{"id":"https://openalex.org/W3094901686","doi":"https://doi.org/10.1155/2020/8811407","title":"A Short-Term Wind Speed Forecasting Hybrid Model Based on Empirical Mode Decomposition and Multiple Kernel Learning","display_name":"A Short-Term Wind Speed Forecasting Hybrid Model Based on Empirical Mode Decomposition and Multiple Kernel Learning","publication_year":2020,"publication_date":"2020-11-02","ids":{"openalex":"https://openalex.org/W3094901686","doi":"https://doi.org/10.1155/2020/8811407","mag":"3094901686"},"language":"en","primary_location":{"id":"doi:10.1155/2020/8811407","is_oa":true,"landing_page_url":"https://doi.org/10.1155/2020/8811407","pdf_url":"https://downloads.hindawi.com/journals/complexity/2020/8811407.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/2020/8811407.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101935920","display_name":"Yuanyuan Xu","orcid":"https://orcid.org/0000-0001-6447-2400"},"institutions":[{"id":"https://openalex.org/I119045251","display_name":"Huaqiao University","ror":"https://ror.org/03frdh605","country_code":"CN","type":"education","lineage":["https://openalex.org/I119045251"]},{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuanyuan Xu","raw_affiliation_strings":["College of Information Science and Engineering, Huaqiao University, Xiamen 361021, China","Department of Automation, Shanghai Jiaotong University, Shanghai 200240, China"],"affiliations":[{"raw_affiliation_string":"College of Information Science and Engineering, Huaqiao University, Xiamen 361021, China","institution_ids":["https://openalex.org/I119045251"]},{"raw_affiliation_string":"Department of Automation, Shanghai Jiaotong University, Shanghai 200240, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001818611","display_name":"Genke Yang","orcid":"https://orcid.org/0000-0003-3492-0211"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Genke Yang","raw_affiliation_strings":["Department of Automation, Shanghai Jiaotong University, Shanghai 200240, China","Ningbo Artificial Intelligence Institute, Shanghai Jiaotong University, Ningbo 315000, China"],"affiliations":[{"raw_affiliation_string":"Department of Automation, Shanghai Jiaotong University, Shanghai 200240, China","institution_ids":["https://openalex.org/I183067930"]},{"raw_affiliation_string":"Ningbo Artificial Intelligence Institute, Shanghai Jiaotong University, Ningbo 315000, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5001818611"],"corresponding_institution_ids":["https://openalex.org/I183067930"],"apc_list":{"value":2300,"currency":"USD","value_usd":2300},"apc_paid":{"value":2300,"currency":"USD","value_usd":2300},"fwci":0.7278,"has_fulltext":true,"cited_by_count":9,"citation_normalized_percentile":{"value":0.71203611,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":99},"biblio":{"volume":"2020","issue":null,"first_page":"1","last_page":"13"},"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/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9757000207901001,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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":0.9628000259399414,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/hilbert\u2013huang-transform","display_name":"Hilbert\u2013Huang transform","score":0.8658701181411743},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6604536771774292},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5687556862831116},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5666418075561523},{"id":"https://openalex.org/keywords/multiple-kernel-learning","display_name":"Multiple kernel learning","score":0.5203151702880859},{"id":"https://openalex.org/keywords/wind-speed","display_name":"Wind speed","score":0.5075327157974243},{"id":"https://openalex.org/keywords/mode","display_name":"Mode (computer interface)","score":0.5024762153625488},{"id":"https://openalex.org/keywords/wind-power","display_name":"Wind power","score":0.4861404597759247},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.417391836643219},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3990955948829651},{"id":"https://openalex.org/keywords/kernel-method","display_name":"Kernel method","score":0.23521319031715393},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1650215983390808},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.12039566040039062},{"id":"https://openalex.org/keywords/meteorology","display_name":"Meteorology","score":0.08728593587875366}],"concepts":[{"id":"https://openalex.org/C25570617","wikidata":"https://www.wikidata.org/wiki/Q1006462","display_name":"Hilbert\u2013Huang transform","level":3,"score":0.8658701181411743},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6604536771774292},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5687556862831116},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5666418075561523},{"id":"https://openalex.org/C2776879701","wikidata":"https://www.wikidata.org/wiki/Q25048660","display_name":"Multiple kernel learning","level":4,"score":0.5203151702880859},{"id":"https://openalex.org/C161067210","wikidata":"https://www.wikidata.org/wiki/Q1464943","display_name":"Wind speed","level":2,"score":0.5075327157974243},{"id":"https://openalex.org/C48677424","wikidata":"https://www.wikidata.org/wiki/Q6888088","display_name":"Mode (computer interface)","level":2,"score":0.5024762153625488},{"id":"https://openalex.org/C78600449","wikidata":"https://www.wikidata.org/wiki/Q43302","display_name":"Wind power","level":2,"score":0.4861404597759247},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.417391836643219},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3990955948829651},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.23521319031715393},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1650215983390808},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.12039566040039062},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.08728593587875366},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","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/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1155/2020/8811407","is_oa":true,"landing_page_url":"https://doi.org/10.1155/2020/8811407","pdf_url":"https://downloads.hindawi.com/journals/complexity/2020/8811407.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:8811407","is_oa":false,"landing_page_url":"http://downloads.hindawi.com/journals/8503/2020/8811407.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:385730b62c6f49aaa18ce3ea0f8aeec7","is_oa":true,"landing_page_url":"https://doaj.org/article/385730b62c6f49aaa18ce3ea0f8aeec7","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":"Complexity, Vol 2020 (2020)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1155/2020/8811407","is_oa":true,"landing_page_url":"https://doi.org/10.1155/2020/8811407","pdf_url":"https://downloads.hindawi.com/journals/complexity/2020/8811407.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":[{"id":"https://metadata.un.org/sdg/7","score":0.9100000262260437,"display_name":"Affordable and clean energy"}],"awards":[{"id":"https://openalex.org/G1040714068","display_name":null,"funder_award_id":"2018YFC0809200","funder_id":"https://openalex.org/F4320323970","funder_display_name":"Ministry of Industry and Information Technology of the People's Republic of China"},{"id":"https://openalex.org/G2149934046","display_name":null,"funder_award_id":"2018-06-46","funder_id":"https://openalex.org/F4320323970","funder_display_name":"Ministry of Industry and Information Technology of the People's Republic of China"},{"id":"https://openalex.org/G8202797743","display_name":null,"funder_award_id":"2017YFA60700602","funder_id":"https://openalex.org/F4320323970","funder_display_name":"Ministry of Industry and Information Technology of the People's Republic of China"}],"funders":[{"id":"https://openalex.org/F4320323970","display_name":"Ministry of Industry and Information Technology of the People's Republic of China","ror":"https://ror.org/0385nmy68"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3094901686.pdf","grobid_xml":"https://content.openalex.org/works/W3094901686.grobid-xml"},"referenced_works_count":41,"referenced_works":["https://openalex.org/W602833636","https://openalex.org/W1185746543","https://openalex.org/W1514832573","https://openalex.org/W1634828275","https://openalex.org/W1923027492","https://openalex.org/W1968528122","https://openalex.org/W1970978817","https://openalex.org/W1972500093","https://openalex.org/W1975783225","https://openalex.org/W1984061847","https://openalex.org/W1986478348","https://openalex.org/W1989770508","https://openalex.org/W1992827946","https://openalex.org/W2006292556","https://openalex.org/W2007221293","https://openalex.org/W2011630059","https://openalex.org/W2024377782","https://openalex.org/W2028477994","https://openalex.org/W2030034065","https://openalex.org/W2046580214","https://openalex.org/W2066890570","https://openalex.org/W2069458148","https://openalex.org/W2078192033","https://openalex.org/W2113238782","https://openalex.org/W2155355176","https://openalex.org/W2247296313","https://openalex.org/W2270059549","https://openalex.org/W2737765213","https://openalex.org/W2789051809","https://openalex.org/W2897446518","https://openalex.org/W2944487131","https://openalex.org/W2944800953","https://openalex.org/W2944835245","https://openalex.org/W2967862655","https://openalex.org/W2968624384","https://openalex.org/W2970853634","https://openalex.org/W2985525905","https://openalex.org/W3014587794","https://openalex.org/W3021129954","https://openalex.org/W3021265055","https://openalex.org/W3039398238"],"related_works":["https://openalex.org/W2900715739","https://openalex.org/W4380482219","https://openalex.org/W2289496068","https://openalex.org/W2043864454","https://openalex.org/W2547116720","https://openalex.org/W4377969695","https://openalex.org/W2028780417","https://openalex.org/W2008549418","https://openalex.org/W2188831877","https://openalex.org/W2157356416"],"abstract_inverted_index":{"Short-term":[0],"wind":[1,19,45,69,126],"speed":[2,20,46,70],"forecasting":[3,71,111],"plays":[4],"an":[5],"increasingly":[6],"important":[7],"role":[8],"in":[9,155,161],"the":[10,60,112,130,151,164],"security,":[11],"scheduling,":[12],"and":[13,25,129,135,169,172],"optimization":[14],"of":[15,62,163],"power":[16],"systems.":[17],"As":[18],"signals":[21,88,95],"are":[22,119,138],"usually":[23],"nonlinear":[24],"nonstationary,":[26],"how":[27],"to":[28,57,85,177],"accurately":[29],"forecast":[30],"future":[31],"states":[32],"is":[33,83,108],"a":[34,50,74,174],"challenge":[35],"for":[36,42,101,110],"existing":[37,147],"methods.":[38],"In":[39],"this":[40,156],"paper,":[41],"highly":[43],"complex":[44,87],"signals,":[47],"we":[48],"propose":[49],"multiple":[51,63,105],"kernel":[52,106],"learning-":[53],"(MKL-)":[54],"based":[55],"method":[56],"adaptively":[58],"assign":[59],"weights":[61],"prediction":[64,132,165],"functions,":[65],"which":[66],"extends":[67],"conventional":[68],"methods":[72],"using":[73],"support":[75],"vector":[76],"machine.":[77],"First,":[78],"empirical":[79],"mode":[80,92],"decomposition":[81],"(EMD)":[82],"used":[84],"decompose":[86],"into":[89],"several":[90,117],"intrinsic":[91],"function":[93],"component":[94],"with":[96,146],"different":[97,123],"time":[98],"scales.":[99],"Then,":[100],"each":[102],"channel,":[103],"one":[104],"model":[107,153],"constructed":[109],"current":[113],"sequence":[114],"signal.":[115],"Finally,":[116],"experiments":[118],"carried":[120],"out":[121],"on":[122],"New":[124],"Zealand":[125],"farm":[127],"data,":[128],"relevant":[131],"accuracy":[133,166],"indexes":[134,168],"confidence":[136,170],"intervals":[137,171],"evaluated.":[139],"Extensive":[140],"experimental":[141],"results":[142],"show":[143],"that,":[144],"compared":[145],"machine":[148],"learning":[149],"methods,":[150],"EMD-MKL":[152],"proposed":[154],"paper":[157],"has":[158],"better":[159,175],"performance":[160],"terms":[162],"evaluation":[167],"shows":[173],"ability":[176],"generalize.":[178]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":3}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}
