{"id":"https://openalex.org/W4361855447","doi":"https://doi.org/10.1109/tii.2023.3262856","title":"SFR Modeling for Hybrid Power Systems Based on Deep Transfer Learning","display_name":"SFR Modeling for Hybrid Power Systems Based on Deep Transfer Learning","publication_year":2023,"publication_date":"2023-03-30","ids":{"openalex":"https://openalex.org/W4361855447","doi":"https://doi.org/10.1109/tii.2023.3262856"},"language":"en","primary_location":{"id":"doi:10.1109/tii.2023.3262856","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tii.2023.3262856","pdf_url":null,"source":{"id":"https://openalex.org/S184777250","display_name":"IEEE Transactions on Industrial Informatics","issn_l":"1551-3203","issn":["1551-3203","1941-0050"],"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 Industrial Informatics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://eprints.whiterose.ac.uk/197711/3/ALL_TII-23-0045_202303241652_final.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101864715","display_name":"Jianhua Zhang","orcid":"https://orcid.org/0000-0001-8828-3751"},"institutions":[{"id":"https://openalex.org/I153473198","display_name":"North China Electric Power University","ror":"https://ror.org/04qr5t414","country_code":"CN","type":"education","lineage":["https://openalex.org/I153473198"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jianhua Zhang","raw_affiliation_strings":["State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing, China","institution_ids":["https://openalex.org/I153473198"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070435655","display_name":"Yongyue Wang","orcid":"https://orcid.org/0000-0002-8370-9597"},"institutions":[{"id":"https://openalex.org/I153473198","display_name":"North China Electric Power University","ror":"https://ror.org/04qr5t414","country_code":"CN","type":"education","lineage":["https://openalex.org/I153473198"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yongyue Wang","raw_affiliation_strings":["State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing, China","institution_ids":["https://openalex.org/I153473198"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101827606","display_name":"Hongrui Li","orcid":"https://orcid.org/0000-0002-6629-1555"},"institutions":[{"id":"https://openalex.org/I153473198","display_name":"North China Electric Power University","ror":"https://ror.org/04qr5t414","country_code":"CN","type":"education","lineage":["https://openalex.org/I153473198"]},{"id":"https://openalex.org/I4210112997","display_name":"State Power Investment Corporation (China)","ror":"https://ror.org/02182rz34","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210112997"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongrui Li","raw_affiliation_strings":["North China Electric Power University, Beijing, China","State Power Investment Corporation Limited, Beijing, China"],"affiliations":[{"raw_affiliation_string":"North China Electric Power University, Beijing, China","institution_ids":["https://openalex.org/I153473198"]},{"raw_affiliation_string":"State Power Investment Corporation Limited, Beijing, China","institution_ids":["https://openalex.org/I4210112997"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103013924","display_name":"Guiping Zhou","orcid":"https://orcid.org/0000-0002-3351-8098"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guiping Zhou","raw_affiliation_strings":["State Grid Liaoning Electric Power Supply Company, Ltd., Shenyang, China"],"affiliations":[{"raw_affiliation_string":"State Grid Liaoning Electric Power Supply Company, Ltd., Shenyang, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100365204","display_name":"Bin Li","orcid":"https://orcid.org/0000-0002-7088-8274"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bin Li","raw_affiliation_strings":["State Grid Liaoning Electric Power Supply Company, Ltd., Shenyang, China"],"affiliations":[{"raw_affiliation_string":"State Grid Liaoning Electric Power Supply Company, Ltd., Shenyang, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115044976","display_name":"Lei Wang","orcid":"https://orcid.org/0009-0001-0134-429X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lei Wang","raw_affiliation_strings":["State Grid Liaoning Electric Power Supply Company, Ltd., Shenyang, China"],"affiliations":[{"raw_affiliation_string":"State Grid Liaoning Electric Power Supply Company, Ltd., Shenyang, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100456957","display_name":"Kang Li","orcid":"https://orcid.org/0000-0001-6657-0522"},"institutions":[{"id":"https://openalex.org/I130828816","display_name":"University of Leeds","ror":"https://ror.org/024mrxd33","country_code":"GB","type":"education","lineage":["https://openalex.org/I130828816"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Kang Li","raw_affiliation_strings":["School of Electronic and Electrical Engineering, University of Leeds, Leeds, U.K"],"affiliations":[{"raw_affiliation_string":"School of Electronic and Electrical Engineering, University of Leeds, Leeds, U.K","institution_ids":["https://openalex.org/I130828816"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5101864715"],"corresponding_institution_ids":["https://openalex.org/I153473198"],"apc_list":null,"apc_paid":null,"fwci":1.4709,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.81602911,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"20","issue":"1","first_page":"399","last_page":"410"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9991000294685364,"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":0.9991000294685364,"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/T12277","display_name":"Frequency Control in Power Systems","score":0.9958999752998352,"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/T11941","display_name":"Power System Reliability and Maintenance","score":0.9929999709129333,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6711723804473877},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.6017992496490479},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.5119744539260864},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.46687906980514526},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.4628424346446991},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.45444995164871216},{"id":"https://openalex.org/keywords/wind-power","display_name":"Wind power","score":0.44847816228866577},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4237963557243347},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4141552150249481},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4056103527545929},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33712446689605713},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.2328713834285736},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12045779824256897},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.11453455686569214}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6711723804473877},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.6017992496490479},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.5119744539260864},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.46687906980514526},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.4628424346446991},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.45444995164871216},{"id":"https://openalex.org/C78600449","wikidata":"https://www.wikidata.org/wiki/Q43302","display_name":"Wind power","level":2,"score":0.44847816228866577},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4237963557243347},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4141552150249481},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4056103527545929},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33712446689605713},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.2328713834285736},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12045779824256897},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.11453455686569214},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"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/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tii.2023.3262856","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tii.2023.3262856","pdf_url":null,"source":{"id":"https://openalex.org/S184777250","display_name":"IEEE Transactions on Industrial Informatics","issn_l":"1551-3203","issn":["1551-3203","1941-0050"],"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 Industrial Informatics","raw_type":"journal-article"},{"id":"pmh:oai:eprints.whiterose.ac.uk:197711","is_oa":true,"landing_page_url":null,"pdf_url":"https://eprints.whiterose.ac.uk/197711/3/ALL_TII-23-0045_202303241652_final.pdf","source":{"id":"https://openalex.org/S4306400854","display_name":"White Rose Research Online (University of Leeds, The University of Sheffield, University of York)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I2800616092","host_organization_name":"White Rose University Consortium","host_organization_lineage":["https://openalex.org/I2800616092"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Article"}],"best_oa_location":{"id":"pmh:oai:eprints.whiterose.ac.uk:197711","is_oa":true,"landing_page_url":null,"pdf_url":"https://eprints.whiterose.ac.uk/197711/3/ALL_TII-23-0045_202303241652_final.pdf","source":{"id":"https://openalex.org/S4306400854","display_name":"White Rose Research Online (University of Leeds, The University of Sheffield, University of York)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I2800616092","host_organization_name":"White Rose University Consortium","host_organization_lineage":["https://openalex.org/I2800616092"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Article"},"sustainable_development_goals":[{"score":0.9100000262260437,"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4361855447.pdf"},"referenced_works_count":36,"referenced_works":["https://openalex.org/W1565956482","https://openalex.org/W2027286467","https://openalex.org/W2028300674","https://openalex.org/W2105653794","https://openalex.org/W2110810023","https://openalex.org/W2126885794","https://openalex.org/W2165698076","https://openalex.org/W2197662544","https://openalex.org/W2313516183","https://openalex.org/W2343257364","https://openalex.org/W2356939382","https://openalex.org/W2372638138","https://openalex.org/W2385556282","https://openalex.org/W2389532982","https://openalex.org/W2390654433","https://openalex.org/W2489292218","https://openalex.org/W2560162973","https://openalex.org/W2737654582","https://openalex.org/W2779000226","https://openalex.org/W2792046648","https://openalex.org/W2887782657","https://openalex.org/W2898078893","https://openalex.org/W2898375427","https://openalex.org/W2900529838","https://openalex.org/W2963565363","https://openalex.org/W2964250810","https://openalex.org/W2997679862","https://openalex.org/W2999496784","https://openalex.org/W3025421528","https://openalex.org/W3045812302","https://openalex.org/W3091421764","https://openalex.org/W3100270150","https://openalex.org/W3103064492","https://openalex.org/W3131468421","https://openalex.org/W3210341941","https://openalex.org/W6688325169"],"related_works":["https://openalex.org/W4298287631","https://openalex.org/W2953061907","https://openalex.org/W3032952384","https://openalex.org/W3034302643","https://openalex.org/W1847088711","https://openalex.org/W4225394202","https://openalex.org/W3036642985","https://openalex.org/W2964335273","https://openalex.org/W2964006806","https://openalex.org/W3171384686"],"abstract_inverted_index":{"A":[0,54],"deep":[1],"transfer":[2],"learning":[3],"method":[4,152],"is":[5,41,58,82,102],"presented":[6],"for":[7,161],"establishing":[8],"the":[9,31,37,45,62,66,72,78,86,89,98,109,117,124,136,147],"aggregated":[10],"system":[11],"frequency":[12],"response":[13],"(SFR)":[14],"model":[15,57,101,122,160],"of":[16,36,88,123],"wind-thermal":[17,163],"hybrid":[18],"power":[19],"systems":[20],"(HPSs).":[21],"In":[22],"order":[23],"to":[24,43,84,108,116],"deal":[25],"with":[26,165],"nonlinearities":[27],"and":[28,93,112,157,169],"non-Gaussian":[29],"disturbances,":[30],"quadratic":[32],"survival":[33],"information":[34],"potential":[35],"squared":[38],"identification":[39],"error":[40],"employed":[42],"construct":[44],"performance":[46],"index":[47],"when":[48],"training":[49],"recurrent":[50,110],"neural":[51],"networks":[52],"(RNNs).":[53],"pretrained":[55,99,140],"SFR":[56,100,121,141,159],"then":[59],"obtained":[60],"by":[61,104],"improved":[63],"RNNs":[64],"using":[65],"source":[67,138],"domain":[68,139],"data":[69,149],"collected":[70],"from":[71],"HPS":[73,90,125,164],"in":[74,91,127],"historical":[75,92],"scenarios.":[76,95],"Subsequently,":[77],"maximum":[79],"mean":[80],"difference":[81],"utilized":[83],"test":[85],"similarity":[87],"current":[94,128],"After":[96],"that,":[97],"fine-tuned":[103],"adding":[105],"some":[106],"nodes":[107],"layer":[111],"a":[113,162],"functional":[114],"link":[115],"input":[118],"layer.":[119],"The":[120],"operating":[126],"scenario":[129],"can,":[130],"then,":[131],"be":[132],"built":[133],"based":[134],"on":[135],"transferred":[137],"model.":[142],"Simulation":[143],"results":[144],"illustrate":[145],"that":[146],"proposed":[148],"driven":[150],"modeling":[151],"can":[153],"obtain":[154],"accurate,":[155],"effective":[156],"timely":[158],"different":[166],"wind":[167],"speeds":[168],"load":[170],"disturbances.":[171]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}