{"id":"https://openalex.org/W3010714554","doi":"https://doi.org/10.1155/2020/9601763","title":"Research on Hybrid Wind Speed Prediction System Based on Artificial Intelligence and Double Prediction Scheme","display_name":"Research on Hybrid Wind Speed Prediction System Based on Artificial Intelligence and Double Prediction Scheme","publication_year":2020,"publication_date":"2020-03-18","ids":{"openalex":"https://openalex.org/W3010714554","doi":"https://doi.org/10.1155/2020/9601763","mag":"3010714554"},"language":"en","primary_location":{"id":"doi:10.1155/2020/9601763","is_oa":true,"landing_page_url":"https://doi.org/10.1155/2020/9601763","pdf_url":"https://downloads.hindawi.com/journals/complexity/2020/9601763.pdf","source":{"id":"https://openalex.org/S207319443","display_name":"Complexity","issn_l":"1076-2787","issn":["1076-2787","1099-0526"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319869","host_organization_name":"Hindawi Publishing Corporation","host_organization_lineage":["https://openalex.org/P4310319869"],"host_organization_lineage_names":["Hindawi Publishing Corporation"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Complexity","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://downloads.hindawi.com/journals/complexity/2020/9601763.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5027283991","display_name":"Ying Nie","orcid":"https://orcid.org/0000-0002-3978-6640"},"institutions":[{"id":"https://openalex.org/I130480701","display_name":"Dongbei University of Finance and Economics","ror":"https://ror.org/05db1pj03","country_code":"CN","type":"education","lineage":["https://openalex.org/I130480701"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ying Nie","raw_affiliation_strings":["School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China","institution_ids":["https://openalex.org/I130480701"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102761442","display_name":"Bo He","orcid":"https://orcid.org/0000-0003-1850-7697"},"institutions":[{"id":"https://openalex.org/I130480701","display_name":"Dongbei University of Finance and Economics","ror":"https://ror.org/05db1pj03","country_code":"CN","type":"education","lineage":["https://openalex.org/I130480701"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"He Bo","raw_affiliation_strings":["Postdoctoral Research Station, Dongbei University of Finance and Economics, Dalian 116025, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Postdoctoral Research Station, Dongbei University of Finance and Economics, Dalian 116025, China","institution_ids":["https://openalex.org/I130480701"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103125949","display_name":"Weiqun Zhang","orcid":"https://orcid.org/0000-0001-6308-9450"},"institutions":[{"id":"https://openalex.org/I4210100953","display_name":"Xi'an University of Finance and Economics","ror":"https://ror.org/01b9y2p27","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210100953"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Weiqun Zhang","raw_affiliation_strings":["School of Statistics, Xi\u2019an University of Finance and Economics, Xi\u2019an 710100, China"],"raw_orcid":"https://orcid.org/0000-0001-6308-9450","affiliations":[{"raw_affiliation_string":"School of Statistics, Xi\u2019an University of Finance and Economics, Xi\u2019an 710100, China","institution_ids":["https://openalex.org/I4210100953"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100758737","display_name":"Haipeng Zhang","orcid":"https://orcid.org/0000-0002-4196-4098"},"institutions":[{"id":"https://openalex.org/I130480701","display_name":"Dongbei University of Finance and Economics","ror":"https://ror.org/05db1pj03","country_code":"CN","type":"education","lineage":["https://openalex.org/I130480701"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haipeng Zhang","raw_affiliation_strings":["School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China","institution_ids":["https://openalex.org/I130480701"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5103125949"],"corresponding_institution_ids":["https://openalex.org/I4210100953"],"apc_list":{"value":2550,"currency":"USD","value_usd":2550},"apc_paid":{"value":2550,"currency":"USD","value_usd":2550},"fwci":1.8713,"has_fulltext":true,"cited_by_count":25,"citation_normalized_percentile":{"value":0.85277419,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"2020","issue":null,"first_page":"1","last_page":"22"},"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/T10680","display_name":"Wind Energy Research and Development","score":0.9940000176429749,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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":0.9886999726295471,"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/computer-science","display_name":"Computer science","score":0.726606011390686},{"id":"https://openalex.org/keywords/wind-speed","display_name":"Wind speed","score":0.632540225982666},{"id":"https://openalex.org/keywords/randomness","display_name":"Randomness","score":0.5992357730865479},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5857057571411133},{"id":"https://openalex.org/keywords/interval","display_name":"Interval (graph theory)","score":0.5488834381103516},{"id":"https://openalex.org/keywords/wind-power","display_name":"Wind power","score":0.5371684432029724},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.5014324188232422},{"id":"https://openalex.org/keywords/backpropagation","display_name":"Backpropagation","score":0.49247774481773376},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.4548497796058655},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.43227800726890564},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.4204980432987213},{"id":"https://openalex.org/keywords/prediction-interval","display_name":"Prediction interval","score":0.41480138897895813},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.40891972184181213},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.38708221912384033},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3441745638847351},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12469789385795593},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.10585147142410278},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.09767228364944458}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.726606011390686},{"id":"https://openalex.org/C161067210","wikidata":"https://www.wikidata.org/wiki/Q1464943","display_name":"Wind speed","level":2,"score":0.632540225982666},{"id":"https://openalex.org/C125112378","wikidata":"https://www.wikidata.org/wiki/Q176640","display_name":"Randomness","level":2,"score":0.5992357730865479},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5857057571411133},{"id":"https://openalex.org/C2778067643","wikidata":"https://www.wikidata.org/wiki/Q166507","display_name":"Interval (graph theory)","level":2,"score":0.5488834381103516},{"id":"https://openalex.org/C78600449","wikidata":"https://www.wikidata.org/wiki/Q43302","display_name":"Wind power","level":2,"score":0.5371684432029724},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.5014324188232422},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.49247774481773376},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.4548497796058655},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.43227800726890564},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.4204980432987213},{"id":"https://openalex.org/C103402496","wikidata":"https://www.wikidata.org/wiki/Q1106171","display_name":"Prediction interval","level":2,"score":0.41480138897895813},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.40891972184181213},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38708221912384033},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3441745638847351},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12469789385795593},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.10585147142410278},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.09767228364944458},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"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/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1155/2020/9601763","is_oa":true,"landing_page_url":"https://doi.org/10.1155/2020/9601763","pdf_url":"https://downloads.hindawi.com/journals/complexity/2020/9601763.pdf","source":{"id":"https://openalex.org/S207319443","display_name":"Complexity","issn_l":"1076-2787","issn":["1076-2787","1099-0526"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319869","host_organization_name":"Hindawi Publishing Corporation","host_organization_lineage":["https://openalex.org/P4310319869"],"host_organization_lineage_names":["Hindawi Publishing Corporation"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Complexity","raw_type":"journal-article"},{"id":"pmh:oai:RePEc:hin:complx:9601763","is_oa":false,"landing_page_url":"http://downloads.hindawi.com/journals/8503/2020/9601763.xml","pdf_url":null,"source":{"id":"https://openalex.org/S4306401271","display_name":"RePEc: Research Papers in Economics","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I77793887","host_organization_name":"Federal Reserve Bank of St. Louis","host_organization_lineage":["https://openalex.org/I77793887"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},{"id":"pmh:oai:doaj.org/article:db912bd2d1ea4460a17c5bf5e38317c9","is_oa":true,"landing_page_url":"https://doaj.org/article/db912bd2d1ea4460a17c5bf5e38317c9","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":"Complexity, Vol 2020 (2020)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1155/2020/9601763","is_oa":true,"landing_page_url":"https://doi.org/10.1155/2020/9601763","pdf_url":"https://downloads.hindawi.com/journals/complexity/2020/9601763.pdf","source":{"id":"https://openalex.org/S207319443","display_name":"Complexity","issn_l":"1076-2787","issn":["1076-2787","1099-0526"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319869","host_organization_name":"Hindawi Publishing Corporation","host_organization_lineage":["https://openalex.org/P4310319869"],"host_organization_lineage_names":["Hindawi Publishing Corporation"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Complexity","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.9100000262260437,"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3010714554.pdf","grobid_xml":"https://content.openalex.org/works/W3010714554.grobid-xml"},"referenced_works_count":59,"referenced_works":["https://openalex.org/W1168959737","https://openalex.org/W1185746543","https://openalex.org/W1481489607","https://openalex.org/W1968315006","https://openalex.org/W1968442252","https://openalex.org/W1970978817","https://openalex.org/W1977398352","https://openalex.org/W1984061847","https://openalex.org/W1989098521","https://openalex.org/W1999614478","https://openalex.org/W2000571358","https://openalex.org/W2000982976","https://openalex.org/W2024377782","https://openalex.org/W2024692966","https://openalex.org/W2040298412","https://openalex.org/W2040317870","https://openalex.org/W2051418667","https://openalex.org/W2056749016","https://openalex.org/W2074511771","https://openalex.org/W2074715647","https://openalex.org/W2101457442","https://openalex.org/W2122130411","https://openalex.org/W2132477882","https://openalex.org/W2143381319","https://openalex.org/W2169548144","https://openalex.org/W2284726324","https://openalex.org/W2329476579","https://openalex.org/W2392689491","https://openalex.org/W2509426069","https://openalex.org/W2511683089","https://openalex.org/W2533807209","https://openalex.org/W2581822685","https://openalex.org/W2582163918","https://openalex.org/W2589454484","https://openalex.org/W2591426394","https://openalex.org/W2606283685","https://openalex.org/W2620924349","https://openalex.org/W2766047633","https://openalex.org/W2775477417","https://openalex.org/W2790834859","https://openalex.org/W2791494608","https://openalex.org/W2792829451","https://openalex.org/W2793017755","https://openalex.org/W2883694838","https://openalex.org/W2886344341","https://openalex.org/W2887216976","https://openalex.org/W2891967931","https://openalex.org/W2892055834","https://openalex.org/W2896864326","https://openalex.org/W2897932070","https://openalex.org/W2900921197","https://openalex.org/W2900946119","https://openalex.org/W2907029015","https://openalex.org/W2914856364","https://openalex.org/W2918257668","https://openalex.org/W2948535221","https://openalex.org/W2952080445","https://openalex.org/W2989725176","https://openalex.org/W2993333239"],"related_works":["https://openalex.org/W3034924094","https://openalex.org/W1488708774","https://openalex.org/W3094954546","https://openalex.org/W2981906196","https://openalex.org/W2487170621","https://openalex.org/W2155128126","https://openalex.org/W2904627439","https://openalex.org/W2953394921","https://openalex.org/W1997526000","https://openalex.org/W2172982367"],"abstract_inverted_index":{"Wind":[0],"energy":[1],"analysis":[2],"and":[3,16,38,66,75,95,200],"wind":[4,12,33,43,63,85],"speed":[5,44,64,86],"modeling":[6],"have":[7,17,54,67],"a":[8,49,82,91,116,123],"significant":[9,19],"impact":[10],"on":[11,28,122,133,155],"power":[13,209],"generation":[14],"systems":[15],"attracted":[18],"attention":[20],"from":[21],"many":[22],"researchers":[23],"in":[24,110,197],"recent":[25],"decades.":[26],"Based":[27,154],"the":[29,40,102,111,128,140,149,171,191],"inherent":[30],"characteristics":[31],"of":[32,42,62,104,148,151,173],"speed,":[34],"such":[35],"as":[36,204],"nonlinearity":[37],"randomness,":[39],"prediction":[41,58,74,88,93,97,109,114,142,157,163,184],"is":[45],"considered":[46,56],"to":[47,99,138,170],"be":[48,202],"challenging":[50],"task.":[51],"Previous":[52],"studies":[53],"only":[55],"point":[57,92,108,152,156],"or":[59],"interval":[60,96,162,183],"measurement":[61],"separately":[65],"not":[68],"combined":[69],"these":[70],"two":[71],"methods":[72,196],"for":[73,101,208],"analysis.":[76],"In":[77],"this":[78],"study,":[79],"we":[80,159],"developed":[81,112],"novel":[83,117],"hybrid":[84],"double":[87,113],"system":[89,193,210],"comprising":[90],"module":[94,98],"compensate":[100],"shortcomings":[103],"existing":[105,195],"research.":[106],"Regarding":[107],"system,":[115],"nonlinear":[118],"integration":[119],"method":[120,164],"based":[121,132],"backpropagation":[124],"network":[125],"optimized":[126],"using":[127],"multiobjective":[129],"evolutionary":[130],"algorithm":[131],"decomposition":[134],"was":[135],"successfully":[136],"implemented":[137],"derive":[139],"final":[141],"results,":[143,158],"which":[144,180],"enable":[145],"further":[146],"improvement":[147],"accuracy":[150],"prediction.":[153],"propose":[160],"an":[161,205],"that":[165,190],"constructs":[166],"different":[167,174],"intervals":[168],"according":[169],"classification":[172],"data":[175],"features":[176],"via":[177],"fuzzy":[178],"clustering,":[179],"provides":[181],"reliable":[182],"results.":[185],"The":[186],"experimental":[187],"results":[188],"demonstrate":[189],"proposed":[192],"outperforms":[194],"engineering":[198],"applications":[199],"can":[201],"used":[203],"effective":[206],"technology":[207],"planning.":[211]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":7},{"year":2020,"cited_by_count":4}],"updated_date":"2026-07-23T08:03:31.855105","created_date":"2025-10-10T00:00:00"}