.org/W4210763500","https://openalex.org/W4220662822","https://openalex.org/W4226020959","https://openalex.org/W4229334887","https://openalex.org/W4289528755","https://openalex.org/W4293799812","https://openalex.org/W4384133867","https://openalex.org/W4384701150","https://openalex.org/W4385288339","https://openalex.org/W4387501656","https://openalex.org/W4392461990","https://openalex.org/W4393857938","https://openalex.org/W4401598821","https://openalex.org/W4401671610","https://openalex.org/W6699590210","https://openalex.org/W6793710795"],"related_works":["https://openalex.org/W2731899572","https://openalex.org/W3215138031","https://openalex.org/W3009238340","https://openalex.org/W4360585206","https://openalex.org/W4321369474","https://openalex.org/W4285208911","https://openalex.org/W3082895349","https://openalex.org/W4213079790","https://openalex.org/W2248239756","https://openalex.org/W3086377361"],"abstract_inverted_index":{"Short-term":[0],"load":[1,49,82,88,220],"forecasting":[2],"(STLF)":[3],"is":[4,90,103,123,144],"a":[5,60,112,135,140,185],"significant":[6,186],"task":[7],"to":[8,24,29,42,79,105,126,149,158,191],"the":[9,25,43,86,107,116,128,132,147,160,163,192],"planning,":[10],"operation":[11],"and":[12,33,52,74,166,178,205,215,228],"control":[13],"of":[14,21,35,131,139,162,203,208,213,225],"future":[15],"power":[16],"systems.":[17],"The":[18,182],"increasing":[19],"number":[20],"devices":[22],"connected":[23,142],"system":[26],"has":[27,38],"led":[28],"more":[30,83],"complex":[31],"characteristics":[32],"forms":[34],"load,":[36],"which":[37],"brought":[39],"considerable":[40],"difficulties":[41],"relevant":[44],"methods":[45],"in":[46,153,188],"achieving":[47],"higher":[48],"prediction":[50,189],"accuracy":[51],"reliability.":[53],"In":[54],"this":[55,57],"regard,":[56],"study":[58],"proposes":[59],"deep":[61,172],"learning":[62,173],"model":[63,77,165],"that":[64],"combines":[65],"graph":[66,93],"convolutional":[67],"network":[68],"(GCN),":[69],"gated":[70],"recurrent":[71],"unit":[72],"(GRU),":[73],"dendritic":[75,136],"neural":[76],"(DNM)":[78],"forecast":[80],"electric":[81],"accurately.":[84],"Firstly,":[85],"sample":[87],"data":[89,94,151],"constructed":[91],"into":[92],"with":[95,169,195],"individual":[96],"time":[97,117],"steps":[98,118],"as":[99,146],"nodes.":[100],"A":[101,121],"GCN":[102],"used":[104,125,145],"extract":[106],"hidden":[108],"features":[109,152],"while":[110],"allowing":[111],"full":[113],"communication":[114],"between":[115],"feature":[119],"data.":[120,133],"GRU":[122],"then":[124],"capture":[127],"time-dependent":[129],"relationship":[130],"Finally,":[134],"layer":[137,143],"instead":[138],"fully":[141],"output":[148],"integrate":[150],"depth.":[154],"Experiments":[155],"are":[156],"conducted":[157],"verify":[159],"validity":[161],"proposed":[164],"compared":[167,190],"it":[168],"several":[170],"effective":[171],"models,":[174,194],"including":[175],"CNN_LSTM,":[176],"Transformer":[177],"Kolmogorov-Arnold":[179],"Networks":[180],"(KAN).":[181],"results":[183],"show":[184],"improvement":[187],"baseline":[193],"mean":[196],"absolute":[197],"percentage":[198],"error(<inline-formula":[199],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[200,210],"xmlns:xlink=\"http://www.w3.org/1999/xlink\"><tex-math":[201,211],"notation=\"LaTeX\">$MAPE$</tex-math></inline-formula>)":[202],"1.62%":[204],"3.98%,":[206],"coefficient":[207],"determination(<inline-formula":[209],"notation=\"LaTeX\">$R^{2}$</tex-math></inline-formula>)":[212],"0.983":[214],"0.928":[216],"respectively":[217],"on":[218],"two":[219],"datasets":[221],"at":[222],"different":[223],"levels":[224],"aggregation,":[226],"nationally":[227],"regionally.":[229]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-10-10T00:00:00"}
