{"id":"https://openalex.org/W4391215980","doi":"https://doi.org/10.1109/tai.2024.3358795","title":"An Automated Few-Shot Learning for Time-Series Forecasting in Smart Grid Under Data Scarcity","display_name":"An Automated Few-Shot Learning for Time-Series Forecasting in Smart Grid Under Data Scarcity","publication_year":2024,"publication_date":"2024-01-25","ids":{"openalex":"https://openalex.org/W4391215980","doi":"https://doi.org/10.1109/tai.2024.3358795"},"language":"en","primary_location":{"id":"doi:10.1109/tai.2024.3358795","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tai.2024.3358795","pdf_url":null,"source":{"id":"https://openalex.org/S4210169448","display_name":"IEEE Transactions on Artificial Intelligence","issn_l":"2691-4581","issn":["2691-4581"],"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 Artificial Intelligence","raw_type":"journal-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/A5033614477","display_name":"Jiangjiao Xu","orcid":"https://orcid.org/0000-0002-7883-705X"},"institutions":[{"id":"https://openalex.org/I23632641","display_name":"Shanghai University of Electric Power","ror":"https://ror.org/02w4tny03","country_code":"CN","type":"education","lineage":["https://openalex.org/I23632641"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jiangjiao Xu","raw_affiliation_strings":["Department of Electrical Engineering, Shanghai University of Electric Power, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Shanghai University of Electric Power, Shanghai, China","institution_ids":["https://openalex.org/I23632641"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100343450","display_name":"Ke Li","orcid":"https://orcid.org/0000-0001-7200-4244"},"institutions":[{"id":"https://openalex.org/I23923803","display_name":"University of Exeter","ror":"https://ror.org/03yghzc09","country_code":"GB","type":"education","lineage":["https://openalex.org/I23923803"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Ke Li","raw_affiliation_strings":["Department of Computer Science, University of Exeter, Exeter, U.K"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Exeter, Exeter, U.K","institution_ids":["https://openalex.org/I23923803"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5107209335","display_name":"Dongdong Li","orcid":null},"institutions":[{"id":"https://openalex.org/I23632641","display_name":"Shanghai University of Electric Power","ror":"https://ror.org/02w4tny03","country_code":"CN","type":"education","lineage":["https://openalex.org/I23632641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dongdong Li","raw_affiliation_strings":["Department of Electrical Engineering, Shanghai University of Electric Power, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Shanghai University of Electric Power, Shanghai, China","institution_ids":["https://openalex.org/I23632641"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5033614477"],"corresponding_institution_ids":["https://openalex.org/I23632641"],"apc_list":null,"apc_paid":null,"fwci":3.3404,"has_fulltext":false,"cited_by_count":16,"citation_normalized_percentile":{"value":0.92441894,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"5","issue":"6","first_page":"2482","last_page":"2492"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9997000098228455,"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":0.9997000098228455,"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/T10603","display_name":"Smart Grid Energy Management","score":0.9919000267982483,"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":0.991100013256073,"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":0.8099410533905029},{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.672518253326416},{"id":"https://openalex.org/keywords/hyperparameter-optimization","display_name":"Hyperparameter optimization","score":0.6251111626625061},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.6215640306472778},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.48736482858657837},{"id":"https://openalex.org/keywords/grid","display_name":"Grid","score":0.4677472412586212},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.43888312578201294},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4302579164505005},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.3298770785331726},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.08339077234268188}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8099410533905029},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.672518253326416},{"id":"https://openalex.org/C10485038","wikidata":"https://www.wikidata.org/wiki/Q48996162","display_name":"Hyperparameter optimization","level":3,"score":0.6251111626625061},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.6215640306472778},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.48736482858657837},{"id":"https://openalex.org/C187691185","wikidata":"https://www.wikidata.org/wiki/Q2020720","display_name":"Grid","level":2,"score":0.4677472412586212},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43888312578201294},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4302579164505005},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3298770785331726},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.08339077234268188},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"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/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tai.2024.3358795","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tai.2024.3358795","pdf_url":null,"source":{"id":"https://openalex.org/S4210169448","display_name":"IEEE Transactions on Artificial Intelligence","issn_l":"2691-4581","issn":["2691-4581"],"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 Artificial Intelligence","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8299999833106995,"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7"}],"awards":[{"id":"https://openalex.org/G1583476792","display_name":"Leveraging Evolutionary Algorithms for Black-Box Adversarial Attacks in Robustness Evaluation of Deep Neural Networks","funder_award_id":"2404317","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"},{"id":"https://openalex.org/G2615012307","display_name":null,"funder_award_id":"62076056","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5567061388","display_name":null,"funder_award_id":"IES/R2/212077","funder_id":"https://openalex.org/F4320320006","funder_display_name":"Royal Society"},{"id":"https://openalex.org/G8245872083","display_name":null,"funder_award_id":"62376056","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320320006","display_name":"Royal Society","ror":"https://ror.org/03wnrjx87"},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320334627","display_name":"Engineering and Physical Sciences Research Council","ror":"https://ror.org/0439y7842"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W161513061","https://openalex.org/W1857789879","https://openalex.org/W2005898567","https://openalex.org/W2073603371","https://openalex.org/W2108114251","https://openalex.org/W2126316555","https://openalex.org/W2144432831","https://openalex.org/W2523246573","https://openalex.org/W2613278213","https://openalex.org/W2742093937","https://openalex.org/W2800064169","https://openalex.org/W2907556374","https://openalex.org/W2954611425","https://openalex.org/W2965525929","https://openalex.org/W3014649138","https://openalex.org/W3080597126","https://openalex.org/W3081707209","https://openalex.org/W3101502877","https://openalex.org/W3120283405","https://openalex.org/W3125822518","https://openalex.org/W3137520600","https://openalex.org/W3158145904","https://openalex.org/W3163842339","https://openalex.org/W3199712681","https://openalex.org/W4287624180","https://openalex.org/W4297800546","https://openalex.org/W4302308043","https://openalex.org/W4363652212","https://openalex.org/W6676576766","https://openalex.org/W6727249380","https://openalex.org/W6729956949","https://openalex.org/W6736057607","https://openalex.org/W6742288159","https://openalex.org/W6751797489","https://openalex.org/W6755365793","https://openalex.org/W6765440942","https://openalex.org/W6765451912","https://openalex.org/W6784924269","https://openalex.org/W6800495581"],"related_works":["https://openalex.org/W2953665647","https://openalex.org/W4281646320","https://openalex.org/W4205712847","https://openalex.org/W3169687406","https://openalex.org/W4388119537","https://openalex.org/W3014750173","https://openalex.org/W3114025147","https://openalex.org/W4287818966","https://openalex.org/W3192751261","https://openalex.org/W3200811867"],"abstract_inverted_index":{"Micro-grid":[0],"can":[1],"improve":[2],"greenhouse":[3],"gas":[4],"emissions":[5],"and":[6,15,29,104],"reduce":[7],"operational":[8],"costs.":[9],"To":[10],"forecast":[11],"both":[12,100],"energy":[13,151],"generation":[14],"load":[16],"demand,":[17],"time":[18],"series":[19],"prediction":[20],"has":[21],"been":[22],"a":[23,41,49,67,76,81,86,116,128,144],"key":[24],"tool":[25],"in":[26,62],"real-time":[27],"control":[28],"optimization.":[30],"Developing":[31],"an":[32,96],"adequate":[33],"predictive":[34],"model":[35],"is":[36,40,112],"difficult":[37],"when":[38],"there":[39],"lack":[42],"of":[43,55,75,119,136],"historical":[44],"data.":[45],"Moreover,":[46],"hyperparameters":[47,101],"have":[48],"tangible":[50],"impact":[51],"on":[52],"the":[53,72,91,109,134],"performance":[54],"machine":[56,120],"learning":[57,78,121,147],"models.":[58],"Bearing":[59],"these":[60],"considerations":[61],"mind,":[63],"this":[64],"paper":[65],"develops":[66],"BiLO-Auto-TSF/ML":[68],"framework":[69,111],"that":[70,108],"automates":[71],"optimal":[73],"design":[74],"few-shot":[77,146],"pipeline":[79,148],"from":[80],"bi-level":[82],"programming":[83],"perspective.":[84],"Specifically,":[85],"lower-level":[87],"meta-learner":[88],"helps":[89],"mitigate":[90],"small":[92],"data":[93],"challenge,":[94],"whereas":[95],"upper-level":[97,105],"optimization":[98],"optimizes":[99],"for":[102,124,143,149],"lower-":[103],"learners.":[106],"Note":[107],"proposed":[110,138],"designed":[113],"to":[114,141],"accommodate":[115],"wide":[117],"range":[118],"methods,":[122],"allowing":[123],"easy":[125],"integration":[126],"through":[127],"plug-in":[129],"mechanism.":[130],"Comprehensive":[131],"experiments":[132],"demonstrate":[133],"effectiveness":[135],"our":[137],"BiLO-Auto-":[139],"TSF/ML":[140],"search":[142],"high-performance":[145],"various":[150],"sources.":[152]},"counts_by_year":[{"year":2025,"cited_by_count":11},{"year":2024,"cited_by_count":5}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
