{"id":"https://openalex.org/W4387421228","doi":"https://doi.org/10.1145/3594739.3612915","title":"LSTM Based Short-Term Data Center Electrical Consumption Forecasting","display_name":"LSTM Based Short-Term Data Center Electrical Consumption Forecasting","publication_year":2023,"publication_date":"2023-10-07","ids":{"openalex":"https://openalex.org/W4387421228","doi":"https://doi.org/10.1145/3594739.3612915"},"language":"en","primary_location":{"id":"doi:10.1145/3594739.3612915","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3594739.3612915","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Adjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing &amp; the 2023 ACM International Symposium on Wearable Computing","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/A5103129856","display_name":"Feiyang Chen","orcid":"https://orcid.org/0009-0004-9171-016X"},"institutions":[{"id":"https://openalex.org/I4210116924","display_name":"Chinese University of Hong Kong, Shenzhen","ror":"https://ror.org/02d5ks197","country_code":"CN","type":"education","lineage":["https://openalex.org/I177725633","https://openalex.org/I180726961","https://openalex.org/I4210116924"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Feiyang Chen","raw_affiliation_strings":["The Chinese University of Hong Kong, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"The Chinese University of Hong Kong, Shenzhen, China","institution_ids":["https://openalex.org/I4210116924"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014416228","display_name":"Chenye Wu","orcid":"https://orcid.org/0000-0002-5730-916X"},"institutions":[{"id":"https://openalex.org/I4210116924","display_name":"Chinese University of Hong Kong, Shenzhen","ror":"https://ror.org/02d5ks197","country_code":"CN","type":"education","lineage":["https://openalex.org/I177725633","https://openalex.org/I180726961","https://openalex.org/I4210116924"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chenye Wu","raw_affiliation_strings":["The Chinese University of Hong Kong, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"The Chinese University of Hong Kong, Shenzhen, China","institution_ids":["https://openalex.org/I4210116924"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100743596","display_name":"Jiasheng Zhang","orcid":"https://orcid.org/0000-0001-5498-5739"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiasheng Zhang","raw_affiliation_strings":["Tsinghua University, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5019796013","display_name":"Guanchi Liu","orcid":"https://orcid.org/0000-0002-3249-4354"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guanchi Liu","raw_affiliation_strings":["Tencent Inc., China"],"affiliations":[{"raw_affiliation_string":"Tencent Inc., China","institution_ids":["https://openalex.org/I2250653659"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5103129856"],"corresponding_institution_ids":["https://openalex.org/I4210116924"],"apc_list":null,"apc_paid":null,"fwci":0.3904,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.60561937,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"730","last_page":"735"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9994999766349792,"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":0.9994999766349792,"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":0.9936000108718872,"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/T11276","display_name":"Solar Radiation and Photovoltaics","score":0.9933000206947327,"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/data-center","display_name":"Data center","score":0.7924126386642456},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7315259575843811},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.6263730525970459},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.6085551977157593},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5480496883392334},{"id":"https://openalex.org/keywords/energy-consumption","display_name":"Energy consumption","score":0.5397139191627502},{"id":"https://openalex.org/keywords/consumption","display_name":"Consumption (sociology)","score":0.5385810732841492},{"id":"https://openalex.org/keywords/electricity","display_name":"Electricity","score":0.505929172039032},{"id":"https://openalex.org/keywords/long-short-term-memory","display_name":"Long short term memory","score":0.48328307271003723},{"id":"https://openalex.org/keywords/autoregressive-integrated-moving-average","display_name":"Autoregressive integrated moving average","score":0.4389249384403229},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.43476757407188416},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4249637722969055},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.3959659934043884},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3885813355445862},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.3350867033004761},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.29496729373931885},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2572554349899292},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.14212384819984436},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.13924866914749146},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.0992671549320221}],"concepts":[{"id":"https://openalex.org/C153740404","wikidata":"https://www.wikidata.org/wiki/Q671224","display_name":"Data center","level":2,"score":0.7924126386642456},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7315259575843811},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.6263730525970459},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.6085551977157593},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5480496883392334},{"id":"https://openalex.org/C2780165032","wikidata":"https://www.wikidata.org/wiki/Q16869822","display_name":"Energy consumption","level":2,"score":0.5397139191627502},{"id":"https://openalex.org/C30772137","wikidata":"https://www.wikidata.org/wiki/Q5164762","display_name":"Consumption (sociology)","level":2,"score":0.5385810732841492},{"id":"https://openalex.org/C206658404","wikidata":"https://www.wikidata.org/wiki/Q12725","display_name":"Electricity","level":2,"score":0.505929172039032},{"id":"https://openalex.org/C133488467","wikidata":"https://www.wikidata.org/wiki/Q6673524","display_name":"Long short term memory","level":4,"score":0.48328307271003723},{"id":"https://openalex.org/C24338571","wikidata":"https://www.wikidata.org/wiki/Q2566298","display_name":"Autoregressive integrated moving average","level":3,"score":0.4389249384403229},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.43476757407188416},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4249637722969055},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.3959659934043884},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3885813355445862},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3350867033004761},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.29496729373931885},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2572554349899292},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.14212384819984436},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.13924866914749146},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0992671549320221},{"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/C36289849","wikidata":"https://www.wikidata.org/wiki/Q34749","display_name":"Social science","level":1,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","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:10.1145/3594739.3612915","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3594739.3612915","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Adjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing &amp; the 2023 ACM International Symposium on Wearable Computing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","score":0.9100000262260437,"display_name":"Affordable and clean energy"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W2036455044","https://openalex.org/W2042225379","https://openalex.org/W2157862425","https://openalex.org/W2534287718","https://openalex.org/W2547687744","https://openalex.org/W2548430090","https://openalex.org/W2586259521","https://openalex.org/W2599285715","https://openalex.org/W2899094839","https://openalex.org/W2996374824","https://openalex.org/W3007075806","https://openalex.org/W3097247253","https://openalex.org/W3102206304","https://openalex.org/W3110718632","https://openalex.org/W3112697087","https://openalex.org/W3135417427","https://openalex.org/W3189601588","https://openalex.org/W4206816939","https://openalex.org/W4317926922"],"related_works":["https://openalex.org/W3175321409","https://openalex.org/W4312561791","https://openalex.org/W2389894046","https://openalex.org/W2215717369","https://openalex.org/W2974356760","https://openalex.org/W4312309719","https://openalex.org/W3115491726","https://openalex.org/W4313123484","https://openalex.org/W4386362517","https://openalex.org/W2146461990"],"abstract_inverted_index":{"Data":[0],"center":[1,35,50,111],"operators,":[2],"with":[3],"the":[4,13,55,58,69,93],"aid":[5],"of":[6,57,92,103],"advanced":[7],"energy":[8],"storage":[9],"devices,":[10],"can":[11],"reduce":[12],"operating":[14],"cost":[15],"by":[16,116],"maintaining":[17],"a":[18,43,84],"long-term":[19],"stable":[20],"electrical":[21,26,51,112],"consumption":[22,27,113],"curve.":[23],"Therefore,":[24],"accurate":[25],"forecasting":[28,45],"is":[29],"crucial":[30],"to":[31,82],"enable":[32],"effective":[33],"data":[34,49,110,114],"electricity":[36],"systems.":[37],"In":[38],"this":[39],"paper,":[40],"we":[41,79],"develop":[42],"data-driven":[44],"model":[46,86],"for":[47],"short-term":[48],"consumption.":[52],"Through":[53],"examining":[54],"results":[56],"traditional":[59],"statistical":[60],"model,":[61,72],"Seasonal":[62],"Auto-Regressive":[63],"Integrated":[64],"Moving":[65],"Average":[66],"(SARIMA),":[67],"and":[68,96],"deep":[70],"learning":[71],"Long":[73],"Short-Term":[74],"Memory":[75],"Neural":[76],"Network":[77],"(LSTM),":[78],"combine":[80],"them":[81],"construct":[83],"hybrid":[85],"which":[87],"outperforms":[88],"each":[89],"single":[90],"one":[91],"two":[94],"models":[95,105],"shows":[97],"good":[98],"mobility.":[99],"The":[100],"overall":[101],"performance":[102],"our":[104],"has":[106],"been":[107],"validated":[108],"on":[109],"provided":[115],"Tencent":[117],"Inc.":[118]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2}],"updated_date":"2026-04-02T15:55:50.835912","created_date":"2025-10-10T00:00:00"}
