{"id":"https://openalex.org/W2051741054","doi":"https://doi.org/10.4304/jcp.5.5.703-708","title":"The Optimized Comparison of The Gray Model Improved by Posterior-error-test and SVM Modified by Markov Residual Error in The Long-medium Power Load Forecast","display_name":"The Optimized Comparison of The Gray Model Improved by Posterior-error-test and SVM Modified by Markov Residual Error in The Long-medium Power Load Forecast","publication_year":2010,"publication_date":"2010-04-01","ids":{"openalex":"https://openalex.org/W2051741054","doi":"https://doi.org/10.4304/jcp.5.5.703-708","mag":"2051741054"},"language":"en","primary_location":{"id":"doi:10.4304/jcp.5.5.703-708","is_oa":false,"landing_page_url":"https://doi.org/10.4304/jcp.5.5.703-708","pdf_url":null,"source":{"id":"https://openalex.org/S77894049","display_name":"Journal of Computers","issn_l":"1796-203X","issn":["1796-203X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318660","host_organization_name":"Academy Publisher","host_organization_lineage":["https://openalex.org/P4310318660"],"host_organization_lineage_names":["Academy Publisher"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computers","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":null,"display_name":"Zhengang Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Zhengang Zhang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5108911205","display_name":"Wei Li","orcid":"https://orcid.org/0009-0007-0450-6075"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wei Li","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.1363,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.81990415,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"5","issue":"5","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T14392","display_name":"Geoscience and Mining Technology","score":0.9750999808311462,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"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/T14392","display_name":"Geoscience and Mining Technology","score":0.9750999808311462,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"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/T13832","display_name":"Advanced Decision-Making Techniques","score":0.9460999965667725,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T13717","display_name":"Advanced Algorithms and Applications","score":0.9401000142097473,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.7700008153915405},{"id":"https://openalex.org/keywords/gray","display_name":"Gray (unit)","score":0.6654430627822876},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5897103548049927},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.4748442471027374},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.41871851682662964},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3734593391418457},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.36759859323501587},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.30942362546920776},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.30346423387527466},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.2917953133583069},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.19802749156951904}],"concepts":[{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.7700008153915405},{"id":"https://openalex.org/C166275286","wikidata":"https://www.wikidata.org/wiki/Q190095","display_name":"Gray (unit)","level":2,"score":0.6654430627822876},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5897103548049927},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.4748442471027374},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.41871851682662964},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3734593391418457},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36759859323501587},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.30942362546920776},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.30346423387527466},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2917953133583069},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.19802749156951904},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.4304/jcp.5.5.703-708","is_oa":false,"landing_page_url":"https://doi.org/10.4304/jcp.5.5.703-708","pdf_url":null,"source":{"id":"https://openalex.org/S77894049","display_name":"Journal of Computers","issn_l":"1796-203X","issn":["1796-203X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318660","host_organization_name":"Academy Publisher","host_organization_lineage":["https://openalex.org/P4310318660"],"host_organization_lineage_names":["Academy Publisher"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computers","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":4,"referenced_works":["https://openalex.org/W2321075151","https://openalex.org/W2355146649","https://openalex.org/W2364278570","https://openalex.org/W2371844551"],"related_works":["https://openalex.org/W2808615211","https://openalex.org/W4250628736","https://openalex.org/W4246259471","https://openalex.org/W4246457037","https://openalex.org/W2049142306","https://openalex.org/W4242439251","https://openalex.org/W659309325","https://openalex.org/W1989714207","https://openalex.org/W2765495565","https://openalex.org/W2022419910"],"abstract_inverted_index":{"Generally,":[0],"the":[1,22,41,55,63,70,76,95,115,143,156],"long-medium":[2],"power":[3,67],"load":[4,68,147],"forecasting":[5],"sequence":[6],"has":[7],"small":[8],"sample,":[9],"stochastic":[10],"growth":[11],"and":[12,17,37,44,61,169],"nonlinear":[13,38],"wave":[14],"characteristics.":[15],"Gray":[16],"SVM":[18,81,131],"model":[19,58,93,128],"could":[20,108],"reflect":[21,110],"relationship":[23],"between":[24],"growing":[25],"characters-":[28],"-tics":[34],"characteristics":[39],"to":[40,163],"series":[42,144],"effectively":[43],"make":[45],"fitting":[46],"calculation":[47],".":[49],"The":[52],"paper":[53],"modifies":[54],"proposed":[56],"gray":[57],"through":[59,166],"posterior-error-test":[60],"compares":[62],"predictive":[64],"value":[65],"of":[66,145],"when":[69],"evaluation":[71],"result":[72,78],"is":[73,83,94,135,149,153,160],"best":[74],"with":[75,118],"optimal":[77],"forecast":[79,127,158],"by":[80,85,114],"that":[82,106,112,155],"modified":[84],"Markov":[86,107],"residual.":[87],"Then":[88],"we":[89,103],"can":[90,104,171],"find":[91],"which":[92],"better.":[96],"As":[99],"result,":[100],"see":[105],"well":[109],"randomness":[111],"produced":[113],"system":[116],"involve":[117],"many":[119],"complex":[120],"factors.":[121],"A":[124],"based":[129],"on":[130],"algorithm":[132],"establish":[138],"ed":[140],",":[142],"historical":[146],"variables":[148],"rolling":[150],"forecasted.":[151],"It":[152],"proved":[154],"presented":[157],"method":[159],"superior":[161],"obviously":[162],"traditional":[164],"methods":[165],"empirical":[167],"study,":[168],"it":[170],"be":[172],"used":[173],"generally.":[174]},"counts_by_year":[{"year":2013,"cited_by_count":1}],"updated_date":"2026-04-16T08:26:57.006410","created_date":"2025-10-10T00:00:00"}
