{"id":"https://openalex.org/W3197161284","doi":"https://doi.org/10.1109/tnnls.2021.3106968","title":"Combination of Manifold Learning and Deep Learning Algorithms for Mid-Term Electrical Load Forecasting","display_name":"Combination of Manifold Learning and Deep Learning Algorithms for Mid-Term Electrical Load Forecasting","publication_year":2021,"publication_date":"2021-09-03","ids":{"openalex":"https://openalex.org/W3197161284","doi":"https://doi.org/10.1109/tnnls.2021.3106968","mag":"3197161284","pmid":"https://pubmed.ncbi.nlm.nih.gov/34478386"},"language":"en","primary_location":{"id":"doi:10.1109/tnnls.2021.3106968","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2021.3106968","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"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 Neural Networks and Learning Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5031436368","display_name":"Jinghua Li","orcid":"https://orcid.org/0000-0003-0395-9578"},"institutions":[{"id":"https://openalex.org/I150807315","display_name":"Guangxi University","ror":"https://ror.org/02c9qn167","country_code":"CN","type":"education","lineage":["https://openalex.org/I150807315"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinghua Li","raw_affiliation_strings":["Department of Electrical Engineering, Guangxi University, Nanning, China"],"raw_orcid":"https://orcid.org/0000-0003-0395-9578","affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Guangxi University, Nanning, China","institution_ids":["https://openalex.org/I150807315"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Shanyang Wei","orcid":"https://orcid.org/0000-0002-1162-2353"},"institutions":[{"id":"https://openalex.org/I150807315","display_name":"Guangxi University","ror":"https://ror.org/02c9qn167","country_code":"CN","type":"education","lineage":["https://openalex.org/I150807315"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shanyang Wei","raw_affiliation_strings":["Department of Electrical Engineering, Guangxi University, Nanning, China"],"raw_orcid":"https://orcid.org/0000-0002-1162-2353","affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Guangxi University, Nanning, China","institution_ids":["https://openalex.org/I150807315"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5028924208","display_name":"Wei Dai","orcid":"https://orcid.org/0000-0001-6815-6536"},"institutions":[{"id":"https://openalex.org/I150807315","display_name":"Guangxi University","ror":"https://ror.org/02c9qn167","country_code":"CN","type":"education","lineage":["https://openalex.org/I150807315"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Dai","raw_affiliation_strings":["Department of Electrical Engineering, Guangxi University, Nanning, China"],"raw_orcid":"https://orcid.org/0000-0001-6815-6536","affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Guangxi University, Nanning, China","institution_ids":["https://openalex.org/I150807315"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I150807315"],"apc_list":null,"apc_paid":null,"fwci":2.8102,"has_fulltext":false,"cited_by_count":53,"citation_normalized_percentile":{"value":0.91265568,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":"34","issue":"5","first_page":"2584","last_page":"2593"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":1.0,"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":1.0,"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":0.9951000213623047,"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/T10320","display_name":"Neural Networks and Applications","score":0.9850999712944031,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.7077952027320862},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6279400587081909},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.6099712252616882},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.6036968231201172},{"id":"https://openalex.org/keywords/nonlinear-dimensionality-reduction","display_name":"Nonlinear dimensionality reduction","score":0.5968671441078186},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5858813524246216},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.5522371530532837},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5189940333366394},{"id":"https://openalex.org/keywords/electric-power-system","display_name":"Electric power system","score":0.509891152381897},{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.5055067539215088},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4717448651790619},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.44571471214294434},{"id":"https://openalex.org/keywords/manifold","display_name":"Manifold (fluid mechanics)","score":0.42479830980300903},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4124513566493988},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.4049561619758606},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.136152446269989}],"concepts":[{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.7077952027320862},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6279400587081909},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.6099712252616882},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.6036968231201172},{"id":"https://openalex.org/C151876577","wikidata":"https://www.wikidata.org/wiki/Q7049464","display_name":"Nonlinear dimensionality reduction","level":3,"score":0.5968671441078186},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5858813524246216},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.5522371530532837},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5189940333366394},{"id":"https://openalex.org/C89227174","wikidata":"https://www.wikidata.org/wiki/Q2388981","display_name":"Electric power system","level":3,"score":0.509891152381897},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.5055067539215088},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4717448651790619},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.44571471214294434},{"id":"https://openalex.org/C529865628","wikidata":"https://www.wikidata.org/wiki/Q1790740","display_name":"Manifold (fluid mechanics)","level":2,"score":0.42479830980300903},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4124513566493988},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.4049561619758606},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.136152446269989},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","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/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tnnls.2021.3106968","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2021.3106968","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"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 Neural Networks and Learning Systems","raw_type":"journal-article"},{"id":"pmid:34478386","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/34478386","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on neural networks and learning systems","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.4399999976158142,"display_name":"Industry, innovation and infrastructure"}],"awards":[{"id":"https://openalex.org/G1992408013","display_name":null,"funder_award_id":"51977042","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":35,"referenced_works":["https://openalex.org/W244768627","https://openalex.org/W1578196132","https://openalex.org/W1908962573","https://openalex.org/W2001141328","https://openalex.org/W2013051270","https://openalex.org/W2026440431","https://openalex.org/W2053186076","https://openalex.org/W2097308346","https://openalex.org/W2122530359","https://openalex.org/W2145732764","https://openalex.org/W2146588145","https://openalex.org/W2152271106","https://openalex.org/W2163922914","https://openalex.org/W2171309505","https://openalex.org/W2342792901","https://openalex.org/W2466975708","https://openalex.org/W2509518867","https://openalex.org/W2516939159","https://openalex.org/W2521449822","https://openalex.org/W2547585091","https://openalex.org/W2754141826","https://openalex.org/W2764289073","https://openalex.org/W2889386826","https://openalex.org/W2889480665","https://openalex.org/W2895418405","https://openalex.org/W2968831808","https://openalex.org/W2972862002","https://openalex.org/W2996412301","https://openalex.org/W3004860350","https://openalex.org/W3025194661","https://openalex.org/W3046994390","https://openalex.org/W3081331352","https://openalex.org/W3120283405","https://openalex.org/W6751792830","https://openalex.org/W6788036346"],"related_works":["https://openalex.org/W2375574759","https://openalex.org/W3088634662","https://openalex.org/W3162910294","https://openalex.org/W2539700568","https://openalex.org/W1995622179","https://openalex.org/W1484111231","https://openalex.org/W2383239174","https://openalex.org/W117517268","https://openalex.org/W2166963679","https://openalex.org/W2573981081"],"abstract_inverted_index":{"Mid-term":[0],"load":[1,20,75,107,148],"forecasting":[2,123,149],"(MTLF)":[3],"is":[4,21,43,103,137,157],"of":[5,26,48,56,74,82],"great":[6],"significance":[7],"for":[8,106],"power":[9,14],"system":[10,141],"planning,":[11],"operation,":[12],"and":[13,29,84,102,147,153],"trading.":[15],"However,":[16],"the":[17,24,46,71,80,87,126,165,177],"mid-term":[18,178],"electrical":[19],"affected":[22],"by":[23,130],"coupling":[25],"multiple":[27],"factors":[28,73],"demonstrates":[30],"complex":[31],"characteristics,":[32],"which":[33,68],"leads":[34],"to":[35,52,77,121],"low":[36],"prediction":[37,170],"accuracy":[38,81,171],"in":[39,125,150,176],"MTLF.":[40],"Furthermore,":[41,112],"MTLF":[42,62,83,135],"faced":[44],"with":[45,109],"\"curse":[47],"dimensionality\"":[49],"problem":[50],"due":[51],"a":[53],"large":[54],"number":[55],"variables.":[57],"This":[58],"article":[59],"proposes":[60],"an":[61],"method":[63,136,167],"based":[64],"on":[65,139],"manifold":[66,94,131],"learning,":[67],"can":[69],"extract":[70],"underlying":[72],"variations":[76],"help":[78],"improve":[79],"significantly":[85],"reduce":[86],"calculation.":[88],"Unlike":[89],"linear":[90],"dimensionality":[91],"reduction":[92],"methods,":[93],"learning":[95],"has":[96,168],"better":[97],"nonlinear":[98,110],"feature":[99],"extraction":[100],"ability":[101],"more":[104],"suitable":[105],"data":[108],"characteristics.":[111],"long":[113],"short-term":[114],"memory":[115],"(LSTM)":[116],"neural":[117],"networks":[118],"are":[119],"used":[120],"establish":[122],"models":[124],"low-dimensional":[127],"space":[128],"obtained":[129],"learning.":[132],"The":[133,160],"proposed":[134,166],"tested":[138],"independent":[140],"operator":[142],"(ISO)":[143],"New":[144],"England":[145],"datasets,":[146],"24,":[151],"168,":[152],"720":[154],"h":[155],"ahead":[156],"carried":[158],"out.":[159],"numerical":[161],"results":[162],"validate":[163],"that":[164],"higher":[169],"than":[172],"many":[173],"mature":[174],"methods":[175],"time":[179],"scale.":[180]},"counts_by_year":[{"year":2026,"cited_by_count":6},{"year":2025,"cited_by_count":12},{"year":2024,"cited_by_count":16},{"year":2023,"cited_by_count":12},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":1}],"updated_date":"2026-07-21T08:15:58.654021","created_date":"2025-10-10T00:00:00"}