{"id":"https://openalex.org/W4287883000","doi":"https://doi.org/10.1109/isie51582.2022.9831704","title":"Ultra-Short- Term Load Forecasting Based on Convolutional-LSTM Hybrid Networks","display_name":"Ultra-Short- Term Load Forecasting Based on Convolutional-LSTM Hybrid Networks","publication_year":2022,"publication_date":"2022-06-01","ids":{"openalex":"https://openalex.org/W4287883000","doi":"https://doi.org/10.1109/isie51582.2022.9831704"},"language":"en","primary_location":{"id":"doi:10.1109/isie51582.2022.9831704","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isie51582.2022.9831704","pdf_url":null,"source":{"id":"https://openalex.org/S4363605663","display_name":"2022 IEEE 31st International Symposium on Industrial Electronics (ISIE)","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":"2022 IEEE 31st International Symposium on Industrial Electronics (ISIE)","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/A5010149552","display_name":"Hanjiang Dong","orcid":"https://orcid.org/0000-0002-9248-3868"},"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":"Hanjiang Dong","raw_affiliation_strings":["School of Electric Power Engineering, South China University of Technology,Guangzhou,China","School of Electric Power Engineering, South China University of Technology, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Electric Power Engineering, South China University of Technology,Guangzhou,China","institution_ids":["https://openalex.org/I90610280"]},{"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/A5025787212","display_name":"Jizhong Zhu","orcid":"https://orcid.org/0000-0001-5681-1361"},"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":"Jizhong Zhu","raw_affiliation_strings":["School of Electric Power Engineering, South China University of Technology,Guangzhou,China","School of Electric Power Engineering, South China University of Technology, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Electric Power Engineering, South China University of Technology,Guangzhou,China","institution_ids":["https://openalex.org/I90610280"]},{"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/A5101593026","display_name":"Shenglin Li","orcid":"https://orcid.org/0000-0002-6614-4301"},"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":"Shenglin Li","raw_affiliation_strings":["School of Electric Power Engineering, South China University of Technology,Guangzhou,China","School of Electric Power Engineering, South China University of Technology, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Electric Power Engineering, South China University of Technology,Guangzhou,China","institution_ids":["https://openalex.org/I90610280"]},{"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/A5004218316","display_name":"Tengyan Luo","orcid":null},"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":"Tengyan Luo","raw_affiliation_strings":["School of Electric Power Engineering, South China University of Technology,Guangzhou,China","School of Electric Power Engineering, South China University of Technology, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Electric Power Engineering, South China University of Technology,Guangzhou,China","institution_ids":["https://openalex.org/I90610280"]},{"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/A5100624344","display_name":"Hong Li","orcid":"https://orcid.org/0000-0003-2602-6762"},"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":"Hong Li","raw_affiliation_strings":["School of Electric Power Engineering, South China University of Technology,Guangzhou,China","School of Electric Power Engineering, South China University of Technology, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Electric Power Engineering, South China University of Technology,Guangzhou,China","institution_ids":["https://openalex.org/I90610280"]},{"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/A5077093461","display_name":"Yanting Huang","orcid":"https://orcid.org/0000-0002-3024-8942"},"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":"Yanting Huang","raw_affiliation_strings":["School of Electric Power Engineering, South China University of Technology,Guangzhou,China","School of Electric Power Engineering, South China University of Technology, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Electric Power Engineering, South China University of Technology,Guangzhou,China","institution_ids":["https://openalex.org/I90610280"]},{"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":6,"corresponding_author_ids":["https://openalex.org/A5010149552"],"corresponding_institution_ids":["https://openalex.org/I90610280"],"apc_list":null,"apc_paid":null,"fwci":1.2876,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.76260029,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"142","last_page":"148"},"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9976999759674072,"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"}},{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9921000003814697,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7837562561035156},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6838707327842712},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.631677508354187},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6238878965377808},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5929343104362488},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.5620164275169373},{"id":"https://openalex.org/keywords/long-short-term-memory","display_name":"Long short term memory","score":0.5392633080482483},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.5122250914573669},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.491991251707077},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.49191856384277344},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4824657738208771},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.48115789890289307},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4653435945510864},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.33585864305496216},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.08741438388824463}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7837562561035156},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6838707327842712},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.631677508354187},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6238878965377808},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5929343104362488},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.5620164275169373},{"id":"https://openalex.org/C133488467","wikidata":"https://www.wikidata.org/wiki/Q6673524","display_name":"Long short term memory","level":4,"score":0.5392633080482483},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.5122250914573669},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.491991251707077},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.49191856384277344},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4824657738208771},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.48115789890289307},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4653435945510864},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33585864305496216},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.08741438388824463},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isie51582.2022.9831704","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isie51582.2022.9831704","pdf_url":null,"source":{"id":"https://openalex.org/S4363605663","display_name":"2022 IEEE 31st International Symposium on Industrial Electronics (ISIE)","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":"2022 IEEE 31st International Symposium on Industrial Electronics (ISIE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8899999856948853,"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy"}],"awards":[{"id":"https://openalex.org/G1173516763","display_name":null,"funder_award_id":"2021A1515010419","funder_id":"https://openalex.org/F4320321921","funder_display_name":"Natural Science Foundation of Guangdong Province"},{"id":"https://openalex.org/G5760645727","display_name":null,"funder_award_id":"52177087","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"},{"id":"https://openalex.org/F4320321921","display_name":"Natural Science Foundation of Guangdong Province","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1711412747","https://openalex.org/W1924770834","https://openalex.org/W2023419040","https://openalex.org/W2064675550","https://openalex.org/W2192896536","https://openalex.org/W2296521892","https://openalex.org/W2360320825","https://openalex.org/W2884378264","https://openalex.org/W2889386826","https://openalex.org/W2900815210","https://openalex.org/W2919115771","https://openalex.org/W2969518884","https://openalex.org/W2985364839","https://openalex.org/W2998186794","https://openalex.org/W3087179847","https://openalex.org/W3091931335","https://openalex.org/W3204946575","https://openalex.org/W6640212811","https://openalex.org/W6802017319","https://openalex.org/W6840339042"],"related_works":["https://openalex.org/W2912153778","https://openalex.org/W4387163678","https://openalex.org/W4288108708","https://openalex.org/W2973430807","https://openalex.org/W4385280324","https://openalex.org/W2890685186","https://openalex.org/W2984436043","https://openalex.org/W4390245176","https://openalex.org/W2912831041","https://openalex.org/W3173606726"],"abstract_inverted_index":{"The":[0],"big":[1],"data":[2,23],"era":[3],"in":[4,103],"energy":[5],"systems":[6],"is":[7],"coming":[8],"with":[9,111],"the":[10,74,84,97,100],"popularity":[11],"of":[12,99,105],"advanced":[13],"metering":[14],"architectures":[15],"such":[16],"as":[17],"smart":[18],"meters,":[19],"providing":[20],"much":[21],"more":[22],"sources":[24],"for":[25],"ultra-short-term":[26,61],"load":[27,52,62],"forecasting.":[28],"In":[29,55],"this":[30,56],"context,":[31],"data-drive":[32],"techniques,":[33],"e.g.,":[34],"long":[35],"short-term":[36],"memory":[37],"(LSTM)":[38],"networks":[39,43,72],"and":[40,69,80,83,108,120],"convolutional":[41],"neural":[42,113],"(CNNs)":[44],"have":[45,95],"gradually":[46],"become":[47],"essential":[48],"methodology":[49],"to":[50],"improve":[51],"forecasting":[53,63],"accuracy.":[54],"paper,":[57],"we":[58],"identify":[59],"an":[60],"model":[64,102],"based":[65],"on":[66],"LSTM":[67,71,118],"net-works":[68],"CNNs.":[70,92],"reflect":[73],"nonlinear":[75],"relation":[76],"between":[77],"influential":[78,85],"features":[79,86],"future":[81],"loads,":[82],"are":[87],"extracted":[88],"implicitly":[89],"from":[90],"special":[91],"Case":[93],"studies":[94],"verified":[96],"effectiveness":[98],"proposed":[101],"terms":[104],"both":[106],"accuracy":[107],"efficiency,":[109],"comparing":[110],"shallow":[112],"networks,":[114,119],"separate":[115,117],"CNNs,":[116],"sepa-rate":[121],"gated":[122],"recurrent":[123],"unit":[124],"(GRU)":[125],"networks.":[126]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}