{"id":"https://openalex.org/W4416250901","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227966","title":"Temporal Attention Mechanism Echo State Network for Ultra-Short-Term Wind Power Forecasting","display_name":"Temporal Attention Mechanism Echo State Network for Ultra-Short-Term Wind Power Forecasting","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416250901","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227966"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11227966","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227966","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","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/A5100407735","display_name":"Xi Li","orcid":"https://orcid.org/0000-0002-7167-8332"},"institutions":[{"id":"https://openalex.org/I139660479","display_name":"Central South University","ror":"https://ror.org/00f1zfq44","country_code":"CN","type":"education","lineage":["https://openalex.org/I139660479"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xi Li","raw_affiliation_strings":["Central South University,Changsha,China"],"affiliations":[{"raw_affiliation_string":"Central South University,Changsha,China","institution_ids":["https://openalex.org/I139660479"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103028846","display_name":"Cheng Xie","orcid":"https://orcid.org/0000-0001-5215-3699"},"institutions":[{"id":"https://openalex.org/I139660479","display_name":"Central South University","ror":"https://ror.org/00f1zfq44","country_code":"CN","type":"education","lineage":["https://openalex.org/I139660479"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Cheng Xie","raw_affiliation_strings":["Central South University,Changsha,China"],"affiliations":[{"raw_affiliation_string":"Central South University,Changsha,China","institution_ids":["https://openalex.org/I139660479"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5057935426","display_name":"Jing Bi","orcid":"https://orcid.org/0000-0002-4610-0141"},"institutions":[{"id":"https://openalex.org/I139660479","display_name":"Central South University","ror":"https://ror.org/00f1zfq44","country_code":"CN","type":"education","lineage":["https://openalex.org/I139660479"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jing Bi","raw_affiliation_strings":["Central South University,Changsha,China"],"affiliations":[{"raw_affiliation_string":"Central South University,Changsha,China","institution_ids":["https://openalex.org/I139660479"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100407735"],"corresponding_institution_ids":["https://openalex.org/I139660479"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.3652684,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9757999777793884,"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":0.9757999777793884,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.002099999925121665,"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/T12611","display_name":"Neural Networks and Reservoir Computing","score":0.0020000000949949026,"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/time-series","display_name":"Time series","score":0.6366999745368958},{"id":"https://openalex.org/keywords/echo-state-network","display_name":"Echo state network","score":0.5985999703407288},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5396000146865845},{"id":"https://openalex.org/keywords/chaotic","display_name":"Chaotic","score":0.5385000109672546},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.4772999882698059},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.4657000005245209},{"id":"https://openalex.org/keywords/wind-power","display_name":"Wind power","score":0.45890000462532043},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.44200000166893005},{"id":"https://openalex.org/keywords/wind-power-forecasting","display_name":"Wind power forecasting","score":0.43959999084472656},{"id":"https://openalex.org/keywords/echo","display_name":"Echo (communications protocol)","score":0.42500001192092896}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6444000005722046},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.6366999745368958},{"id":"https://openalex.org/C172025690","wikidata":"https://www.wikidata.org/wiki/Q5332763","display_name":"Echo state network","level":4,"score":0.5985999703407288},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5396000146865845},{"id":"https://openalex.org/C2777052490","wikidata":"https://www.wikidata.org/wiki/Q5072826","display_name":"Chaotic","level":2,"score":0.5385000109672546},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4779999852180481},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.4772999882698059},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.4657000005245209},{"id":"https://openalex.org/C78600449","wikidata":"https://www.wikidata.org/wiki/Q43302","display_name":"Wind power","level":2,"score":0.45890000462532043},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.44200000166893005},{"id":"https://openalex.org/C2781084341","wikidata":"https://www.wikidata.org/wiki/Q2583670","display_name":"Wind power forecasting","level":4,"score":0.43959999084472656},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4260999858379364},{"id":"https://openalex.org/C2779426996","wikidata":"https://www.wikidata.org/wiki/Q18389128","display_name":"Echo (communications protocol)","level":2,"score":0.42500001192092896},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.4090000092983246},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.3603000044822693},{"id":"https://openalex.org/C89227174","wikidata":"https://www.wikidata.org/wiki/Q2388981","display_name":"Electric power system","level":3,"score":0.3441999852657318},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32899999618530273},{"id":"https://openalex.org/C89611455","wikidata":"https://www.wikidata.org/wiki/Q6804646","display_name":"Mechanism (biology)","level":2,"score":0.3287999927997589},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.328000009059906},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3025999963283539},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.2903999984264374},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.28279998898506165},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2818000018596649},{"id":"https://openalex.org/C187691185","wikidata":"https://www.wikidata.org/wiki/Q2020720","display_name":"Grid","level":2,"score":0.28029999136924744},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.2750999927520752},{"id":"https://openalex.org/C161067210","wikidata":"https://www.wikidata.org/wiki/Q1464943","display_name":"Wind speed","level":2,"score":0.274399995803833},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.27000001072883606},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.26750001311302185},{"id":"https://openalex.org/C135796866","wikidata":"https://www.wikidata.org/wiki/Q7315328","display_name":"Reservoir computing","level":4,"score":0.2660999894142151},{"id":"https://openalex.org/C25570617","wikidata":"https://www.wikidata.org/wiki/Q1006462","display_name":"Hilbert\u2013Huang transform","level":3,"score":0.2644999921321869},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.2624000012874603},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.260699987411499},{"id":"https://openalex.org/C2780440489","wikidata":"https://www.wikidata.org/wiki/Q5227278","display_name":"Data-driven","level":2,"score":0.25929999351501465},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.2563999891281128},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.25440001487731934}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11227966","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227966","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W2539519787","https://openalex.org/W2885420314","https://openalex.org/W2914579595","https://openalex.org/W2942406345","https://openalex.org/W2978791508","https://openalex.org/W2987201163","https://openalex.org/W3094948551","https://openalex.org/W3112298559","https://openalex.org/W3158692828","https://openalex.org/W3195017936","https://openalex.org/W3196068745","https://openalex.org/W3213190881","https://openalex.org/W4225416449","https://openalex.org/W4226142228","https://openalex.org/W4296185147","https://openalex.org/W4308391870","https://openalex.org/W4308595652","https://openalex.org/W4321373582","https://openalex.org/W4366529450","https://openalex.org/W4378085795","https://openalex.org/W4383820077"],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"ultra-short-term":[1,201],"wind":[2,27,177,202],"power":[3,14,28,178,203],"forecasting":[4,36,165,190],"(UWPF)":[5],"is":[6,160],"crucial":[7],"for":[8,34,97,200],"real-time":[9,164],"grid":[10],"management,":[11],"enabling":[12],"efficient":[13],"equipment":[15],"utilization":[16],"and":[17,23,50,130,174],"optimized":[18],"dispatching.":[19],"However,":[20],"the":[21,63,71,107,137,171],"chaotic":[22,80],"nonlinear":[24,56,150],"characteristics":[25],"of":[26,65,111],"time":[29,57,67,81,151],"series":[30,58,68,73,152],"pose":[31],"significant":[32],"challenges":[33],"traditional":[35],"models,":[37],"often":[38],"resulting":[39],"in":[40,54,78,149,189],"suboptimal":[41],"predictions.":[42],"Recurrent":[43],"neural":[44],"networks":[45],"(RNN)s":[46],"such":[47],"as":[48,197],"LSTM":[49],"GRU":[51],"perform":[52],"well":[53],"fitting":[55],"but":[59],"struggle":[60],"to":[61,120,140,162],"capture":[62],"influence":[64],"individual":[66],"samples":[69],"on":[70,125,142,170],"overall":[72],"evolution,":[74],"limiting":[75],"their":[76],"efficacy":[77],"predicting":[79],"series.":[82],"To":[83],"address":[84],"these":[85],"challenges,":[86],"we":[87],"propose":[88],"a":[89,102],"Temporal":[90],"Attention":[91],"Mechanism":[92],"Echo":[93,112],"State":[94,113],"Network":[95],"(TAM-ESN)":[96],"UWPF.":[98],"This":[99,133],"model":[100],"integrates":[101],"temporal":[103],"attention":[104,118],"mechanism":[105,159],"into":[106],"ridge":[108],"regression":[109],"phase":[110],"Networks":[114],"(ESNs),":[115],"dynamically":[116],"assigning":[117],"weights":[119],"different":[121],"sequence":[122],"segments":[123],"based":[124],"trend":[126],"factors,":[127],"turning":[128],"factors":[129],"internal":[131],"features.":[132],"novel":[134],"approach":[135],"enhances":[136],"model\u2019s":[138],"ability":[139],"focus":[141],"critical":[143],"historical":[144],"samples,":[145],"improving":[146],"its":[147],"generalization":[148],"prediction.":[153],"In":[154],"addition,":[155],"an":[156],"online":[157],"update":[158],"implemented":[161],"support":[163],"demand.":[166],"Our":[167],"empirical":[168],"evaluation":[169],"Mackey-Glass":[172],"system":[173],"three":[175],"public":[176],"datasets":[179],"demonstrates":[180],"that":[181],"TAM-ESN":[182],"significantly":[183],"outperforms":[184],"eleven":[185],"state-of-the-art":[186],"baseline":[187],"models":[188],"accuracy.":[191],"These":[192],"results":[193],"highlight":[194],"TAM-ESN\u2019s":[195],"potential":[196],"robust":[198],"solution":[199],"forecasting.":[204]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-11-14T00:00:00"}
