{"id":"https://openalex.org/W2745913225","doi":"https://doi.org/10.1109/tsg.2017.2743015","title":"Holographic Ensemble Forecasting Method for Short-Term Power Load","display_name":"Holographic Ensemble Forecasting Method for Short-Term Power Load","publication_year":2017,"publication_date":"2017-08-22","ids":{"openalex":"https://openalex.org/W2745913225","doi":"https://doi.org/10.1109/tsg.2017.2743015","mag":"2745913225"},"language":"en","primary_location":{"id":"doi:10.1109/tsg.2017.2743015","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsg.2017.2743015","pdf_url":null,"source":{"id":"https://openalex.org/S59604973","display_name":"IEEE Transactions on Smart Grid","issn_l":"1949-3053","issn":["1949-3053","1949-3061"],"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 Smart Grid","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/A5101984076","display_name":"Mo Zhou","orcid":"https://orcid.org/0000-0003-0768-1374"},"institutions":[{"id":"https://openalex.org/I16609230","display_name":"Hunan University","ror":"https://ror.org/05htk5m33","country_code":"CN","type":"education","lineage":["https://openalex.org/I16609230"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Mo Zhou","raw_affiliation_strings":["College of Computer Science and Electronic Engineering, Hunan University, Changsha, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science and Electronic Engineering, Hunan University, Changsha, China","institution_ids":["https://openalex.org/I16609230"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5030110245","display_name":"Min Jin","orcid":"https://orcid.org/0000-0002-4858-8048"},"institutions":[{"id":"https://openalex.org/I16609230","display_name":"Hunan University","ror":"https://ror.org/05htk5m33","country_code":"CN","type":"education","lineage":["https://openalex.org/I16609230"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Min Jin","raw_affiliation_strings":["College of Computer Science and Electronic Engineering, Hunan University, Changsha, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science and Electronic Engineering, Hunan University, Changsha, China","institution_ids":["https://openalex.org/I16609230"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5101984076"],"corresponding_institution_ids":["https://openalex.org/I16609230"],"apc_list":null,"apc_paid":null,"fwci":2.0346,"has_fulltext":false,"cited_by_count":53,"citation_normalized_percentile":{"value":0.87682483,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"10","issue":"1","first_page":"425","last_page":"434"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9997000098228455,"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":0.9997000098228455,"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/T11276","display_name":"Solar Radiation and Photovoltaics","score":0.9980999827384949,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9840999841690063,"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/computer-science","display_name":"Computer science","score":0.7277752161026001},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.7105473279953003},{"id":"https://openalex.org/keywords/ensemble-forecasting","display_name":"Ensemble forecasting","score":0.6471869945526123},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5777431726455688},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5758728981018066},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.5270074605941772},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5192630290985107},{"id":"https://openalex.org/keywords/probabilistic-forecasting","display_name":"Probabilistic forecasting","score":0.497649222612381},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.47334980964660645},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4455299973487854},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.4194953739643097},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4153457283973694}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7277752161026001},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.7105473279953003},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.6471869945526123},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5777431726455688},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5758728981018066},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5270074605941772},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5192630290985107},{"id":"https://openalex.org/C122282355","wikidata":"https://www.wikidata.org/wiki/Q7246855","display_name":"Probabilistic forecasting","level":3,"score":0.497649222612381},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.47334980964660645},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4455299973487854},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.4194953739643097},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4153457283973694},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"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/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"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/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tsg.2017.2743015","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsg.2017.2743015","pdf_url":null,"source":{"id":"https://openalex.org/S59604973","display_name":"IEEE Transactions on Smart Grid","issn_l":"1949-3053","issn":["1949-3053","1949-3061"],"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 Smart Grid","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/12","display_name":"Responsible consumption and production","score":0.5400000214576721}],"awards":[{"id":"https://openalex.org/G8436765734","display_name":null,"funder_award_id":"61773157","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8558210210","display_name":null,"funder_award_id":"61374172","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W79427069","https://openalex.org/W185455334","https://openalex.org/W1974160070","https://openalex.org/W1982169178","https://openalex.org/W1984051156","https://openalex.org/W2008750647","https://openalex.org/W2015799639","https://openalex.org/W2062582534","https://openalex.org/W2098207764","https://openalex.org/W2130426455","https://openalex.org/W2132698288","https://openalex.org/W2133752269","https://openalex.org/W2157825465","https://openalex.org/W2162505666","https://openalex.org/W2185592418","https://openalex.org/W2271974312","https://openalex.org/W2292129691","https://openalex.org/W2326513046","https://openalex.org/W2337474653","https://openalex.org/W2506829934","https://openalex.org/W2555957534","https://openalex.org/W2785046580","https://openalex.org/W2884843966","https://openalex.org/W4237537547","https://openalex.org/W6603182422","https://openalex.org/W6679105208","https://openalex.org/W6701590318","https://openalex.org/W6748147313"],"related_works":["https://openalex.org/W2794896638","https://openalex.org/W2891633941","https://openalex.org/W2990134330","https://openalex.org/W3202800081","https://openalex.org/W3101614107","https://openalex.org/W1909207154","https://openalex.org/W4390971112","https://openalex.org/W3036530763","https://openalex.org/W4320498733","https://openalex.org/W1790870804"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3,13,41,68,96],"newly":[4],"propose":[5],"a":[6,70,131,184,245],"holographic":[7],"ensemble":[8,28,55,82,149,161],"forecasting":[9,92,102,172,185,198,215,230],"method":[10,20,186],"(HEFM).":[11],"First,":[12],"use":[14,97],"the":[15,32,37,63,91,98,101,108,114,118,122,139,157,177,192,197,202,213,217,229,237,249],"mutual":[16],"information":[17,30,57,84,151,165,189],"and":[18,76,117,143,174,207,241,251,258],"statistical":[19],"to":[21,129,212,247],"select":[22],"feature":[23],"variables,":[24],"which":[25,52,79,104,135],"is":[26,53,80,105,136,147,183,221,244],"an":[27,54,81,148],"of":[29,56,83,107,121,150,162,179,196,219,239],"about":[31,58,85,152],"cross-border":[33],"multi-source":[34],"data":[35,259],"at":[36,62,90,156],"dataset":[38],"level.":[39,66,94,159],"Then,":[40],"generate":[42,130],"multiple":[43,59,86,109],"training":[44,133],"sets":[45,61],"by":[46,223],"performing":[47],"diversity":[48],"sampling":[49,64,170],"with":[50,187,236],"bootstrap,":[51],"sample":[60],"space":[65],"Next,":[67],"construct":[69],"multi-model":[71],"using":[72],"different":[73,256],"artificial":[74],"intelligence":[75],"machine-learning":[77],"algorithms,":[78,240],"nonlinear":[87],"heterogeneous":[88,110],"models":[89,111],"model":[93],"Finally,":[95],"original":[99],"features,":[100],"load":[103,120,203],"output":[106],"trained":[112],"in":[113,204],"first":[115],"learning,":[116],"actual":[119],"recent":[123],"period":[124],"before":[125],"each":[126],"forecasted":[127],"time":[128],"new":[132],"set,":[134],"used":[137],"for":[138,166,191],"online":[140,153],"second":[141,154],"learning":[142,155],"final":[144],"forecasting.":[145],"This":[146],"decision":[158],"The":[160,225],"multi-category":[163],"multi-state":[164],"four":[167],"levels":[168],"(dataset,":[169],"space,":[171],"model,":[173],"decision)":[175],"constitutes":[176],"framework":[178],"HEFM,":[180],"whose":[181],"essence":[182],"comprehensive":[188],"integration":[190],"whole":[193],"life":[194],"cycle":[195],"process.":[199],"We":[200],"study":[201],"Guangzhou,":[205],"China,":[206],"New":[208],"England,":[209],"USA.":[210],"Compared":[211],"state-of-the-art":[214],"methods,":[216],"MAPE":[218],"HEFM":[220],"reduced":[222],"7.69%-65.77%.":[224],"results":[226],"demonstrate":[227],"that":[228,242],"performance":[231],"may":[232],"not":[233],"be":[234],"improved":[235],"number":[238],"there":[243],"need":[246],"understand":[248],"positive":[250],"negative":[252],"fusion":[253],"effect":[254],"between":[255],"algorithms":[257],"characteristics.":[260]},"counts_by_year":[{"year":2025,"cited_by_count":9},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":11},{"year":2021,"cited_by_count":13},{"year":2020,"cited_by_count":8},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":2}],"updated_date":"2026-03-06T13:50:29.536080","created_date":"2025-10-10T00:00:00"}
