{"id":"https://openalex.org/W2080832532","doi":"https://doi.org/10.1109/icnc.2010.5583914","title":"Electric load forecasting using Bayesian Least Squares Support Vector Machine","display_name":"Electric load forecasting using Bayesian Least Squares Support Vector Machine","publication_year":2010,"publication_date":"2010-08-01","ids":{"openalex":"https://openalex.org/W2080832532","doi":"https://doi.org/10.1109/icnc.2010.5583914","mag":"2080832532"},"language":"en","primary_location":{"id":"doi:10.1109/icnc.2010.5583914","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icnc.2010.5583914","pdf_url":null,"source":{"id":"https://openalex.org/S4363608012","display_name":"2010 Sixth International Conference on Natural Computation","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2010 Sixth International Conference on Natural Computation","raw_type":"proceedings-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/A5100728521","display_name":"Xiaoxia Zheng","orcid":"https://orcid.org/0000-0001-5092-7152"},"institutions":[{"id":"https://openalex.org/I23632641","display_name":"Shanghai University of Electric Power","ror":"https://ror.org/02w4tny03","country_code":"CN","type":"education","lineage":["https://openalex.org/I23632641"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiaoxia Zheng","raw_affiliation_strings":["Shanghai University of Electrical Power, Shanghai, China","Shanghai Univ. of Electric Power (China)"],"affiliations":[{"raw_affiliation_string":"Shanghai University of Electrical Power, Shanghai, China","institution_ids":["https://openalex.org/I23632641"]},{"raw_affiliation_string":"Shanghai Univ. of Electric Power (China)","institution_ids":["https://openalex.org/I23632641"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5100728521"],"corresponding_institution_ids":["https://openalex.org/I23632641"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.14978187,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"19","issue":null,"first_page":"880","last_page":"883"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9995999932289124,"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":0.9995999932289124,"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/T12368","display_name":"Grey System Theory Applications","score":0.9979000091552734,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9940000176429749,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/support-vector-machine","display_name":"Support vector machine","score":0.8136758804321289},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6916438341140747},{"id":"https://openalex.org/keywords/statistical-learning-theory","display_name":"Statistical learning theory","score":0.6257579326629639},{"id":"https://openalex.org/keywords/least-squares-support-vector-machine","display_name":"Least squares support vector machine","score":0.6093547940254211},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.573968768119812},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5509874820709229},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5218518972396851},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5189416408538818},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.495644211769104},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.48151883482933044},{"id":"https://openalex.org/keywords/electrical-load","display_name":"Electrical load","score":0.4355233311653137},{"id":"https://openalex.org/keywords/least-squares-function-approximation","display_name":"Least-squares function approximation","score":0.4153149425983429},{"id":"https://openalex.org/keywords/relevance-vector-machine","display_name":"Relevance vector machine","score":0.4126951992511749},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3802996873855591},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.3066367506980896},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.15267574787139893},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.145159512758255}],"concepts":[{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.8136758804321289},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6916438341140747},{"id":"https://openalex.org/C2779915298","wikidata":"https://www.wikidata.org/wiki/Q7604400","display_name":"Statistical learning theory","level":3,"score":0.6257579326629639},{"id":"https://openalex.org/C145828037","wikidata":"https://www.wikidata.org/wiki/Q17086219","display_name":"Least squares support vector machine","level":3,"score":0.6093547940254211},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.573968768119812},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5509874820709229},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5218518972396851},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5189416408538818},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.495644211769104},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.48151883482933044},{"id":"https://openalex.org/C77715397","wikidata":"https://www.wikidata.org/wiki/Q931447","display_name":"Electrical load","level":3,"score":0.4355233311653137},{"id":"https://openalex.org/C9936470","wikidata":"https://www.wikidata.org/wiki/Q6510405","display_name":"Least-squares function approximation","level":3,"score":0.4153149425983429},{"id":"https://openalex.org/C14948415","wikidata":"https://www.wikidata.org/wiki/Q7310972","display_name":"Relevance vector machine","level":3,"score":0.4126951992511749},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3802996873855591},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.3066367506980896},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.15267574787139893},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.145159512758255},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","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/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icnc.2010.5583914","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icnc.2010.5583914","pdf_url":null,"source":{"id":"https://openalex.org/S4363608012","display_name":"2010 Sixth International Conference on Natural Computation","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2010 Sixth International Conference on Natural Computation","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1520773827","https://openalex.org/W1964357740","https://openalex.org/W1985539175","https://openalex.org/W2041868317","https://openalex.org/W2084601095","https://openalex.org/W2105916576","https://openalex.org/W2121844625","https://openalex.org/W2149298154","https://openalex.org/W2169548144","https://openalex.org/W2371398301","https://openalex.org/W2413535096","https://openalex.org/W6671612679","https://openalex.org/W6678298199"],"related_works":["https://openalex.org/W2349452475","https://openalex.org/W2356478727","https://openalex.org/W2383448435","https://openalex.org/W2368933322","https://openalex.org/W2378880053","https://openalex.org/W2122277321","https://openalex.org/W2382810153","https://openalex.org/W4291214623","https://openalex.org/W2094992113","https://openalex.org/W1963674083"],"abstract_inverted_index":{"Electric":[0],"load":[1,65,103],"forecasting":[2,66],"has":[3,113],"received":[4],"increasing":[5],"attention":[6],"over":[7],"the":[8,21,42,59,70,73,89,110,127],"years":[9],"by":[10],"academic":[11],"and":[12,23,75,92,117],"industrial":[13],"researchers":[14],"due":[15],"to":[16],"its":[17],"major":[18],"role":[19],"for":[20,63],"effective":[22],"economic":[24],"operation":[25],"of":[26],"power":[27,102],"utilities.":[28],"Least":[29],"Support":[30],"Vector":[31],"Machine":[32],"(LS":[33,56],"SVM)":[34,57],"is":[35,67,98],"a":[36,85],"new":[37],"learning":[38,44],"machine":[39,55],"based":[40,49],"on":[41,50],"statistical":[43],"theory.":[45],"A":[46],"modelling":[47],"approach":[48,97,112],"least":[51],"squares":[52],"support":[53],"vector":[54],"within":[58],"Bayesian":[60],"evidence":[61,71],"framework":[62],"short-term":[64],"proposed.":[68],"Under":[69],"framework,":[72],"regularization":[74],"kernel":[76],"parameters":[77],"can":[78,83],"be":[79],"adjusted":[80],"automatically,":[81],"which":[82],"achieve":[84],"fine":[86],"tradeoff":[87],"between":[88],"minimum":[90],"error":[91,121],"model's":[93],"complexities.":[94],"The":[95],"proposed":[96,111],"tested":[99],"using":[100,126],"actual":[101],"data":[104,130],"sets.":[105],"Experimental":[106],"results":[107],"show":[108],"that":[109],"better":[114],"generalization":[115],"performance":[116],"yields":[118],"lower":[119],"prediction":[120],"compared":[122],"with":[123],"LS":[124],"SVM":[125],"same":[128],"test":[129],"set.":[131]},"counts_by_year":[{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
