{"id":"https://openalex.org/W3134751135","doi":"https://doi.org/10.3233/ida-194969","title":"Multi-task prediction model based on ConvLSTM and encoder-decoder","display_name":"Multi-task prediction model based on ConvLSTM and encoder-decoder","publication_year":2021,"publication_date":"2021-03-04","ids":{"openalex":"https://openalex.org/W3134751135","doi":"https://doi.org/10.3233/ida-194969","mag":"3134751135"},"language":"en","primary_location":{"id":"doi:10.3233/ida-194969","is_oa":false,"landing_page_url":"https://doi.org/10.3233/ida-194969","pdf_url":null,"source":{"id":"https://openalex.org/S2498839158","display_name":"Intelligent Data Analysis","issn_l":"1088-467X","issn":["1088-467X","1571-4128"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Intelligent Data Analysis","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/A5002713029","display_name":"Tao Luo","orcid":"https://orcid.org/0000-0003-4870-5942"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Tao Luo","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040475616","display_name":"Xudong Cao","orcid":"https://orcid.org/0000-0003-4761-693X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xudong Cao","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100364819","display_name":"Jin Li","orcid":"https://orcid.org/0000-0003-0385-8793"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jin Li","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101958555","display_name":"Kun Dong","orcid":"https://orcid.org/0000-0002-5440-185X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kun Dong","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108116186","display_name":"Rui Zhang","orcid":"https://orcid.org/0000-0002-6605-2458"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rui Zhang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5008340821","display_name":"Xueliang Wei","orcid":"https://orcid.org/0000-0003-3571-1702"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xueliang Wei","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5002713029"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.1039,"has_fulltext":false,"cited_by_count":17,"citation_normalized_percentile":{"value":0.76509746,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":"25","issue":"2","first_page":"359","last_page":"382"},"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9887999892234802,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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/T12451","display_name":"Smart Grid and Power Systems","score":0.9854000210762024,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8169137239456177},{"id":"https://openalex.org/keywords/encode","display_name":"ENCODE","score":0.7120208144187927},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.6803286075592041},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6684479713439941},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.6235626935958862},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5667605400085449},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5559563040733337},{"id":"https://openalex.org/keywords/layer","display_name":"Layer (electronics)","score":0.5504804253578186},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.5086325407028198},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35969486832618713}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8169137239456177},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.7120208144187927},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.6803286075592041},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6684479713439941},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.6235626935958862},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5667605400085449},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5559563040733337},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.5504804253578186},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.5086325407028198},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35969486832618713},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"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/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3233/ida-194969","is_oa":false,"landing_page_url":"https://doi.org/10.3233/ida-194969","pdf_url":null,"source":{"id":"https://openalex.org/S2498839158","display_name":"Intelligent Data Analysis","issn_l":"1088-467X","issn":["1088-467X","1571-4128"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Intelligent Data Analysis","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7","score":0.8999999761581421}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W1498436455","https://openalex.org/W1560739766","https://openalex.org/W1903029394","https://openalex.org/W1970012672","https://openalex.org/W2000093387","https://openalex.org/W2064675550","https://openalex.org/W2102605133","https://openalex.org/W2108563286","https://openalex.org/W2112796928","https://openalex.org/W2117130368","https://openalex.org/W2168013545","https://openalex.org/W2194775991","https://openalex.org/W2302255633","https://openalex.org/W2407776548","https://openalex.org/W2474211927","https://openalex.org/W2525330092","https://openalex.org/W2556605533","https://openalex.org/W2593051978","https://openalex.org/W2735926531","https://openalex.org/W2766736793","https://openalex.org/W2786806645","https://openalex.org/W2796538079","https://openalex.org/W2812669263","https://openalex.org/W2903162817","https://openalex.org/W2912773550","https://openalex.org/W2913340405","https://openalex.org/W2949888546","https://openalex.org/W2962897020","https://openalex.org/W2964036520","https://openalex.org/W2984328837"],"related_works":["https://openalex.org/W2372020181","https://openalex.org/W2156531654","https://openalex.org/W4378714697","https://openalex.org/W1581723585","https://openalex.org/W2294330161","https://openalex.org/W2804553224","https://openalex.org/W4283822356","https://openalex.org/W1950940422","https://openalex.org/W2129146436","https://openalex.org/W2253069048"],"abstract_inverted_index":{"The":[0,76,126],"energy":[1,57],"load":[2,58,113],"data":[3,114],"in":[4,35,119],"the":[5,24,36,56,61,72,86,93,98,103,111,116,136,141,148,152,157,160,164],"micro-energy":[6,37,62,117],"network":[7,118],"are":[8,89],"a":[9,20,51,120],"time":[10,74],"series":[11],"with":[12,169],"sequential":[13],"and":[14,27,45,135,151,168],"nonlinear":[15],"characteristics.":[16],"This":[17,105],"paper":[18],"proposes":[19],"model":[21,52,106,132,142,165],"based":[22],"on":[23],"encode-decode":[25],"architecture":[26],"ConvLSTM":[28,67],"for":[29,54],"multi-scale":[30],"prediction":[31],"of":[32,60,115,123,140,147,159],"multi-energy":[33,112],"loads":[34],"network.":[38,63],"We":[39],"apply":[40],"ConvLSTM,":[41],"LSTM,":[42],"attention":[43,77,161],"mechanism":[44,78,162],"multi-task":[46,149],"learning":[47],"concepts":[48],"to":[49,70,81,85,109],"construct":[50],"specifically":[53],"processing":[55],"forecasting":[59],"In":[64,155],"this":[65],"paper,":[66],"is":[68,79,107,133,143],"used":[69,80],"encode":[71],"input":[73],"series.":[75],"assign":[82],"different":[83],"weights":[84],"features,":[87],"which":[88],"subsequently":[90],"decoded":[91],"by":[92],"decoder":[94],"LSTM":[95],"layer.":[96],"Finally,":[97],"fully":[99],"connected":[100],"layer":[101],"interprets":[102],"output.":[104],"applied":[108],"forecast":[110],"certain":[121],"area":[122],"Northwest":[124],"China.":[125],"test":[127],"results":[128],"prove":[129],"that":[130,146],"our":[131],"convergent,":[134],"evaluation":[137],"index":[138],"value":[139],"better":[144],"than":[145],"FC-LSTM":[150],"single-task":[153],"FC-LSTM.":[154],"particular,":[156],"application":[158],"makes":[163],"converge":[166],"faster":[167],"higher":[170],"precision.":[171]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":1}],"updated_date":"2026-03-01T08:55:55.761014","created_date":"2025-10-10T00:00:00"}