{"id":"https://openalex.org/W3089283807","doi":"https://doi.org/10.1109/access.2020.3025811","title":"Meta Learning-Based Hybrid Ensemble Approach for Short-Term Wind Speed Forecasting","display_name":"Meta Learning-Based Hybrid Ensemble Approach for Short-Term Wind Speed Forecasting","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3089283807","doi":"https://doi.org/10.1109/access.2020.3025811","mag":"3089283807"},"language":"en","primary_location":{"id":"doi:10.1109/access.2020.3025811","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.3025811","pdf_url":null,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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://doi.org/10.1109/access.2020.3025811","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101901154","display_name":"Zhengwei Ma","orcid":"https://orcid.org/0000-0003-0229-7197"},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]},{"id":"https://openalex.org/I4210152380","display_name":"Shenzhen Technology University","ror":"https://ror.org/04qzpec27","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210152380"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhengwei Ma","raw_affiliation_strings":["College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, China","College of Urban Transportation and Logistics, Shenzhen Technology University, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, China","institution_ids":["https://openalex.org/I180726961"]},{"raw_affiliation_string":"College of Urban Transportation and Logistics, Shenzhen Technology University, Shenzhen, China","institution_ids":["https://openalex.org/I4210152380"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103006978","display_name":"Sensen Guo","orcid":"https://orcid.org/0000-0001-7588-2878"},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]},{"id":"https://openalex.org/I4210152380","display_name":"Shenzhen Technology University","ror":"https://ror.org/04qzpec27","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210152380"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sensen Guo","raw_affiliation_strings":["College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, China","College of Urban Transportation and Logistics, Shenzhen Technology University, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, China","institution_ids":["https://openalex.org/I180726961"]},{"raw_affiliation_string":"College of Urban Transportation and Logistics, Shenzhen Technology University, Shenzhen, China","institution_ids":["https://openalex.org/I4210152380"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080289760","display_name":"Gang Xu","orcid":"https://orcid.org/0000-0003-2329-9458"},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]},{"id":"https://openalex.org/I4210152380","display_name":"Shenzhen Technology University","ror":"https://ror.org/04qzpec27","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210152380"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Gang Xu","raw_affiliation_strings":["College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, China","College of Urban Transportation and Logistics, Shenzhen Technology University, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, China","institution_ids":["https://openalex.org/I180726961"]},{"raw_affiliation_string":"College of Urban Transportation and Logistics, Shenzhen Technology University, Shenzhen, China","institution_ids":["https://openalex.org/I4210152380"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5064383960","display_name":"Saddam Aziz","orcid":"https://orcid.org/0000-0002-6863-497X"},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Saddam Aziz","raw_affiliation_strings":["College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, China","institution_ids":["https://openalex.org/I180726961"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5101901154"],"corresponding_institution_ids":["https://openalex.org/I180726961","https://openalex.org/I4210152380"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":1.66,"has_fulltext":false,"cited_by_count":22,"citation_normalized_percentile":{"value":0.84300218,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":93,"max":99},"biblio":{"volume":"8","issue":null,"first_page":"172859","last_page":"172868"},"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/T10424","display_name":"Electric Power System Optimization","score":0.9858999848365784,"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/T11276","display_name":"Solar Radiation and Photovoltaics","score":0.9840999841690063,"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/wind-speed","display_name":"Wind speed","score":0.7427312731742859},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.6900428533554077},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.685679018497467},{"id":"https://openalex.org/keywords/ensemble-forecasting","display_name":"Ensemble forecasting","score":0.6661657691001892},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.6226365566253662},{"id":"https://openalex.org/keywords/wind-power","display_name":"Wind power","score":0.6171048283576965},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.603104829788208},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5327038168907166},{"id":"https://openalex.org/keywords/meta-learning","display_name":"Meta learning (computer science)","score":0.4959176480770111},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.478807270526886},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4710374176502228},{"id":"https://openalex.org/keywords/renewable-energy","display_name":"Renewable energy","score":0.4286465346813202},{"id":"https://openalex.org/keywords/wind-power-forecasting","display_name":"Wind power forecasting","score":0.42352229356765747},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.41802671551704407},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.27382275462150574},{"id":"https://openalex.org/keywords/electric-power-system","display_name":"Electric power system","score":0.24324673414230347},{"id":"https://openalex.org/keywords/meteorology","display_name":"Meteorology","score":0.18176773190498352},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1218104362487793}],"concepts":[{"id":"https://openalex.org/C161067210","wikidata":"https://www.wikidata.org/wiki/Q1464943","display_name":"Wind speed","level":2,"score":0.7427312731742859},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.6900428533554077},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.685679018497467},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.6661657691001892},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.6226365566253662},{"id":"https://openalex.org/C78600449","wikidata":"https://www.wikidata.org/wiki/Q43302","display_name":"Wind power","level":2,"score":0.6171048283576965},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.603104829788208},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5327038168907166},{"id":"https://openalex.org/C2781002164","wikidata":"https://www.wikidata.org/wiki/Q6822311","display_name":"Meta learning (computer science)","level":3,"score":0.4959176480770111},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.478807270526886},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4710374176502228},{"id":"https://openalex.org/C188573790","wikidata":"https://www.wikidata.org/wiki/Q12705","display_name":"Renewable energy","level":2,"score":0.4286465346813202},{"id":"https://openalex.org/C2781084341","wikidata":"https://www.wikidata.org/wiki/Q2583670","display_name":"Wind power forecasting","level":4,"score":0.42352229356765747},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.41802671551704407},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.27382275462150574},{"id":"https://openalex.org/C89227174","wikidata":"https://www.wikidata.org/wiki/Q2388981","display_name":"Electric power system","level":3,"score":0.24324673414230347},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.18176773190498352},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1218104362487793},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","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},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"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:10.1109/access.2020.3025811","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.3025811","pdf_url":null,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:55f25377c8dd48bda94544cc4a0e0035","is_oa":true,"landing_page_url":"https://doaj.org/article/55f25377c8dd48bda94544cc4a0e0035","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-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 8, Pp 172859-172868 (2020)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2020.3025811","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.3025811","pdf_url":null,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.8399999737739563,"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7"}],"awards":[{"id":"https://openalex.org/G2264215648","display_name":null,"funder_award_id":"52002251","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2603347797","display_name":null,"funder_award_id":"52075345","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5909336704","display_name":null,"funder_award_id":"2018A030310523","funder_id":"https://openalex.org/F4320321921","funder_display_name":"Natural Science Foundation of Guangdong Province"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321921","display_name":"Natural Science Foundation of Guangdong Province","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W588468042","https://openalex.org/W1977587074","https://openalex.org/W1984061847","https://openalex.org/W1993642620","https://openalex.org/W2021826571","https://openalex.org/W2025037893","https://openalex.org/W2048857692","https://openalex.org/W2066341465","https://openalex.org/W2067543652","https://openalex.org/W2070795314","https://openalex.org/W2070960910","https://openalex.org/W2074715647","https://openalex.org/W2090449585","https://openalex.org/W2102303403","https://openalex.org/W2142809749","https://openalex.org/W2149906128","https://openalex.org/W2254535312","https://openalex.org/W2500765456","https://openalex.org/W2511683089","https://openalex.org/W2560370080","https://openalex.org/W2581822685","https://openalex.org/W2606522347","https://openalex.org/W2821843609","https://openalex.org/W2884380318","https://openalex.org/W2897029086","https://openalex.org/W2901078150","https://openalex.org/W2910279921","https://openalex.org/W2920974156","https://openalex.org/W2953222912","https://openalex.org/W2960560113","https://openalex.org/W2963182713","https://openalex.org/W2985419578","https://openalex.org/W2989725176","https://openalex.org/W2997147307","https://openalex.org/W2999319845","https://openalex.org/W3000471799","https://openalex.org/W3001563951","https://openalex.org/W3006315601","https://openalex.org/W3009884378","https://openalex.org/W3015799856","https://openalex.org/W3021727993","https://openalex.org/W3140168516"],"related_works":["https://openalex.org/W2794896638","https://openalex.org/W2891633941","https://openalex.org/W3202800081","https://openalex.org/W3201464374","https://openalex.org/W3101614107","https://openalex.org/W1909207154","https://openalex.org/W4390971112","https://openalex.org/W3036530763","https://openalex.org/W3124390867","https://openalex.org/W1790870804"],"abstract_inverted_index":{"Under":[0],"raising":[1],"pressure":[2],"of":[3,19,30,53,77,113,150,153,202,214,224,261],"global":[4],"energy":[5,23],"and":[6,33,37,51,67,81,94,130,178,229,266],"environmental":[7,180],"issues":[8],"in":[9,47,166,252,259],"recent":[10],"years,":[11],"wind":[12,39,54,138,161],"power":[13],"has":[14,255],"been":[15],"considered":[16],"as":[17,183],"one":[18],"the":[20,48,107,154,167,192,199,203,211,215,237,244],"most":[21],"promising":[22],"sources":[24],"owing":[25],"to":[26,141,173,186,197],"with":[27,102,222],"its":[28],"advantages":[29],"being":[31],"renewable":[32],"pollution-free.":[34],"The":[35,72,85,137,164,240],"accurate":[36],"efficient":[38],"speed":[40,139,162],"forecasting":[41,200,257],"(WSF)":[42],"plays":[43],"a":[44,59,92,175],"key":[45],"role":[46],"generation,":[49],"distribution,":[50],"management":[52],"power.":[55],"This":[56],"study":[57,254],"proposes":[58],"meta":[60,78,86,245],"learning":[61,79,87,246],"based":[62,90,118,158,247],"novel":[63],"hybrid":[64,233],"ensemble":[65,73,155,169,205,217,248],"approach":[66,207],"model":[68,75,157,171,219,235,250],"for":[69,190,208],"short-term":[70,123,209],"WSF.":[71],"prediction":[74,156,206,212,218,227,234,249,262,264],"consists":[76],"part":[80,88,110],"individual":[82,108,116,193],"predictor":[83,109],"part.":[84],"is":[89,111,172],"on":[91,119,159],"multi-input":[93],"multi-output":[95],"back":[96],"propagation":[97],"(BP)":[98],"neural":[99,127],"network":[100,128],"(NN)":[101],"multiple":[103],"hidden":[104],"layers,":[105],"whereas":[106],"composed":[112],"three":[114],"pre-trained":[115],"predictors":[117],"BP":[120,176],"NN,":[121],"long":[122],"memory":[124],"(LSTM)":[125],"recurrent":[126,132],"(RNN),":[129],"gated":[131],"units":[133],"(GRU)":[134],"RNN,":[135],"respectively.":[136],"value":[140],"be":[142,145],"predicted":[143],"can":[144],"obtained":[146],"by":[147],"weighted":[148],"summation":[149],"two":[151],"parts":[152],"historical":[160],"data.":[163],"innovation":[165],"proposed":[168,204,216,251],"WSF":[170,271],"build":[174],"NN":[177],"use":[179],"feature":[181],"data":[182,185,267],"input":[184],"generate":[187],"weight":[188],"coefficients":[189],"updating":[191],"predictor.":[194],"In":[195],"order":[196],"illustrate":[198,242],"performance":[201,258],"WSF,":[210],"results":[213,241],"are":[220],"compared":[221],"those":[223],"several":[225],"single":[226],"models":[228],"an":[230],"average":[231],"coefficient":[232],"under":[236],"same":[238],"conditions.":[239],"that":[243],"this":[253],"better":[256],"both":[260],"accuracy,":[263],"stability,":[265],"correlation":[268],"than":[269],"other":[270],"models.":[272]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":2}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}