{"id":"https://openalex.org/W4365790152","doi":"https://doi.org/10.1109/access.2023.3266783","title":"Construction and Application of Short-Term and Mid-Term Power System Load Forecasting Model Based on Hybrid Deep Learning","display_name":"Construction and Application of Short-Term and Mid-Term Power System Load Forecasting Model Based on Hybrid Deep Learning","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4365790152","doi":"https://doi.org/10.1109/access.2023.3266783"},"language":"en","primary_location":{"id":"doi:10.1109/access.2023.3266783","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3266783","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10101801.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10101801.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5021827637","display_name":"Hongsheng Xu","orcid":"https://orcid.org/0000-0002-0749-0271"},"institutions":[{"id":"https://openalex.org/I4210117164","display_name":"Luoyang Normal University","ror":"https://ror.org/029man787","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210117164"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Hongsheng Xu","raw_affiliation_strings":["College of Electronic Commerce, Luoyang Normal University, Luoyang, Henan, China","Henan key Laboratory for Big Data Processing & Analytics of Electronic Commerce, Luoyang Normal University, Luoyang, Henan, China"],"raw_orcid":"https://orcid.org/0000-0002-0749-0271","affiliations":[{"raw_affiliation_string":"College of Electronic Commerce, Luoyang Normal University, Luoyang, Henan, China","institution_ids":["https://openalex.org/I4210117164"]},{"raw_affiliation_string":"Henan key Laboratory for Big Data Processing & Analytics of Electronic Commerce, Luoyang Normal University, Luoyang, Henan, China","institution_ids":["https://openalex.org/I4210117164"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073461762","display_name":"Ganglong Fan","orcid":null},"institutions":[{"id":"https://openalex.org/I4210117164","display_name":"Luoyang Normal University","ror":"https://ror.org/029man787","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210117164"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ganglong Fan","raw_affiliation_strings":["College of Electronic Commerce, Luoyang Normal University, Luoyang, Henan, China","Henan key Laboratory for Big Data Processing & Analytics of Electronic Commerce, Luoyang Normal University, Luoyang, Henan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Electronic Commerce, Luoyang Normal University, Luoyang, Henan, China","institution_ids":["https://openalex.org/I4210117164"]},{"raw_affiliation_string":"Henan key Laboratory for Big Data Processing & Analytics of Electronic Commerce, Luoyang Normal University, Luoyang, Henan, China","institution_ids":["https://openalex.org/I4210117164"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033439821","display_name":"Guofang Kuang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210117164","display_name":"Luoyang Normal University","ror":"https://ror.org/029man787","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210117164"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guofang Kuang","raw_affiliation_strings":["School of Information Technology, Luoyang Normal University, Luoyang, Henan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information Technology, Luoyang Normal University, Luoyang, Henan, China","institution_ids":["https://openalex.org/I4210117164"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5089933395","display_name":"Yanping Song","orcid":null},"institutions":[{"id":"https://openalex.org/I4210117164","display_name":"Luoyang Normal University","ror":"https://ror.org/029man787","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210117164"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanping Song","raw_affiliation_strings":["College of Electronic Commerce, Luoyang Normal University, Luoyang, Henan, China","Henan key Laboratory for Big Data Processing & Analytics of Electronic Commerce, Luoyang Normal University, Luoyang, Henan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Electronic Commerce, Luoyang Normal University, Luoyang, Henan, China","institution_ids":["https://openalex.org/I4210117164"]},{"raw_affiliation_string":"Henan key Laboratory for Big Data Processing & Analytics of Electronic Commerce, Luoyang Normal University, Luoyang, Henan, China","institution_ids":["https://openalex.org/I4210117164"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5021827637"],"corresponding_institution_ids":["https://openalex.org/I4210117164"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":6.3304,"has_fulltext":true,"cited_by_count":50,"citation_normalized_percentile":{"value":0.97260725,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":"11","issue":null,"first_page":"37494","last_page":"37507"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","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/T12368","display_name":"Grey System Theory Applications","score":0.9817000031471252,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T13955","display_name":"Evaluation Methods in Various Fields","score":0.9814000129699707,"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":0.7465529441833496},{"id":"https://openalex.org/keywords/electric-power-system","display_name":"Electric power system","score":0.7179264426231384},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6529561877250671},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.641431987285614},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.44916683435440063},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4116186797618866},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.4108126163482666},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.34157606959342957}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7465529441833496},{"id":"https://openalex.org/C89227174","wikidata":"https://www.wikidata.org/wiki/Q2388981","display_name":"Electric power system","level":3,"score":0.7179264426231384},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6529561877250671},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.641431987285614},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44916683435440063},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4116186797618866},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.4108126163482666},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34157606959342957},{"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},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2023.3266783","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3266783","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10101801.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:8a9db799f882491785e64b84d4c1771b","is_oa":true,"landing_page_url":"https://doaj.org/article/8a9db799f882491785e64b84d4c1771b","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,"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":"IEEE Access, Vol 11, Pp 37494-37507 (2023)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2023.3266783","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3266783","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10101801.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.5299999713897705,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[{"id":"https://openalex.org/G7572044835","display_name":null,"funder_award_id":"61272015","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":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4365790152.pdf","grobid_xml":"https://content.openalex.org/works/W4365790152.grobid-xml"},"referenced_works_count":24,"referenced_works":["https://openalex.org/W2032617530","https://openalex.org/W2597560131","https://openalex.org/W2754252319","https://openalex.org/W2805797750","https://openalex.org/W2894793845","https://openalex.org/W2922329508","https://openalex.org/W2949891635","https://openalex.org/W3033406500","https://openalex.org/W3034726856","https://openalex.org/W3090661556","https://openalex.org/W3108087516","https://openalex.org/W3109239204","https://openalex.org/W3122860865","https://openalex.org/W3129762955","https://openalex.org/W3135794967","https://openalex.org/W3140593863","https://openalex.org/W3155772937","https://openalex.org/W3157294059","https://openalex.org/W3181520670","https://openalex.org/W3215693540","https://openalex.org/W4220757726","https://openalex.org/W4226263724","https://openalex.org/W4293150140","https://openalex.org/W4312998103"],"related_works":["https://openalex.org/W2378211422","https://openalex.org/W4321353415","https://openalex.org/W2745001401","https://openalex.org/W2130974462","https://openalex.org/W2028665553","https://openalex.org/W2086519370","https://openalex.org/W4246352526","https://openalex.org/W2121910908","https://openalex.org/W915438175","https://openalex.org/W2345517883"],"abstract_inverted_index":{"Power":[0],"system":[1,14,46,79,137,188,242],"load":[2,22,47,80,122,138,149,189,243],"forecasting":[3,23,60,81,139,190],"model":[4,140,169,191,218,234],"plays":[5],"an":[6,25],"important":[7,26],"role":[8],"in":[9,57],"all":[10],"aspects":[11],"of":[12,32,44,74,111,124,182,215,240],"power":[13,21,34,45,78,127,136,187,241],"planning,":[15],"operation":[16,31],"and":[17,49,76,129,132,157,175],"control.":[18],"Therefore,":[19],"accurate":[20],"provides":[24],"guarantee":[27],"for":[28,67,205],"the":[29,33,41,58,89,98,115,120,180,183,200,213,216,225,231,238],"stable":[30],"grid":[35,128],"system.":[36],"This":[37,117,228],"paper":[38,71,94,118,152],"first":[39,153],"analyzes":[40,119],"current":[42],"status":[43],"forecasting,":[48,150],"finds":[50],"that":[51,212,230],"there":[52],"are":[53,203],"still":[54],"some":[55],"deficiencies":[56],"existing":[59],"models.":[61,227],"In":[62,88],"order":[63],"to":[64,96,104,161],"make":[65],"up":[66],"these":[68],"shortcomings,":[69],"this":[70,93,151],"proposes":[72,95,133],"construction":[73],"short-term":[75,135],"mid-term":[77,148,186],"models":[82,202],"based":[83,141,172,192],"on":[84,142,173,193],"hybrid":[85,166,194,233],"deep":[86,167,195],"learning.":[87],"data":[90,123],"preprocessing":[91],"part,":[92],"use":[97],"exponential":[99],"weight":[100],"moving":[101],"average":[102],"method":[103,110],"process":[105],"missing":[106],"values.":[107],"The":[108,208],"detection":[109],"abnormal":[112],"value":[113],"is":[114,170,197,219,222],"GeneralizedESDTestAD(GESD).":[116],"historical":[121],"a":[125,134,165,185],"regional":[126],"four":[130],"industries,":[131],"Bi-directional":[143],"Long":[144],"Short-Term":[145],"Memory(BiLSTM);":[146],"For":[147],"uses":[154],"random":[155,176],"forest":[156],"Pearson":[158],"correlation":[159],"coefficient":[160],"select":[162],"features.":[163],"Then":[164],"learning":[168,196],"constructed":[171],"BiLSTM":[174],"forest.":[177],"After":[178],"optimizing":[179],"parameters":[181],"model,":[184],"constructed.":[198],"Finally,":[199],"benchmark":[201,226],"selected":[204],"comparative":[206],"experiments.":[207],"experimental":[209],"results":[210],"show":[211],"MAPE":[214],"proposed":[217,232],"2.36%,":[220],"which":[221],"better":[223],"than":[224],"proves":[229],"can":[235],"effectively":[236],"improve":[237],"accuracy":[239],"forecasting.":[244]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":25},{"year":2024,"cited_by_count":14},{"year":2023,"cited_by_count":9}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-10T00:00:00"}
