{"id":"https://openalex.org/W4389742565","doi":"https://doi.org/10.1145/3637552","title":"DEWP: Deep Expansion Learning for Wind Power Forecasting","display_name":"DEWP: Deep Expansion Learning for Wind Power Forecasting","publication_year":2023,"publication_date":"2023-12-14","ids":{"openalex":"https://openalex.org/W4389742565","doi":"https://doi.org/10.1145/3637552"},"language":"en","primary_location":{"id":"doi:10.1145/3637552","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3637552","pdf_url":null,"source":{"id":"https://openalex.org/S41523882","display_name":"ACM Transactions on Knowledge Discovery from Data","issn_l":"1556-4681","issn":["1556-4681","1556-472X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Knowledge Discovery from Data","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/A5116668175","display_name":"Wei Fan","orcid":"https://orcid.org/0000-0001-7656-445X"},"institutions":[{"id":"https://openalex.org/I40120149","display_name":"University of Oxford","ror":"https://ror.org/052gg0110","country_code":"GB","type":"education","lineage":["https://openalex.org/I40120149"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Wei Fan","raw_affiliation_strings":["University of Oxford, UK"],"raw_orcid":"https://orcid.org/0000-0001-7656-445X","affiliations":[{"raw_affiliation_string":"University of Oxford, UK","institution_ids":["https://openalex.org/I40120149"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5116668146","display_name":"Yanjie Fu","orcid":"https://orcid.org/0000-0003-2725-3334"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yanjie Fu","raw_affiliation_strings":["Arizona State University, USA"],"raw_orcid":"https://orcid.org/0000-0003-2725-3334","affiliations":[{"raw_affiliation_string":"Arizona State University, USA","institution_ids":["https://openalex.org/I55732556"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101807424","display_name":"Shun Zheng","orcid":"https://orcid.org/0009-0005-7355-7090"},"institutions":[{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shun Zheng","raw_affiliation_strings":["Microsoft Research, China"],"raw_orcid":"https://orcid.org/0009-0005-7355-7090","affiliations":[{"raw_affiliation_string":"Microsoft Research, China","institution_ids":["https://openalex.org/I4210113369"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101544241","display_name":"Jiang Bian","orcid":"https://orcid.org/0000-0002-9472-600X"},"institutions":[{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiang Bian","raw_affiliation_strings":["Microsoft Research, China"],"raw_orcid":"https://orcid.org/0000-0002-9472-600X","affiliations":[{"raw_affiliation_string":"Microsoft Research, China","institution_ids":["https://openalex.org/I4210113369"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065865669","display_name":"Yuanchun Zhou","orcid":"https://orcid.org/0000-0003-2144-1131"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210108629","display_name":"Computer Network Information Center","ror":"https://ror.org/01s0wyf50","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210108629"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuanchun Zhou","raw_affiliation_strings":["Computer Network Information Center, Chinese Academy of Sciences, China"],"raw_orcid":"https://orcid.org/0000-0003-2144-1131","affiliations":[{"raw_affiliation_string":"Computer Network Information Center, Chinese Academy of Sciences, China","institution_ids":["https://openalex.org/I4210108629","https://openalex.org/I19820366"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101862104","display_name":"Hui Xiong","orcid":"https://orcid.org/0000-0001-6016-6465"},"institutions":[{"id":"https://openalex.org/I200769079","display_name":"Hong Kong University of Science and Technology","ror":"https://ror.org/00q4vv597","country_code":"HK","type":"education","lineage":["https://openalex.org/I200769079"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Hui Xiong","raw_affiliation_strings":["Hong Kong University of Science and Technology, China"],"raw_orcid":"https://orcid.org/0000-0001-6016-6465","affiliations":[{"raw_affiliation_string":"Hong Kong University of Science and Technology, China","institution_ids":["https://openalex.org/I200769079"]}]}],"institutions":[],"countries_distinct_count":4,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5116668175"],"corresponding_institution_ids":["https://openalex.org/I40120149"],"apc_list":null,"apc_paid":null,"fwci":1.3952,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.81812602,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"18","issue":"3","first_page":"1","last_page":"21"},"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9805999994277954,"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/T11276","display_name":"Solar Radiation and Photovoltaics","score":0.9681000113487244,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.5070626735687256},{"id":"https://openalex.org/keywords/wind-power","display_name":"Wind power","score":0.4778939485549927},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4771294593811035},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4395718574523926},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.37927961349487305},{"id":"https://openalex.org/keywords/meteorology","display_name":"Meteorology","score":0.3737184703350067},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.3394099175930023},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.19753605127334595},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.18414172530174255},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.16008874773979187},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.07378584146499634}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5070626735687256},{"id":"https://openalex.org/C78600449","wikidata":"https://www.wikidata.org/wiki/Q43302","display_name":"Wind power","level":2,"score":0.4778939485549927},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4771294593811035},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4395718574523926},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37927961349487305},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.3737184703350067},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3394099175930023},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.19753605127334595},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.18414172530174255},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16008874773979187},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.07378584146499634}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1145/3637552","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3637552","pdf_url":null,"source":{"id":"https://openalex.org/S41523882","display_name":"ACM Transactions on Knowledge Discovery from Data","issn_l":"1556-4681","issn":["1556-4681","1556-472X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Knowledge Discovery from Data","raw_type":"journal-article"},{"id":"pmh:oai:repository.hkust.edu.hk:1783.1-134353","is_oa":false,"landing_page_url":"https://repository.hkust.edu.hk/ir/Record/1783.1-134353","pdf_url":null,"source":{"id":"https://openalex.org/S4306401796","display_name":"Rare & Special e-Zone (The Hong Kong University of Science and Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I200769079","host_organization_name":"Hong Kong University of Science and Technology","host_organization_lineage":["https://openalex.org/I200769079"],"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"},{"id":"pmh:oai:ora.ox.ac.uk:uuid:d9c4f2a9-3d06-4d54-bd44-e770499d16dc","is_oa":false,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4306402636","display_name":"Oxford University Research Archive (ORA) (University of Oxford)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I40120149","host_organization_name":"University of Oxford","host_organization_lineage":["https://openalex.org/I40120149"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Symplectic Elements","raw_type":"Journal article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","score":0.8399999737739563,"id":"https://metadata.un.org/sdg/7"}],"awards":[{"id":"https://openalex.org/G1040948671","display_name":null,"funder_award_id":"61836013","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G1782397203","display_name":null,"funder_award_id":"92370204","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/F4320324202","display_name":"Guangdong Science and Technology Department","ror":"https://ror.org/00tjzgn92"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":46,"referenced_works":["https://openalex.org/W1168959737","https://openalex.org/W1538131130","https://openalex.org/W1924770834","https://openalex.org/W2067718885","https://openalex.org/W2085866051","https://openalex.org/W2145925782","https://openalex.org/W2166889509","https://openalex.org/W2194775991","https://openalex.org/W2290832617","https://openalex.org/W2495605196","https://openalex.org/W2603648311","https://openalex.org/W2604738573","https://openalex.org/W2604847698","https://openalex.org/W2605800522","https://openalex.org/W2792764867","https://openalex.org/W2794201716","https://openalex.org/W2798831744","https://openalex.org/W2883904934","https://openalex.org/W2896920734","https://openalex.org/W2911892655","https://openalex.org/W2944245994","https://openalex.org/W2946709941","https://openalex.org/W2954586649","https://openalex.org/W2970585579","https://openalex.org/W2980994438","https://openalex.org/W2981654920","https://openalex.org/W2992096112","https://openalex.org/W2996552856","https://openalex.org/W3044963726","https://openalex.org/W3090528971","https://openalex.org/W3095043043","https://openalex.org/W3111349778","https://openalex.org/W3173539742","https://openalex.org/W3177318507","https://openalex.org/W3196636665","https://openalex.org/W3212890323","https://openalex.org/W4213137001","https://openalex.org/W4223926961","https://openalex.org/W4236047370","https://openalex.org/W4281651325","https://openalex.org/W4283826931","https://openalex.org/W4288050066","https://openalex.org/W4293649366","https://openalex.org/W4382239131","https://openalex.org/W4387723790","https://openalex.org/W7038994942"],"related_works":["https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W4375867731","https://openalex.org/W2611989081","https://openalex.org/W2731899572","https://openalex.org/W4230611425","https://openalex.org/W4294635752","https://openalex.org/W4304166257","https://openalex.org/W4383066092","https://openalex.org/W4380075502"],"abstract_inverted_index":{"Wind":[0,11],"is":[1,140,255],"one":[2,14],"kind":[3],"of":[4,15,43,74,82,142,151,196,198,295],"high-efficient,":[5],"environmentally-friendly,":[6],"and":[7,28,64,94,169,220,267],"cost-effective":[8],"energy":[9,19],"source.":[10],"power,":[12],"as":[13,58],"the":[16,21,41,75,79,98,125,278,293],"largest":[17],"renewable":[18],"in":[20,32,38,176,236,277,289],"world,":[22],"has":[23],"been":[24],"playing":[25],"a":[26,108,134,144,161],"more":[27,29,234],"important":[30],"role":[31],"supplying":[33],"electricity.":[34],"Though":[35],"growing":[36],"dramatically":[37],"recent":[39],"years,":[40],"amount":[42],"generated":[44,76,226],"wind":[45,59,61,227,251],"power":[46,77,252],"can":[47,192],"be":[48],"directly":[49],"or":[50],"latently":[51],"affected":[52],"by":[53,91,262],"multiple":[54,84,158],"uncertain":[55],"factors,":[56],"such":[57],"speed,":[60],"direction,":[62],"temperatures,":[63],"so":[65],"on.":[66],"More":[67],"importantly,":[68],"there":[69],"exist":[70],"very":[71],"complicated":[72,126],"dependencies":[73,127,156,175,197],"on":[78,282],"latent":[80,188,223],"composition":[81],"these":[83],"time-evolving":[85,199],"variables,":[86],"which":[87,191,212],"are":[88],"always":[89],"ignored":[90],"existing":[92],"works":[93],"thus":[95],"largely":[96],"hinder":[97],"prediction":[99],"performances.":[100],"To":[101],"this":[102],"end,":[103],"we":[104,203,241,273],"propose":[105,204],"DEWP":[106,131,233],",":[107],"novel":[109],"D":[110],"eep":[111],"E":[112],"xpansion":[113],"learning":[114,245],"for":[115,209],"W":[116],"ind":[117],"P":[118],"ower":[119],"forecasting":[120,253],"framework":[121],"to":[122,154,172,216,231,246,291],"carefully":[123],"model":[124,193],"with":[128,133],"adequate":[129],"expressiveness.":[130],"starts":[132],"stack-by-stack":[135,248],"architecture,":[136],"where":[137],"each":[138,210],"stack":[139,265,270],"composed":[141],"(i)":[143],"variable":[145],"expansion":[146,163],"block":[147,164,207],"that":[148,165],"makes":[149],"use":[150],"convolutional":[152],"layers":[153],"capture":[155],"among":[157],"variables;":[159],"(ii)":[160],"time":[162],"applies":[166,213],"Fourier":[167],"series":[168],"backcast/forecast":[170],"mechanism":[171],"learn":[173],"temporal":[174],"sequential":[177,200],"patterns.":[178],"These":[179],"two":[180,283,286],"tailored":[181],"blocks":[182],"expand":[183],"raw":[184],"inputs":[185],"into":[186,225],"different":[187,194,287],"feature":[189],"spaces":[190],"levels":[195],"data.":[201],"Moreover,":[202],"an":[205],"inference":[206],"corresponding":[208],"stack,":[211],"multi-head":[214],"self-attentions":[215],"acquire":[217],"attentive":[218],"features":[219],"maps":[221],"expanded":[222],"representations":[224],"power.":[228],"In":[229],"addition,":[230],"make":[232],"expressive":[235],"handling":[237],"deep":[238],"neural":[239],"architectures,":[240],"adapt":[242],"doubly":[243],"residue":[244],"process":[247],"outputs.":[249],"Accurate":[250],"(WPF)":[254],"then":[256],"better":[257],"achieved":[258],"through":[259],"fine-grained":[260],"outputs":[261],"continuously":[263],"removing":[264],"residues":[266],"accumulating":[268],"useful":[269],"forecasts.":[271],"Finally,":[272],"present":[274],"extensive":[275],"experiments":[276],"real-world":[279],"WPF":[280],"application":[281],"datasets":[284],"from":[285],"turbines,":[288],"order":[290],"demonstrate":[292],"effectiveness":[294],"our":[296],"approach.":[297]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":7},{"year":2023,"cited_by_count":1}],"updated_date":"2026-05-05T08:41:31.759640","created_date":"2025-10-10T00:00:00"}