{"id":"https://openalex.org/W4366381057","doi":"https://doi.org/10.1145/3584376.3584553","title":"Short-term wind speed interval prediction by convolutional long- and short-term memory networks based on attention mechanism","display_name":"Short-term wind speed interval prediction by convolutional long- and short-term memory networks based on attention mechanism","publication_year":2022,"publication_date":"2022-12-16","ids":{"openalex":"https://openalex.org/W4366381057","doi":"https://doi.org/10.1145/3584376.3584553"},"language":"en","primary_location":{"id":"doi:10.1145/3584376.3584553","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3584376.3584553","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 4th International Conference on Robotics, Intelligent Control and Artificial Intelligence","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/A5021566782","display_name":"Dong Guo","orcid":"https://orcid.org/0000-0003-3441-9996"},"institutions":[{"id":"https://openalex.org/I4210096899","display_name":"Jiangsu University of Science and Technology","ror":"https://ror.org/00tyjp878","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210096899"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Dong Guo","raw_affiliation_strings":["School of Computer Science, Jiangsu University of Science and Technology, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Jiangsu University of Science and Technology, China","institution_ids":["https://openalex.org/I4210096899"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5021566782"],"corresponding_institution_ids":["https://openalex.org/I4210096899"],"apc_list":null,"apc_paid":null,"fwci":0.0915,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.43672793,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"993","last_page":"997"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9998000264167786,"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":0.9998000264167786,"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/T12368","display_name":"Grey System Theory Applications","score":0.9434999823570251,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11276","display_name":"Solar Radiation and Photovoltaics","score":0.9409999847412109,"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/wind-speed","display_name":"Wind speed","score":0.7687680721282959},{"id":"https://openalex.org/keywords/prediction-interval","display_name":"Prediction interval","score":0.6688865423202515},{"id":"https://openalex.org/keywords/kriging","display_name":"Kriging","score":0.5917130708694458},{"id":"https://openalex.org/keywords/interval","display_name":"Interval (graph theory)","score":0.5706692337989807},{"id":"https://openalex.org/keywords/wind-power","display_name":"Wind power","score":0.5704493522644043},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5676670074462891},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.47827064990997314},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.46773821115493774},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.4562346935272217},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.4222012460231781},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.41043663024902344},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.37184250354766846},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.25207680463790894},{"id":"https://openalex.org/keywords/meteorology","display_name":"Meteorology","score":0.21684947609901428},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.15389123558998108},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.14986810088157654}],"concepts":[{"id":"https://openalex.org/C161067210","wikidata":"https://www.wikidata.org/wiki/Q1464943","display_name":"Wind speed","level":2,"score":0.7687680721282959},{"id":"https://openalex.org/C103402496","wikidata":"https://www.wikidata.org/wiki/Q1106171","display_name":"Prediction interval","level":2,"score":0.6688865423202515},{"id":"https://openalex.org/C81692654","wikidata":"https://www.wikidata.org/wiki/Q225926","display_name":"Kriging","level":2,"score":0.5917130708694458},{"id":"https://openalex.org/C2778067643","wikidata":"https://www.wikidata.org/wiki/Q166507","display_name":"Interval (graph theory)","level":2,"score":0.5706692337989807},{"id":"https://openalex.org/C78600449","wikidata":"https://www.wikidata.org/wiki/Q43302","display_name":"Wind power","level":2,"score":0.5704493522644043},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5676670074462891},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.47827064990997314},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.46773821115493774},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.4562346935272217},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4222012460231781},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.41043663024902344},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.37184250354766846},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.25207680463790894},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.21684947609901428},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.15389123558998108},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.14986810088157654},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","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},{"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/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"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:10.1145/3584376.3584553","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3584376.3584553","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 4th International Conference on Robotics, Intelligent Control and Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","score":0.9100000262260437,"id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":6,"referenced_works":["https://openalex.org/W1677182931","https://openalex.org/W2070960910","https://openalex.org/W2340896543","https://openalex.org/W2565739165","https://openalex.org/W2767124238","https://openalex.org/W3199385355"],"related_works":["https://openalex.org/W566010457","https://openalex.org/W2600092203","https://openalex.org/W4300066510","https://openalex.org/W2056958800","https://openalex.org/W2803685231","https://openalex.org/W4293503520","https://openalex.org/W3134152097","https://openalex.org/W4311388919","https://openalex.org/W2966696655","https://openalex.org/W430895897"],"abstract_inverted_index":{"Large-scale":[0],"integration":[1],"of":[2,14,27,35,40,79,96,102],"wind":[3,15,23,36,51,97,124,139],"energy":[4],"is":[5,26,43,89,106],"limited":[6],"by":[7,120],"the":[8,31,68,77,100,103,122,138,143,161],"strong":[9],"volatility":[10],"and":[11,20,33,54,76,117,151,157,166],"stochastic":[12],"nature":[13],"speeds.":[16],"Therefore,":[17],"obtaining":[18],"reliable":[19,153],"high":[21],"quality":[22],"speed":[24,52,125,140],"forecasts":[25],"great":[28],"importance":[29],"for":[30,49,137],"planning":[32],"application":[34],"energy.":[37],"The":[38,132],"objective":[39],"this":[41,60],"study":[42],"to":[44,55,91],"develop":[45],"a":[46,62],"hybrid":[47,63],"model":[48,105,144],"short-term":[50],"prediction":[53,74,94,112,115,118,141,149,155],"quantify":[56],"its":[57],"uncertainty.":[58],"In":[59],"study,":[61],"method,":[64],"CLSTMA-GPR,":[65],"which":[66],"combines":[67],"Gaussian":[69,167],"process":[70,168],"regression":[71,169],"(GPR)":[72],"interval":[73,93,114],"features":[75],"advantages":[78],"attention":[80],"mechanism":[81],"(AM)-based":[82],"space-time":[83],"feature":[84],"fusion":[85],"(CLSTM)":[86],"point":[87,111,154],"prediction,":[88],"proposed":[90,104],"perform":[92],"analysis":[95],"speed.":[98],"Finally,":[99],"performance":[101],"verified":[107],"in":[108,128],"four":[109],"aspects:":[110],"accuracy,":[113,147],"suitability,":[116],"reliability":[119],"using":[121],"actual":[123],"data":[126],"cases":[127],"Inner":[129],"Mongolia":[130],"region.":[131],"experimental":[133],"results":[134,156],"show":[135],"that":[136],"problem,":[142],"obtains":[145],"higher":[146],"suitable":[148],"interval,":[150],"more":[152],"probability":[158],"distribution":[159],"than":[160],"traditional":[162],"structural":[163],"deep":[164],"learning":[165],"models":[170],"alone.":[171]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
