{"id":"https://openalex.org/W2821843609","doi":"https://doi.org/10.1109/tii.2018.2854549","title":"Short-Term Wind Speed Forecasting via Stacked Extreme Learning Machine With Generalized Correntropy","display_name":"Short-Term Wind Speed Forecasting via Stacked Extreme Learning Machine With Generalized Correntropy","publication_year":2018,"publication_date":"2018-07-09","ids":{"openalex":"https://openalex.org/W2821843609","doi":"https://doi.org/10.1109/tii.2018.2854549","mag":"2821843609"},"language":"en","primary_location":{"id":"doi:10.1109/tii.2018.2854549","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tii.2018.2854549","pdf_url":null,"source":{"id":"https://openalex.org/S184777250","display_name":"IEEE Transactions on Industrial Informatics","issn_l":"1551-3203","issn":["1551-3203","1941-0050"],"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 Industrial Informatics","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/A5070952629","display_name":"Xiong Luo","orcid":"https://orcid.org/0000-0002-1929-8447"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Xiong Luo","raw_affiliation_strings":["Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101720398","display_name":"Jiankun Sun","orcid":"https://orcid.org/0000-0002-7121-1206"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiankun Sun","raw_affiliation_strings":["Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100447540","display_name":"Long Wang","orcid":"https://orcid.org/0000-0001-6695-6054"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Long Wang","raw_affiliation_strings":["Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100442319","display_name":"Weiping Wang","orcid":"https://orcid.org/0000-0002-6796-7596"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Weiping Wang","raw_affiliation_strings":["Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065763841","display_name":"Wenbing Zhao","orcid":"https://orcid.org/0000-0002-3202-1127"},"institutions":[{"id":"https://openalex.org/I102607778","display_name":"Cleveland State University","ror":"https://ror.org/002tx1f22","country_code":"US","type":"education","lineage":["https://openalex.org/I102607778"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wenbing Zhao","raw_affiliation_strings":["Department of Electrical Engineering and Computer Science, Cleveland State University, Cleveland, OH, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering and Computer Science, Cleveland State University, Cleveland, OH, USA","institution_ids":["https://openalex.org/I102607778"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029265765","display_name":"Jinsong Wu","orcid":"https://orcid.org/0000-0003-4720-5946"},"institutions":[{"id":"https://openalex.org/I69737025","display_name":"University of Chile","ror":"https://ror.org/047gc3g35","country_code":"CL","type":"education","lineage":["https://openalex.org/I69737025"]}],"countries":["CL"],"is_corresponding":false,"raw_author_name":"Jinsong Wu","raw_affiliation_strings":["Department of Electrical Engineering, Universidad de Chile, Santiago, Chile"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Universidad de Chile, Santiago, Chile","institution_ids":["https://openalex.org/I69737025"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100677096","display_name":"Jenq\u2010Haur Wang","orcid":"https://orcid.org/0000-0002-6076-7380"},"institutions":[{"id":"https://openalex.org/I118292597","display_name":"National Taipei University of Technology","ror":"https://ror.org/00cn92c09","country_code":"TW","type":"education","lineage":["https://openalex.org/I118292597"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Jenq-Haur Wang","raw_affiliation_strings":["Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei, Taiwan"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei, Taiwan","institution_ids":["https://openalex.org/I118292597"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100764713","display_name":"Zijun Zhang","orcid":"https://orcid.org/0000-0002-2717-5033"},"institutions":[{"id":"https://openalex.org/I168719708","display_name":"City University of Hong Kong","ror":"https://ror.org/03q8dnn23","country_code":"HK","type":"education","lineage":["https://openalex.org/I168719708"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Zijun Zhang","raw_affiliation_strings":["School of Data Science, City University of Hong Kong, Hong Kong"],"affiliations":[{"raw_affiliation_string":"School of Data Science, City University of Hong Kong, Hong Kong","institution_ids":["https://openalex.org/I168719708"]}]}],"institutions":[],"countries_distinct_count":4,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5070952629"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":18.1817,"has_fulltext":false,"cited_by_count":247,"citation_normalized_percentile":{"value":0.99520278,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":"14","issue":"11","first_page":"4963","last_page":"4971"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9998999834060669,"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":0.9998999834060669,"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/T11233","display_name":"Advanced Adaptive Filtering Techniques","score":0.9987000226974487,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/T10573","display_name":"Power Quality and Harmonics","score":0.9951000213623047,"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/extreme-learning-machine","display_name":"Extreme learning machine","score":0.9425921440124512},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7264339327812195},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5952674150466919},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.5180128216743469},{"id":"https://openalex.org/keywords/wind-speed","display_name":"Wind speed","score":0.48605114221572876},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.45857080817222595},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.45639580488204956},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4504554867744446},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3454556167125702}],"concepts":[{"id":"https://openalex.org/C2780150128","wikidata":"https://www.wikidata.org/wiki/Q21948731","display_name":"Extreme learning machine","level":3,"score":0.9425921440124512},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7264339327812195},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5952674150466919},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.5180128216743469},{"id":"https://openalex.org/C161067210","wikidata":"https://www.wikidata.org/wiki/Q1464943","display_name":"Wind speed","level":2,"score":0.48605114221572876},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.45857080817222595},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45639580488204956},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4504554867744446},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3454556167125702},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tii.2018.2854549","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tii.2018.2854549","pdf_url":null,"source":{"id":"https://openalex.org/S184777250","display_name":"IEEE Transactions on Industrial Informatics","issn_l":"1551-3203","issn":["1551-3203","1941-0050"],"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 Industrial Informatics","raw_type":"journal-article"},{"id":"pmh:oai:engagedscholarship.csuohio.edu:enece_facpub-1445","is_oa":false,"landing_page_url":"https://engagedscholarship.csuohio.edu/enece_facpub/447","pdf_url":null,"source":{"id":"https://openalex.org/S4377196594","display_name":"EngagedScholarship - Cleveland State University Scholarly & Creative Work from CSU (Cleveland State University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I102607778","host_organization_name":"Cleveland State University","host_organization_lineage":["https://openalex.org/I102607778"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Electrical Engineering &amp; Computer Science Faculty Publications","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","score":0.8500000238418579,"display_name":"Affordable and clean energy"}],"awards":[{"id":"https://openalex.org/G4365766575","display_name":null,"funder_award_id":"61174103","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5919323023","display_name":null,"funder_award_id":"2017YFB0702300","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G6150030697","display_name":null,"funder_award_id":"06500025","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G6458097795","display_name":null,"funder_award_id":"61603032","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8008212812","display_name":null,"funder_award_id":"06500078","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W341735883","https://openalex.org/W1495476169","https://openalex.org/W1519238132","https://openalex.org/W1982870982","https://openalex.org/W2019607443","https://openalex.org/W2024377782","https://openalex.org/W2039238531","https://openalex.org/W2044810230","https://openalex.org/W2061065224","https://openalex.org/W2066341465","https://openalex.org/W2081526790","https://openalex.org/W2111072639","https://openalex.org/W2111307099","https://openalex.org/W2114471530","https://openalex.org/W2135160607","https://openalex.org/W2189201746","https://openalex.org/W2511683089","https://openalex.org/W2515948533","https://openalex.org/W2520861906","https://openalex.org/W2739250163","https://openalex.org/W2746865825","https://openalex.org/W2756322174","https://openalex.org/W2767073696","https://openalex.org/W2994602700","https://openalex.org/W3024853581"],"related_works":["https://openalex.org/W2067443264","https://openalex.org/W31566076","https://openalex.org/W4297902562","https://openalex.org/W2741186499","https://openalex.org/W2804652951","https://openalex.org/W2968645206","https://openalex.org/W2556335056","https://openalex.org/W2002678693","https://openalex.org/W2743832667","https://openalex.org/W1986096622"],"abstract_inverted_index":{"Recently,":[0],"wind":[1,193,216],"speed":[2,98,217],"forecasting":[3,45,154,168,213],"as":[4],"an":[5,10,108,125],"effective":[6],"computing":[7,53,82,227],"technique":[8],"plays":[9],"important":[11],"role":[12],"in":[13,43,143,200],"advancing":[14],"industry":[15],"informatics,":[16],"while":[17,186],"dealing":[18],"with":[19,47,95,145,162,230],"these":[20,44],"issues":[21],"of":[22,50,70,136,158,209],"control":[23],"and":[24,75,99,181,211,233],"operation":[25],"for":[26],"renewable":[27],"power":[28],"systems.":[29],"However,":[30],"it":[31],"is":[32,79,107,128,178],"facing":[33],"some":[34,201],"increasing":[35],"difficulties":[36],"to":[37,57,92,150,165,191],"handle":[38],"the":[39,48,68,86,133,137,146,153,159,173,196,206,221],"large-scale":[40],"dataset":[41],"generated":[42],"applications,":[46],"purpose":[49],"ensuring":[51],"stable":[52,180],"performance.":[54,102,155],"In":[55,122],"response":[56],"such":[58],"limitation,":[59],"this":[60,123],"paper":[61],"proposes":[62],"a":[63,80,179],"more":[64,234],"practical":[65],"approach":[66,223],"through":[67],"combination":[69],"extreme-learning":[71],"machine":[72,188],"(ELM)":[73],"method":[74,190],"deep-learning":[76,113],"model.":[77],"ELM":[78,105,110,144],"novel":[81],"paradigm":[83],"that":[84,220],"enables":[85],"neural":[87],"network":[88],"(NN)":[89],"based":[90],"learning":[91,189],"be":[93],"achieved":[94],"fast":[96],"training":[97],"good":[100],"generalization":[101],"The":[103,156],"stacked":[104],"(SELM)":[106],"advanced":[109],"algorithm":[111],"under":[112],"framework,":[114],"which":[115],"works":[116],"efficiently":[117],"on":[118,172,214],"memory":[119],"consumption":[120],"decrease.":[121],"paper,":[124],"enhanced":[126,160],"SELM":[127,161],"accordingly":[129],"developed":[130],"via":[131],"replacing":[132],"Euclidean":[134],"norm":[135],"mean":[138],"square":[139],"error":[140],"(MSE)":[141],"criterion":[142,149],"generalized":[147,163],"correntropy":[148,164,177],"further":[151],"improve":[152],"advantage":[157],"achieve":[166,225],"better":[167,226],"performance":[169,228],"mainly":[170],"relies":[171],"following":[174],"aspect.":[175],"Generalized":[176],"robust":[182],"nonlinear":[183],"similarity":[184],"measure":[185],"employing":[187],"forecast":[192],"speed,":[194],"where":[195],"outliers":[197],"may":[198],"exist":[199],"industrially":[202],"measured":[203],"values.":[204],"Specifically,":[205],"experimental":[207],"results":[208],"short-term":[210],"ultra-short-term":[212],"real":[215],"data":[218],"show":[219],"proposed":[222],"can":[224],"compared":[229],"other":[231],"traditional":[232],"recent":[235],"methods.":[236]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":14},{"year":2024,"cited_by_count":24},{"year":2023,"cited_by_count":27},{"year":2022,"cited_by_count":42},{"year":2021,"cited_by_count":44},{"year":2020,"cited_by_count":49},{"year":2019,"cited_by_count":38},{"year":2018,"cited_by_count":8}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}
