{"id":"https://openalex.org/W4290996374","doi":"https://doi.org/10.1109/icc45855.2022.9838983","title":"Short-term Load Forecasting with Deep Boosting Transfer Regression","display_name":"Short-term Load Forecasting with Deep Boosting Transfer Regression","publication_year":2022,"publication_date":"2022-05-16","ids":{"openalex":"https://openalex.org/W4290996374","doi":"https://doi.org/10.1109/icc45855.2022.9838983"},"language":"en","primary_location":{"id":"doi:10.1109/icc45855.2022.9838983","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icc45855.2022.9838983","pdf_url":null,"source":{"id":"https://openalex.org/S4363607711","display_name":"ICC 2022 - IEEE International Conference on Communications","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":"ICC 2022 - IEEE International Conference on Communications","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/A5022267948","display_name":"Di Wu","orcid":"https://orcid.org/0000-0001-7419-9903"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Di Wu","raw_affiliation_strings":["Samsung AI Center,Montreal,Canada","Samsung AI Center, Montreal, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Samsung AI Center,Montreal,Canada","institution_ids":[]},{"raw_affiliation_string":"Samsung AI Center, Montreal, Canada","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000944002","display_name":"Yi Xu","orcid":"https://orcid.org/0000-0001-6722-5587"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yi Tian Xu","raw_affiliation_strings":["Samsung AI Center,Montreal,Canada","Samsung AI Center, Montreal, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Samsung AI Center,Montreal,Canada","institution_ids":[]},{"raw_affiliation_string":"Samsung AI Center, Montreal, Canada","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058737924","display_name":"Michael Jenkin","orcid":"https://orcid.org/0000-0002-2969-0012"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Michael Jenkin","raw_affiliation_strings":["Samsung AI Center,Montreal,Canada","Samsung AI Center, Montreal, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Samsung AI Center,Montreal,Canada","institution_ids":[]},{"raw_affiliation_string":"Samsung AI Center, Montreal, Canada","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083959011","display_name":"Ju Wang","orcid":"https://orcid.org/0000-0001-8026-9787"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ju Wang","raw_affiliation_strings":["Samsung AI Center,Montreal,Canada","Samsung AI Center, Montreal, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Samsung AI Center,Montreal,Canada","institution_ids":[]},{"raw_affiliation_string":"Samsung AI Center, Montreal, Canada","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100455139","display_name":"Hang Li","orcid":"https://orcid.org/0000-0002-6221-6195"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hang Li","raw_affiliation_strings":["Samsung AI Center,Montreal,Canada","Samsung AI Center, Montreal, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Samsung AI Center,Montreal,Canada","institution_ids":[]},{"raw_affiliation_string":"Samsung AI Center, Montreal, Canada","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100372152","display_name":"Xue Liu","orcid":"https://orcid.org/0000-0001-5252-3442"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xue Liu","raw_affiliation_strings":["Samsung AI Center,Montreal,Canada","Samsung AI Center, Montreal, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Samsung AI Center,Montreal,Canada","institution_ids":[]},{"raw_affiliation_string":"Samsung AI Center, Montreal, Canada","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5075441381","display_name":"Gregory Dudek","orcid":"https://orcid.org/0000-0001-5040-4925"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gregory Dudek","raw_affiliation_strings":["Samsung AI Center,Montreal,Canada","Samsung AI Center, Montreal, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Samsung AI Center,Montreal,Canada","institution_ids":[]},{"raw_affiliation_string":"Samsung AI Center, Montreal, Canada","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":7,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.8705,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.9253746,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"5530","last_page":"5536"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9926000237464905,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9886999726295471,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7317661643028259},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.723092794418335},{"id":"https://openalex.org/keywords/gradient-boosting","display_name":"Gradient boosting","score":0.6603272557258606},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.6549944877624512},{"id":"https://openalex.org/keywords/electricity","display_name":"Electricity","score":0.540002703666687},{"id":"https://openalex.org/keywords/smart-grid","display_name":"Smart grid","score":0.534141480922699},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5296148061752319},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.5015192031860352},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.45078906416893005},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4444187879562378},{"id":"https://openalex.org/keywords/probabilistic-forecasting","display_name":"Probabilistic forecasting","score":0.4375765919685364},{"id":"https://openalex.org/keywords/grid","display_name":"Grid","score":0.41420403122901917},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.412118136882782},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.17831194400787354},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.08200246095657349}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7317661643028259},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.723092794418335},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.6603272557258606},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.6549944877624512},{"id":"https://openalex.org/C206658404","wikidata":"https://www.wikidata.org/wiki/Q12725","display_name":"Electricity","level":2,"score":0.540002703666687},{"id":"https://openalex.org/C10558101","wikidata":"https://www.wikidata.org/wiki/Q689855","display_name":"Smart grid","level":2,"score":0.534141480922699},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5296148061752319},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.5015192031860352},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45078906416893005},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4444187879562378},{"id":"https://openalex.org/C122282355","wikidata":"https://www.wikidata.org/wiki/Q7246855","display_name":"Probabilistic forecasting","level":3,"score":0.4375765919685364},{"id":"https://openalex.org/C187691185","wikidata":"https://www.wikidata.org/wiki/Q2020720","display_name":"Grid","level":2,"score":0.41420403122901917},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.412118136882782},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.17831194400787354},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.08200246095657349},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"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/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","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/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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icc45855.2022.9838983","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icc45855.2022.9838983","pdf_url":null,"source":{"id":"https://openalex.org/S4363607711","display_name":"ICC 2022 - IEEE International Conference on Communications","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":"ICC 2022 - IEEE International Conference on Communications","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.4399999976158142}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":46,"referenced_works":["https://openalex.org/W1511334422","https://openalex.org/W2064675550","https://openalex.org/W2069143585","https://openalex.org/W2072957652","https://openalex.org/W2126831543","https://openalex.org/W2143753889","https://openalex.org/W2165698076","https://openalex.org/W2194775991","https://openalex.org/W2472819217","https://openalex.org/W2555581413","https://openalex.org/W2581582336","https://openalex.org/W2601450892","https://openalex.org/W2604099671","https://openalex.org/W2604763608","https://openalex.org/W2754252319","https://openalex.org/W2766447205","https://openalex.org/W2775854174","https://openalex.org/W2801549487","https://openalex.org/W2808125608","https://openalex.org/W2902276651","https://openalex.org/W2904943247","https://openalex.org/W2907010510","https://openalex.org/W2907277777","https://openalex.org/W2907395574","https://openalex.org/W2910531685","https://openalex.org/W2949891635","https://openalex.org/W2953958347","https://openalex.org/W2963341924","https://openalex.org/W2963855133","https://openalex.org/W2963864421","https://openalex.org/W2965525929","https://openalex.org/W2978631110","https://openalex.org/W2990021894","https://openalex.org/W3012910746","https://openalex.org/W3047961354","https://openalex.org/W3176181793","https://openalex.org/W3187911394","https://openalex.org/W6630019171","https://openalex.org/W6668722396","https://openalex.org/W6684921986","https://openalex.org/W6717697761","https://openalex.org/W6720057410","https://openalex.org/W6735236233","https://openalex.org/W6736057607","https://openalex.org/W6752783944","https://openalex.org/W6763659292"],"related_works":["https://openalex.org/W2967733078","https://openalex.org/W3137904399","https://openalex.org/W4310492845","https://openalex.org/W2885778889","https://openalex.org/W2766514146","https://openalex.org/W2885516856","https://openalex.org/W4289703016","https://openalex.org/W4310224730","https://openalex.org/W3094138326","https://openalex.org/W1985505753"],"abstract_inverted_index":{"With":[0],"the":[1,8,18,29,46,51,106,140,161,176],"increasing":[2],"popularity":[3],"of":[4,11,50,76,108,120],"electric":[5,67],"vehicles":[6],"and":[7,48,153],"growing":[9],"trend":[10],"working":[12],"from":[13],"home,":[14],"electricity":[15],"consumption":[16],"in":[17,62],"residential":[19,37,171],"sector":[20],"is":[21,40,151],"expected":[22],"to":[23,25,92,143,159],"continue":[24],"grow":[26],"rapidly":[27],"over":[28],"next":[30],"few":[31],"years.":[32],"As":[33],"a":[34,73,94,118],"consequence,":[35],"short-term":[36,66],"load":[38,68,82],"forecasting":[39,83,96,182],"becoming":[41],"even":[42],"more":[43],"vital":[44],"for":[45,155,163],"reliability":[47],"sustainability":[49],"smart":[52],"grid.":[53],"Although":[54],"deep":[55,121],"learning":[56,111],"models":[57,71,123,138],"have":[58,88],"shown":[59],"impressive":[60],"success":[61],"different":[63],"areas":[64],"including":[65],"forecasting,":[69],"such":[70],"require":[72],"large":[74],"amount":[75],"training":[77,90],"data.":[78,132],"For":[79],"many":[80],"real-world":[81,170],"cases,":[84],"we":[85,101],"may":[86],"not":[87],"enough":[89],"data":[91,172],"learn":[93],"reliable":[95],"model.":[97],"In":[98],"this":[99,103],"paper,":[100],"address":[102],"challenge":[104],"through":[105],"use":[107],"boosting-based":[109],"transfer":[110,135,149],"with":[112],"multiple":[113],"sources.":[114],"We":[115,133],"first":[116],"train":[117],"set":[119],"regression":[122],"on":[124,169],"source":[125],"houses":[126],"that":[127,175],"can":[128,179],"provide":[129],"relatively":[130],"abundant":[131],"then":[134],"these":[136],"learned":[137],"via":[139],"boosting":[141],"framework":[142],"support":[144],"data-scarce":[145],"target":[146,157],"houses.":[147],"The":[148],"process":[150],"selective":[152],"customized":[154],"each":[156],"house":[158],"minimize":[160],"potential":[162],"negative":[164],"transfer.":[165],"Experimental":[166],"results,":[167],"based":[168],"sets,":[173],"show":[174],"proposed":[177],"method":[178],"significantly":[180],"improve":[181],"accuracy.":[183]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":3}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}