{"id":"https://openalex.org/W4400292667","doi":"https://doi.org/10.1007/s10489-024-05651-3","title":"Pinball-Huber boosted extreme learning machine regression: a multiobjective approach to accurate power load forecasting","display_name":"Pinball-Huber boosted extreme learning machine regression: a multiobjective approach to accurate power load forecasting","publication_year":2024,"publication_date":"2024-07-03","ids":{"openalex":"https://openalex.org/W4400292667","doi":"https://doi.org/10.1007/s10489-024-05651-3"},"language":"en","primary_location":{"id":"doi:10.1007/s10489-024-05651-3","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10489-024-05651-3","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10489-024-05651-3.pdf","source":{"id":"https://openalex.org/S74726891","display_name":"Applied Intelligence","issn_l":"0924-669X","issn":["0924-669X","1573-7497"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s10489-024-05651-3.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100397688","display_name":"Yang Yang","orcid":"https://orcid.org/0000-0002-8706-2831"},"institutions":[{"id":"https://openalex.org/I41198531","display_name":"Nanjing University of Posts and Telecommunications","ror":"https://ror.org/043bpky34","country_code":"CN","type":"education","lineage":["https://openalex.org/I41198531"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yang Yang","raw_affiliation_strings":["Nanjing University of Posts and Telecommunications, Nanjing, 210023, Jiangsu, PR China"],"affiliations":[{"raw_affiliation_string":"Nanjing University of Posts and Telecommunications, Nanjing, 210023, Jiangsu, PR China","institution_ids":["https://openalex.org/I41198531"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114115163","display_name":"Hao Lou","orcid":null},"institutions":[{"id":"https://openalex.org/I41198531","display_name":"Nanjing University of Posts and Telecommunications","ror":"https://ror.org/043bpky34","country_code":"CN","type":"education","lineage":["https://openalex.org/I41198531"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hao Lou","raw_affiliation_strings":["Nanjing University of Posts and Telecommunications, Nanjing, 210023, Jiangsu, PR China"],"affiliations":[{"raw_affiliation_string":"Nanjing University of Posts and Telecommunications, Nanjing, 210023, Jiangsu, PR China","institution_ids":["https://openalex.org/I41198531"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102903560","display_name":"Zijin Wang","orcid":"https://orcid.org/0000-0002-1491-8475"},"institutions":[{"id":"https://openalex.org/I41198531","display_name":"Nanjing University of Posts and Telecommunications","ror":"https://ror.org/043bpky34","country_code":"CN","type":"education","lineage":["https://openalex.org/I41198531"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zijin Wang","raw_affiliation_strings":["Nanjing University of Posts and Telecommunications, Nanjing, 210023, Jiangsu, PR China"],"affiliations":[{"raw_affiliation_string":"Nanjing University of Posts and Telecommunications, Nanjing, 210023, Jiangsu, PR China","institution_ids":["https://openalex.org/I41198531"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044575556","display_name":"Jinran Wu","orcid":"https://orcid.org/0000-0002-2388-3614"},"institutions":[{"id":"https://openalex.org/I86695891","display_name":"Australian Catholic University","ror":"https://ror.org/04cxm4j25","country_code":"AU","type":"education","lineage":["https://openalex.org/I86695891"]}],"countries":["AU"],"is_corresponding":true,"raw_author_name":"Jinran Wu","raw_affiliation_strings":["Australian Catholic University, North Sydney, 2060, NSW, Australia"],"affiliations":[{"raw_affiliation_string":"Australian Catholic University, North Sydney, 2060, NSW, Australia","institution_ids":["https://openalex.org/I86695891"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5044575556"],"corresponding_institution_ids":["https://openalex.org/I86695891"],"apc_list":{"value":2390,"currency":"EUR","value_usd":2990},"apc_paid":{"value":2390,"currency":"EUR","value_usd":2990},"fwci":3.4909,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.93367446,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":"54","issue":"17-18","first_page":"8745","last_page":"8760"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12676","display_name":"Machine Learning and ELM","score":1.0,"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"}},"topics":[{"id":"https://openalex.org/T12676","display_name":"Machine Learning and ELM","score":1.0,"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"}},{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9991999864578247,"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/T10320","display_name":"Neural Networks and Applications","score":0.9926999807357788,"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":0.8605665564537048},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.7643986940383911},{"id":"https://openalex.org/keywords/sorting","display_name":"Sorting","score":0.7240398526191711},{"id":"https://openalex.org/keywords/extreme-learning-machine","display_name":"Extreme learning machine","score":0.6309549808502197},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5837308764457703},{"id":"https://openalex.org/keywords/genetic-algorithm","display_name":"Genetic algorithm","score":0.453574538230896},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.43990641832351685},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4188840091228485},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4188675284385681},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.410983681678772},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.32879185676574707},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.15159600973129272},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.0800006091594696}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8605665564537048},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.7643986940383911},{"id":"https://openalex.org/C111696304","wikidata":"https://www.wikidata.org/wiki/Q2303697","display_name":"Sorting","level":2,"score":0.7240398526191711},{"id":"https://openalex.org/C2780150128","wikidata":"https://www.wikidata.org/wiki/Q21948731","display_name":"Extreme learning machine","level":3,"score":0.6309549808502197},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5837308764457703},{"id":"https://openalex.org/C8880873","wikidata":"https://www.wikidata.org/wiki/Q187787","display_name":"Genetic algorithm","level":2,"score":0.453574538230896},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.43990641832351685},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4188840091228485},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4188675284385681},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.410983681678772},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32879185676574707},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.15159600973129272},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.0800006091594696},{"id":"https://openalex.org/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","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/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"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/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1007/s10489-024-05651-3","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10489-024-05651-3","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10489-024-05651-3.pdf","source":{"id":"https://openalex.org/S74726891","display_name":"Applied Intelligence","issn_l":"0924-669X","issn":["0924-669X","1573-7497"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Intelligence","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1007/s10489-024-05651-3","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10489-024-05651-3","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10489-024-05651-3.pdf","source":{"id":"https://openalex.org/S74726891","display_name":"Applied Intelligence","issn_l":"0924-669X","issn":["0924-669X","1573-7497"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4400292667.pdf"},"referenced_works_count":51,"referenced_works":["https://openalex.org/W1711412747","https://openalex.org/W1968535060","https://openalex.org/W1970071487","https://openalex.org/W2017719067","https://openalex.org/W2023419040","https://openalex.org/W2033894760","https://openalex.org/W2052912973","https://openalex.org/W2056052206","https://openalex.org/W2064593130","https://openalex.org/W2065742895","https://openalex.org/W2069627663","https://openalex.org/W2121971770","https://openalex.org/W2134603844","https://openalex.org/W2157331557","https://openalex.org/W2167159964","https://openalex.org/W2270330859","https://openalex.org/W2275088575","https://openalex.org/W2338443192","https://openalex.org/W2543909292","https://openalex.org/W2569349941","https://openalex.org/W2591703502","https://openalex.org/W2732779023","https://openalex.org/W2736385547","https://openalex.org/W2796318045","https://openalex.org/W2910849319","https://openalex.org/W2936519819","https://openalex.org/W2954413670","https://openalex.org/W2996149946","https://openalex.org/W3004010467","https://openalex.org/W3004335670","https://openalex.org/W3007040893","https://openalex.org/W3026665694","https://openalex.org/W3040453902","https://openalex.org/W3041303727","https://openalex.org/W3044693143","https://openalex.org/W3047472222","https://openalex.org/W3119051141","https://openalex.org/W3137304821","https://openalex.org/W3165588125","https://openalex.org/W3193217591","https://openalex.org/W3195928301","https://openalex.org/W3203561252","https://openalex.org/W4303684236","https://openalex.org/W4304183101","https://openalex.org/W4307551445","https://openalex.org/W4309484088","https://openalex.org/W4311498863","https://openalex.org/W4313368260","https://openalex.org/W4376880182","https://openalex.org/W4379140952","https://openalex.org/W4385662342"],"related_works":["https://openalex.org/W2067443264","https://openalex.org/W31566076","https://openalex.org/W4297902562","https://openalex.org/W2741186499","https://openalex.org/W1986096622","https://openalex.org/W2969890106","https://openalex.org/W3006936859","https://openalex.org/W1987040457","https://openalex.org/W2378502887","https://openalex.org/W2184492720"],"abstract_inverted_index":{"Abstract":[0],"Power":[1],"load":[2,99],"data":[3,100],"frequently":[4],"display":[5],"outliers":[6],"and":[7],"an":[8,24],"uneven":[9],"distribution":[10],"of":[11,111],"noise.":[12],"To":[13],"tackle":[14],"this":[15],"issue,":[16],"we":[17,31],"present":[18],"a":[19,63,68],"forecasting":[20],"model":[21,88,95],"based":[22,57],"on":[23,58],"improved":[25],"extreme":[26],"learning":[27],"machine":[28],"(ELM).":[29],"Specifically,":[30],"introduce":[32],"the":[33,40,49,77,93,108,118],"novel":[34],"Pinball-Huber":[35],"robust":[36],"loss":[37,46],"function":[38,42,47],"as":[39],"objective":[41],"in":[43,76,101,107],"training.":[44],"The":[45,114],"enhances":[48],"precision":[50],"by":[51],"assigning":[52],"distinct":[53],"penalties":[54],"to":[55,96],"errors":[56,85],"their":[59],"directions.":[60],"We":[61,90],"employ":[62],"genetic":[64],"algorithm,":[65],"combined":[66],"with":[67],"swift":[69],"nondominated":[70],"sorting":[71],"technique,":[72],"for":[73],"multiobjective":[74],"optimization":[75],"ELM-Pinball-Huber":[78],"context.":[79],"This":[80],"method":[81],"simultaneously":[82],"reduces":[83],"training":[84],"while":[86],"streamlining":[87],"structure.":[89],"practically":[91],"apply":[92],"integrated":[94],"forecast":[97],"power":[98],"Taixing":[102],"City,":[103],"which":[104],"is":[105],"situated":[106],"southern":[109],"part":[110],"Jiangsu":[112],"Province.":[113],"empirical":[115],"findings":[116],"confirm":[117],"method\u2019s":[119],"effectiveness.":[120]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":1}],"updated_date":"2026-03-27T14:29:43.386196","created_date":"2025-10-10T00:00:00"}
