{"id":"https://openalex.org/W4411186915","doi":"https://doi.org/10.1111/coin.70078","title":"Short\u2010Term Wind Speed Prediction Model Based on Hybrid Decomposition Method and Deep Learning","display_name":"Short\u2010Term Wind Speed Prediction Model Based on Hybrid Decomposition Method and Deep Learning","publication_year":2025,"publication_date":"2025-06-01","ids":{"openalex":"https://openalex.org/W4411186915","doi":"https://doi.org/10.1111/coin.70078"},"language":"en","primary_location":{"id":"doi:10.1111/coin.70078","is_oa":false,"landing_page_url":"https://doi.org/10.1111/coin.70078","pdf_url":null,"source":{"id":"https://openalex.org/S56561474","display_name":"Computational Intelligence","issn_l":"0824-7935","issn":["0824-7935","1467-8640"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320595","host_organization_name":"Wiley","host_organization_lineage":["https://openalex.org/P4310320595"],"host_organization_lineage_names":["Wiley"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computational Intelligence","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/A5102684411","display_name":"Xueqiong Yuan","orcid":"https://orcid.org/0009-0008-4706-3099"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xueqiong Yuan","raw_affiliation_strings":["School of Electronic Engineering Hunan College of Information  Xiangtan China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronic Engineering Hunan College of Information  Xiangtan China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007001354","display_name":"Feiyu Hu","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Feiyu Hu","raw_affiliation_strings":["School of Wind Energy Engineering Hunan Electrical College of Technology  Xiangtan China"],"raw_orcid":"https://orcid.org/0009-0001-7179-1482","affiliations":[{"raw_affiliation_string":"School of Wind Energy Engineering Hunan Electrical College of Technology  Xiangtan China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5051479591","display_name":"Zehui Zhu","orcid":"https://orcid.org/0000-0003-3965-4979"},"institutions":[{"id":"https://openalex.org/I4210122501","display_name":"Hunan Xiangdian Test Research Institute (China)","ror":"https://ror.org/0391pty66","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210122501"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zehui Zhu","raw_affiliation_strings":["State Grid Hunan Extra High Voltage Substation Company  Changsha China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Grid Hunan Extra High Voltage Substation Company  Changsha China","institution_ids":["https://openalex.org/I4210122501"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5007001354"],"corresponding_institution_ids":[],"apc_list":{"value":3450,"currency":"USD","value_usd":3450},"apc_paid":null,"fwci":0.5226,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.66381796,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":"41","issue":"3","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9990000128746033,"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":0.9990000128746033,"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":0.9308000206947327,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.7741813063621521},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5245513916015625},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5032939314842224},{"id":"https://openalex.org/keywords/decomposition","display_name":"Decomposition","score":0.47081121802330017},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.42824530601501465},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3643951416015625},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.106997549533844},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.09485301375389099}],"concepts":[{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.7741813063621521},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5245513916015625},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5032939314842224},{"id":"https://openalex.org/C124681953","wikidata":"https://www.wikidata.org/wiki/Q339062","display_name":"Decomposition","level":2,"score":0.47081121802330017},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.42824530601501465},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3643951416015625},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.106997549533844},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.09485301375389099},{"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/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1111/coin.70078","is_oa":false,"landing_page_url":"https://doi.org/10.1111/coin.70078","pdf_url":null,"source":{"id":"https://openalex.org/S56561474","display_name":"Computational Intelligence","issn_l":"0824-7935","issn":["0824-7935","1467-8640"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320595","host_organization_name":"Wiley","host_organization_lineage":["https://openalex.org/P4310320595"],"host_organization_lineage_names":["Wiley"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computational Intelligence","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":55,"referenced_works":["https://openalex.org/W602833636","https://openalex.org/W1923027492","https://openalex.org/W2789689614","https://openalex.org/W2900921197","https://openalex.org/W2946405318","https://openalex.org/W2956194600","https://openalex.org/W2981345666","https://openalex.org/W2995756228","https://openalex.org/W3090528971","https://openalex.org/W3130808753","https://openalex.org/W3153574022","https://openalex.org/W3196636665","https://openalex.org/W3200304500","https://openalex.org/W4213450433","https://openalex.org/W4220966296","https://openalex.org/W4289443182","https://openalex.org/W4292417287","https://openalex.org/W4300860779","https://openalex.org/W4303628746","https://openalex.org/W4313461942","https://openalex.org/W4320487090","https://openalex.org/W4321766484","https://openalex.org/W4323669640","https://openalex.org/W4372257357","https://openalex.org/W4378573288","https://openalex.org/W4385226727","https://openalex.org/W4385724950","https://openalex.org/W4385756500","https://openalex.org/W4386071000","https://openalex.org/W4386569382","https://openalex.org/W4387501945","https://openalex.org/W4388509959","https://openalex.org/W4388742619","https://openalex.org/W4388947904","https://openalex.org/W4389274098","https://openalex.org/W4389966098","https://openalex.org/W4391275467","https://openalex.org/W4391772729","https://openalex.org/W4391836549","https://openalex.org/W4391934226","https://openalex.org/W4392123354","https://openalex.org/W4392202905","https://openalex.org/W4392510482","https://openalex.org/W4393294796","https://openalex.org/W4393970653","https://openalex.org/W4395669689","https://openalex.org/W4395697085","https://openalex.org/W4396220526","https://openalex.org/W4396699441","https://openalex.org/W4399879027","https://openalex.org/W4400235584","https://openalex.org/W4400812216","https://openalex.org/W4401465337","https://openalex.org/W4401691402","https://openalex.org/W4403728468"],"related_works":["https://openalex.org/W2980611886","https://openalex.org/W42295635","https://openalex.org/W1973996291","https://openalex.org/W2330575325","https://openalex.org/W2163803519","https://openalex.org/W2497592525","https://openalex.org/W3096145648","https://openalex.org/W3197510923","https://openalex.org/W2033914206","https://openalex.org/W2042327336"],"abstract_inverted_index":{"ABSTRACT":[0],"Wind":[1],"power,":[2],"as":[3,69],"an":[4,145,187],"important":[5],"component":[6],"of":[7,19,29,42,136,167,182,189,212,220],"distributed":[8],"power":[9,22,44,115,222],"grid":[10,223],"integration,":[11],"plays":[12],"a":[13,20,55],"vital":[14],"role":[15],"in":[16,118],"the":[17,25,40,46,84,91,98,104,112,123,133,137,154,164,175,197,204,210,218],"establishment":[18],"robust":[21],"grid.":[23,47],"However,":[24],"size":[26],"and":[27,34,77,96,161,179,193],"direction":[28],"wind":[30,43,60,114,213,221],"speeds":[31],"are":[32,151],"random":[33],"intermittent,":[35],"posing":[36],"significant":[37],"challenges":[38],"to":[39,89,110,131,144],"integration":[41],"into":[45],"To":[48],"address":[49],"this":[50,52],"issue,":[51],"article":[53],"proposes":[54],"highly":[56],"accurate":[57],"hybrid":[58],"optimized":[59,146,155],"speed":[61,214],"prediction":[62],"model":[63,107,207],"(HOWSPM)":[64],"by":[65,186],"combining":[66],"techniques":[67],"such":[68],"data":[70,100],"noise":[71],"processing":[72],"methods,":[73],"intelligent":[74],"optimization":[75,86,126],"algorithms,":[76],"deep":[78],"learning":[79],"models.":[80],"First,":[81],"HOWSPM":[82,183,206],"utilizes":[83],"Rime":[85],"algorithm":[87,127],"(RIME)":[88],"optimize":[90],"variational":[92],"modal":[93,120,168],"decomposition":[94,101,106],"(VMD)":[95],"obtain":[97],"RIME\u2010VMD":[99,105],"model.":[102],"Second,":[103],"is":[108,129],"employed":[109],"preprocess":[111],"nonlinear":[113],"data,":[116],"resulting":[117],"10":[119,165],"eigencomponents.":[121],"Additionally,":[122],"fruit":[124],"fly":[125],"(FOA)":[128],"applied":[130],"determine":[132],"optimal":[134],"hyperparameters":[135],"bidirectional":[138],"long\u2010short":[139],"memory":[140],"network":[141,157],"(Bi\u2010LSTM),":[142],"leading":[143],"Bi\u2010LSTM":[147,156],"network.":[148],"Finally,":[149],"experiments":[150],"conducted":[152],"using":[153],"for":[158],"feature":[159],"extraction":[160],"training":[162],"on":[163],"types":[166],"data.":[169],"The":[170],"experimental":[171],"results":[172,201],"show":[173],"that":[174,203],"RMSE,":[176],"MAE,":[177],"MAPE,":[178],"R":[180],"2":[181],"were":[184],"improved":[185],"average":[188],"36.04%,":[190],"42.42%,":[191],"23.65%,":[192],"3.09%,":[194],"respectively,":[195],"across":[196],"four":[198],"sites.":[199],"Experimental":[200],"indicate":[202],"proposed":[205],"effectively":[208],"enhances":[209],"accuracy":[211],"prediction,":[215],"thereby":[216],"improving":[217],"efficiency":[219],"integration.":[224]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-06-17T08:01:34.144755","created_date":"2025-10-10T00:00:00"}