{"id":"https://openalex.org/W2913651970","doi":"https://doi.org/10.1109/access.2019.2895604","title":"Deep Learning for Daily Peak Load Forecasting\u2013A Novel Gated Recurrent Neural Network Combining Dynamic Time Warping","display_name":"Deep Learning for Daily Peak Load Forecasting\u2013A Novel Gated Recurrent Neural Network Combining Dynamic Time Warping","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2913651970","doi":"https://doi.org/10.1109/access.2019.2895604","mag":"2913651970"},"language":"en","primary_location":{"id":"doi:10.1109/access.2019.2895604","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2895604","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08629072.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08629072.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101652711","display_name":"Zeyuan Yu","orcid":"https://orcid.org/0000-0002-6462-8417"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zeyuan Yu","raw_affiliation_strings":["School of Electric Power Engineering, South China University of Technology, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electric Power Engineering, South China University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001091807","display_name":"Zhewen Niu","orcid":"https://orcid.org/0000-0002-4859-1511"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhewen Niu","raw_affiliation_strings":["School of Electric Power Engineering, South China University of Technology, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electric Power Engineering, South China University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016974124","display_name":"Wenhu Tang","orcid":"https://orcid.org/0009-0005-5786-4568"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenhu Tang","raw_affiliation_strings":["School of Electric Power Engineering, South China University of Technology, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0003-1823-2355","affiliations":[{"raw_affiliation_string":"School of Electric Power Engineering, South China University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5016939864","display_name":"Qinghua Wu","orcid":"https://orcid.org/0000-0002-0598-8367"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qinghua Wu","raw_affiliation_strings":["School of Electric Power Engineering, South China University of Technology, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electric Power Engineering, South China University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I90610280"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5101652711"],"corresponding_institution_ids":["https://openalex.org/I90610280"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":10.2916,"has_fulltext":true,"cited_by_count":140,"citation_normalized_percentile":{"value":0.98726535,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":"7","issue":null,"first_page":"17184","last_page":"17194"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9991999864578247,"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"}},"topics":[{"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9984999895095825,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.9937000274658203,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/dynamic-time-warping","display_name":"Dynamic time warping","score":0.8488403558731079},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7288303375244141},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6318727135658264},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.6310102939605713},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5820331573486328},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5695372819900513},{"id":"https://openalex.org/keywords/image-warping","display_name":"Image warping","score":0.549081563949585},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.38430678844451904},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3486486077308655},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.327449232339859}],"concepts":[{"id":"https://openalex.org/C88516994","wikidata":"https://www.wikidata.org/wiki/Q1268863","display_name":"Dynamic time warping","level":2,"score":0.8488403558731079},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7288303375244141},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6318727135658264},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.6310102939605713},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5820331573486328},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5695372819900513},{"id":"https://openalex.org/C157202957","wikidata":"https://www.wikidata.org/wiki/Q1659609","display_name":"Image warping","level":2,"score":0.549081563949585},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38430678844451904},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3486486077308655},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.327449232339859}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2019.2895604","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2895604","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08629072.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:e6ed9f2133d34e9683e6013427fd4239","is_oa":false,"landing_page_url":"https://doaj.org/article/e6ed9f2133d34e9683e6013427fd4239","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 7, Pp 17184-17194 (2019)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2019.2895604","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2895604","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08629072.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.7900000214576721,"display_name":"Peace, Justice and strong institutions"}],"awards":[{"id":"https://openalex.org/G4322368975","display_name":null,"funder_award_id":"51477054","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5130899290","display_name":null,"funder_award_id":"X2DLD2181850","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2913651970.pdf","grobid_xml":"https://content.openalex.org/works/W2913651970.grobid-xml"},"referenced_works_count":49,"referenced_works":["https://openalex.org/W23725892","https://openalex.org/W1499864241","https://openalex.org/W1511239192","https://openalex.org/W1908962573","https://openalex.org/W1964984358","https://openalex.org/W1988566200","https://openalex.org/W1993882792","https://openalex.org/W2001086848","https://openalex.org/W2004225242","https://openalex.org/W2010680143","https://openalex.org/W2015861736","https://openalex.org/W2019949418","https://openalex.org/W2032161710","https://openalex.org/W2044186388","https://openalex.org/W2076462394","https://openalex.org/W2123469553","https://openalex.org/W2133614187","https://openalex.org/W2136922672","https://openalex.org/W2137089646","https://openalex.org/W2137383673","https://openalex.org/W2138857742","https://openalex.org/W2143612262","https://openalex.org/W2150291692","https://openalex.org/W2152510616","https://openalex.org/W2154997814","https://openalex.org/W2157331557","https://openalex.org/W2160815625","https://openalex.org/W2168138569","https://openalex.org/W2384495648","https://openalex.org/W2470209168","https://openalex.org/W2504972167","https://openalex.org/W2520016695","https://openalex.org/W2525733385","https://openalex.org/W2552749018","https://openalex.org/W2565448738","https://openalex.org/W2591426394","https://openalex.org/W2597866042","https://openalex.org/W2601171548","https://openalex.org/W2721931776","https://openalex.org/W2748388862","https://openalex.org/W2754252319","https://openalex.org/W2762337157","https://openalex.org/W2764791077","https://openalex.org/W2766447205","https://openalex.org/W2784168210","https://openalex.org/W3122046970","https://openalex.org/W4298861651","https://openalex.org/W6647763306","https://openalex.org/W6680300913"],"related_works":["https://openalex.org/W1670332068","https://openalex.org/W2095618524","https://openalex.org/W2347413598","https://openalex.org/W2330863229","https://openalex.org/W71572444","https://openalex.org/W1997383766","https://openalex.org/W2350336482","https://openalex.org/W2340348677","https://openalex.org/W3008584592","https://openalex.org/W2130799169"],"abstract_inverted_index":{"Daily":[0],"peak":[1,20,59,132],"load":[2,21,60,73,80,88,118],"forecasting":[3],"is":[4,22,66,101,127,137],"an":[5],"essential":[6],"tool":[7],"for":[8,40,56],"decision":[9],"making":[10],"in":[11,79,177],"power":[12],"system":[13],"operation":[14],"and":[15,26,90,145],"planning.":[16],"However,":[17],"the":[18,41,70,84,87,91,97,105,110,140,148,152,163,174],"daily":[19,58,131],"a":[23,45,121],"nonlinear,":[24],"nonstationary,":[25],"volatile":[27],"time":[28,53],"series,":[29],"which":[30,75],"makes":[31],"it":[32],"difficult":[33],"to":[34,68,108,112,129],"be":[35],"forecasted":[36],"accurately.":[37],"This":[38],"paper,":[39],"first":[42,102],"time,":[43],"proposes":[44],"bespoke":[46],"gated":[47,123],"recurrent":[48,124],"neural":[49,125],"network":[50,126],"combining":[51],"dynamic":[52],"warping":[54],"(DTW)":[55],"accurate":[57],"forecasting.":[61],"The":[62,134,158],"shape-based":[63],"DTW":[64],"distance":[65],"used":[67],"match":[69],"most":[71],"similar":[72],"curve,":[74],"can":[76],"capture":[77],"trends":[78],"changes.":[81],"By":[82],"analyzing":[83],"relationship":[85],"between":[86],"curve":[89],"cycle":[92],"of":[93,151],"human":[94],"social":[95],"activities,":[96],"some-hot":[98],"encoding":[99],"scheme":[100],"applied":[103],"on":[104,117,139,147,155],"discrete":[106],"variables":[107],"expand":[109],"features":[111],"further":[113],"characterize":[114],"their":[115],"impact":[116],"curves.":[119],"Then,":[120],"three-layer":[122],"developed":[128],"forecast":[130],"load.":[133],"proposed":[135,164],"algorithm":[136,165],"implemented":[138],"Theano":[141],"deep":[142],"learning":[143],"platform":[144],"tested":[146],"loaded":[149],"dataset":[150,176],"European":[153],"Network":[154],"Intelligent":[156],"Technologies.":[157],"simulation":[159],"results":[160,168],"show":[161],"that":[162],"achieves":[166],"satisfactory":[167],"compared":[169],"with":[170],"other":[171],"algorithms":[172],"using":[173],"same":[175],"this":[178],"paper.":[179]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":10},{"year":2024,"cited_by_count":25},{"year":2023,"cited_by_count":18},{"year":2022,"cited_by_count":28},{"year":2021,"cited_by_count":25},{"year":2020,"cited_by_count":20},{"year":2019,"cited_by_count":12}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-10T00:00:00"}