{"id":"https://openalex.org/W4205735732","doi":"https://doi.org/10.1109/icct52962.2021.9657948","title":"Short-Term Power Load Probability Density Forecasting Based on a Double-Layer LSTM-Attention Quantile Regression","display_name":"Short-Term Power Load Probability Density Forecasting Based on a Double-Layer LSTM-Attention Quantile Regression","publication_year":2021,"publication_date":"2021-10-13","ids":{"openalex":"https://openalex.org/W4205735732","doi":"https://doi.org/10.1109/icct52962.2021.9657948"},"language":"en","primary_location":{"id":"doi:10.1109/icct52962.2021.9657948","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icct52962.2021.9657948","pdf_url":null,"source":{"id":"https://openalex.org/S4363607878","display_name":"2021 IEEE 21st International Conference on Communication Technology (ICCT)","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":"2021 IEEE 21st International Conference on Communication Technology (ICCT)","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/A5057146908","display_name":"Xiaofeng Tao","orcid":null},"institutions":[{"id":"https://openalex.org/I4210118629","display_name":"NARI Group (China)","ror":"https://ror.org/02egn3136","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210118629"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiaofeng Tao","raw_affiliation_strings":["NARI Group Co. Ltd., State Grid Electric Power Research Institute, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"NARI Group Co. Ltd., State Grid Electric Power Research Institute, Nanjing, China","institution_ids":["https://openalex.org/I4210118629"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064477629","display_name":"Yang Lu","orcid":"https://orcid.org/0000-0002-7141-1048"},"institutions":[{"id":"https://openalex.org/I4210118629","display_name":"NARI Group (China)","ror":"https://ror.org/02egn3136","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210118629"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yang Lu","raw_affiliation_strings":["NARI Group Co. Ltd., State Grid Electric Power Research Institute, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"NARI Group Co. Ltd., State Grid Electric Power Research Institute, Nanjing, China","institution_ids":["https://openalex.org/I4210118629"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102712919","display_name":"Xueliang Yang","orcid":"https://orcid.org/0000-0003-2313-0329"},"institutions":[{"id":"https://openalex.org/I4210118629","display_name":"NARI Group (China)","ror":"https://ror.org/02egn3136","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210118629"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xueliang Yang","raw_affiliation_strings":["NARI Group Co. Ltd., State Grid Electric Power Research Institute, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"NARI Group Co. Ltd., State Grid Electric Power Research Institute, Nanjing, China","institution_ids":["https://openalex.org/I4210118629"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5057146908"],"corresponding_institution_ids":["https://openalex.org/I4210118629"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.22932103,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"43","issue":null,"first_page":"1046","last_page":"1051"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":1.0,"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":1.0,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9979000091552734,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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/T13955","display_name":"Evaluation Methods in Various Fields","score":0.9901000261306763,"subfield":{"id":"https://openalex.org/subfields/2302","display_name":"Ecological Modeling"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/quantile","display_name":"Quantile","score":0.7060065269470215},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6525746583938599},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.6082384586334229},{"id":"https://openalex.org/keywords/quantile-regression","display_name":"Quantile regression","score":0.5583041906356812},{"id":"https://openalex.org/keywords/smoothing","display_name":"Smoothing","score":0.5280547738075256},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.47726577520370483},{"id":"https://openalex.org/keywords/smart-grid","display_name":"Smart grid","score":0.46731895208358765},{"id":"https://openalex.org/keywords/kernel-density-estimation","display_name":"Kernel density estimation","score":0.4659819006919861},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.45225951075553894},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.41156184673309326},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2356368601322174},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2216866910457611},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15679877996444702},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.12944507598876953}],"concepts":[{"id":"https://openalex.org/C118671147","wikidata":"https://www.wikidata.org/wiki/Q578714","display_name":"Quantile","level":2,"score":0.7060065269470215},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6525746583938599},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.6082384586334229},{"id":"https://openalex.org/C63817138","wikidata":"https://www.wikidata.org/wiki/Q3455889","display_name":"Quantile regression","level":2,"score":0.5583041906356812},{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.5280547738075256},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.47726577520370483},{"id":"https://openalex.org/C10558101","wikidata":"https://www.wikidata.org/wiki/Q689855","display_name":"Smart grid","level":2,"score":0.46731895208358765},{"id":"https://openalex.org/C71134354","wikidata":"https://www.wikidata.org/wiki/Q458825","display_name":"Kernel density estimation","level":3,"score":0.4659819006919861},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.45225951075553894},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.41156184673309326},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2356368601322174},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2216866910457611},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15679877996444702},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.12944507598876953},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"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},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","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":1,"locations":[{"id":"doi:10.1109/icct52962.2021.9657948","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icct52962.2021.9657948","pdf_url":null,"source":{"id":"https://openalex.org/S4363607878","display_name":"2021 IEEE 21st International Conference on Communication Technology (ICCT)","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":"2021 IEEE 21st International Conference on Communication Technology (ICCT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","score":0.7599999904632568,"id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W1979793013","https://openalex.org/W2032161710","https://openalex.org/W2133564696","https://openalex.org/W2275088575","https://openalex.org/W2382217885","https://openalex.org/W2891150346","https://openalex.org/W2910322950","https://openalex.org/W2922146383","https://openalex.org/W2964068664","https://openalex.org/W3046072278","https://openalex.org/W3155706277","https://openalex.org/W4245093383","https://openalex.org/W6679434410","https://openalex.org/W6754950502"],"related_works":["https://openalex.org/W4206511378","https://openalex.org/W4206618949","https://openalex.org/W2526321210","https://openalex.org/W3205863630","https://openalex.org/W4318833145","https://openalex.org/W2364275385","https://openalex.org/W4388704167","https://openalex.org/W2007977664","https://openalex.org/W4376874882","https://openalex.org/W2224749288"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"a":[3,9,104],"quantile":[4],"regression":[5],"method":[6,80],"based":[7],"on":[8,92],"two-layer":[10,32],"Long-short-term":[11],"memory":[12],"network":[13,35],"with":[14],"attention":[15,51],"scheme":[16],"is":[17,36,53,81],"proposed":[18,37,100],"for":[19],"the":[20,41,50,60,64,76,85,99,115],"short-term":[21,45],"power":[22,46,65,86,94,108],"load":[23,47,66,87,95,109],"probability":[24,88,110],"density":[25,111],"forecasting":[26,112],"problem":[27],"in":[28],"smart":[29],"grid.":[30],"The":[31],"LSTM":[33],"deep":[34],"to":[38,55,83],"better":[39],"extract":[40],"high-order":[42],"features":[43,58],"of":[44,59],"forecasting.":[48],"Meanwhile,":[49],"mechanism":[52],"introduced":[54],"capture":[56],"temporal":[57],"long":[61],"sequence,":[62],"and":[63],"forecast":[67],"results":[68,91],"under":[69],"different":[70],"quantiles":[71],"can":[72,102],"be":[73],"obtained.":[74],"Finally,":[75],"Gaussian":[77],"kernel":[78],"smoothing":[79],"used":[82],"obtain":[84],"density.":[89],"Simulation":[90],"actual":[93],"data":[96],"show":[97],"that":[98],"model":[101],"provide":[103],"more":[105],"accurate":[106],"long-term":[107],"performance":[113],"than":[114],"previous":[116],"methods.":[117]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
