{"id":"https://openalex.org/W4391093421","doi":"https://doi.org/10.1109/iciscae59047.2023.10392717","title":"WOA-BiLSTM-Attention Model for Short-term Power Load Projection","display_name":"WOA-BiLSTM-Attention Model for Short-term Power Load Projection","publication_year":2023,"publication_date":"2023-09-23","ids":{"openalex":"https://openalex.org/W4391093421","doi":"https://doi.org/10.1109/iciscae59047.2023.10392717"},"language":"en","primary_location":{"id":"doi:10.1109/iciscae59047.2023.10392717","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/iciscae59047.2023.10392717","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE 6th International Conference on Information Systems and Computer Aided Education (ICISCAE)","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/A5104232305","display_name":"Linxin Gao","orcid":null},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Linxin Gao","raw_affiliation_strings":["Beijing Institute of Technology,Beijing,China","Beijing Institute of Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Institute of Technology,Beijing,China","institution_ids":["https://openalex.org/I125839683"]},{"raw_affiliation_string":"Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5104232305"],"corresponding_institution_ids":["https://openalex.org/I125839683"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.18179015,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"41","issue":null,"first_page":"569","last_page":"574"},"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":0.9944000244140625,"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.9916999936103821,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6874929070472717},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.662388801574707},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5846123099327087},{"id":"https://openalex.org/keywords/electric-power-system","display_name":"Electric power system","score":0.5415646433830261},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5041216611862183},{"id":"https://openalex.org/keywords/whale","display_name":"Whale","score":0.46513834595680237},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.45805323123931885},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4271150529384613},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4125116765499115},{"id":"https://openalex.org/keywords/ecology","display_name":"Ecology","score":0.084220290184021}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6874929070472717},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.662388801574707},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5846123099327087},{"id":"https://openalex.org/C89227174","wikidata":"https://www.wikidata.org/wiki/Q2388981","display_name":"Electric power system","level":3,"score":0.5415646433830261},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5041216611862183},{"id":"https://openalex.org/C2777704720","wikidata":"https://www.wikidata.org/wiki/Q1865281","display_name":"Whale","level":2,"score":0.46513834595680237},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.45805323123931885},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4271150529384613},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4125116765499115},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.084220290184021},{"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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iciscae59047.2023.10392717","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/iciscae59047.2023.10392717","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE 6th International Conference on Information Systems and Computer Aided Education (ICISCAE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.47999998927116394,"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":5,"referenced_works":["https://openalex.org/W3037398653","https://openalex.org/W3131110071","https://openalex.org/W3165310135","https://openalex.org/W4306403747","https://openalex.org/W4317940693"],"related_works":["https://openalex.org/W2802282844","https://openalex.org/W4321217271","https://openalex.org/W2051770645","https://openalex.org/W4323922773","https://openalex.org/W4395147403","https://openalex.org/W343773973","https://openalex.org/W2910458994","https://openalex.org/W3085879776","https://openalex.org/W4251658542","https://openalex.org/W4324092079"],"abstract_inverted_index":{"Aiming":[0],"at":[1],"the":[2,19,27,43,49,53,74,101,106,110,117,122,137,142,147,165,172,192,197],"periodicity,":[3],"nonlinearity":[4],"and":[5,18,52,85,128,175,227],"certain":[6],"volatility":[7],"of":[8,22,29,76,105,180,204,225,230],"power":[9,15,32,70,79,87],"load,":[10],"this":[11],"study":[12],"employs":[13],"historical":[14,84],"load":[16,33,71,80,88,182],"data":[17,183],"defining":[20],"characteristics":[21],"its":[23],"influencing":[24,94],"factors":[25],"for":[26,68,191],"purpose":[28],"multivariate":[30],"short-term":[31,56,69],"prediction.":[34],"Subsequently,":[35],"an":[36,152,188,201,221,228],"integrated":[37],"model":[38,173,199],"(WOA-BiLSTM-Attention)":[39],"is":[40,161],"introduced,":[41],"utilizing":[42],"whale":[44],"optimization":[45],"algorithm":[46],"to":[47,98,119,125,146,163,170,208,220],"synergize":[48],"attention":[50],"mechanism":[51,112],"bi-directional":[54],"long":[55],"memory":[57],"(BiLSTM)":[58],"network.":[59],"This":[60],"innovative":[61],"approach":[62],"serves":[63,186],"as":[64,187,214],"a":[65],"novel":[66],"methodology":[67],"forecasting.":[72],"Initially,":[73],"utilization":[75,179],"BiLSTM":[77],"involves":[78],"prediction,":[81],"wherein":[82],"both":[83],"prospective":[86],"data,":[89,148],"along":[90],"with":[91,136],"their":[92],"respective":[93],"factors,":[95],"are":[96],"harnessed":[97],"comprehensively":[99],"capture":[100],"inherent":[102],"temporal":[103],"intricacies":[104],"dataset.":[107],"Following":[108],"this,":[109],"Attention":[111],"comes":[113],"into":[114],"play,":[115],"acquiring":[116],"ability":[118],"independently":[120],"discern":[121],"significance":[123],"attributed":[124],"diverse":[126],"features":[127],"chronological":[129],"segments.":[130],"By":[131],"prioritizing":[132],"attributes":[133],"intricately":[134],"associated":[135],"target,":[138],"it":[139],"adeptly":[140],"manages":[141],"extended":[143],"interdependence":[144],"intrinsic":[145],"thus":[149],"culminating":[150],"in":[151],"augmented":[153],"predictive":[154],"accuracy.":[155],"Finally,":[156],"Whale":[157],"Optimization":[158],"Algorithm":[159],"(WOA)":[160],"utilized":[162],"find":[164],"optimal":[166],"neural":[167,210],"network":[168,211],"parameters":[169],"reduce":[171],"stochasticity":[174],"time":[176],"costs.":[177],"The":[178],"electricity":[181],"from":[184],"Panama":[185],"illustrative":[189],"case":[190],"conducted":[193],"experiments,":[194],"revealing":[195],"that":[196],"WOA-BiLSTM-Attention":[198],"attains":[200],"elevated":[202],"level":[203],"prediction":[205],"accuracy":[206],"compared":[207],"single":[209],"methods":[212],"such":[213],"BP,":[215],"LSTM,":[216],"BiLSTM,":[217],"etc.,":[218],"corresponding":[219],"R<sup":[222],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[223],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">2</sup>":[224],"0.93":[226],"RMSE":[229],"29.49.":[231]},"counts_by_year":[],"updated_date":"2025-12-25T23:11:45.687758","created_date":"2025-10-10T00:00:00"}