{"id":"https://openalex.org/W4312059933","doi":"https://doi.org/10.1109/la-cci54402.2022.9981839","title":"Ensemble Learning Models Applied in Energy Time Series of a University Building","display_name":"Ensemble Learning Models Applied in Energy Time Series of a University Building","publication_year":2022,"publication_date":"2022-11-23","ids":{"openalex":"https://openalex.org/W4312059933","doi":"https://doi.org/10.1109/la-cci54402.2022.9981839"},"language":"en","primary_location":{"id":"doi:10.1109/la-cci54402.2022.9981839","is_oa":false,"landing_page_url":"https://doi.org/10.1109/la-cci54402.2022.9981839","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE Latin American Conference on Computational Intelligence (LA-CCI)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5075060549","display_name":"Isaakc Junior Ortiz Aguirre","orcid":"https://orcid.org/0000-0002-0543-5738"},"institutions":[{"id":"https://openalex.org/I2405095","display_name":"University of Guayaquil","ror":"https://ror.org/047kyg834","country_code":"EC","type":"education","lineage":["https://openalex.org/I2405095"]}],"countries":["EC"],"is_corresponding":false,"raw_author_name":"Isaakc Ortiz-Aguirre","raw_affiliation_strings":["University of Guayaquil,Faculty of Mathematics and Physical Sciences,Guayaquil,Ecuador","Faculty of Mathematics and Physical Sciences, University of Guayaquil, Guayaquil, Ecuador"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Guayaquil,Faculty of Mathematics and Physical Sciences,Guayaquil,Ecuador","institution_ids":["https://openalex.org/I2405095"]},{"raw_affiliation_string":"Faculty of Mathematics and Physical Sciences, University of Guayaquil, Guayaquil, Ecuador","institution_ids":["https://openalex.org/I2405095"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067973644","display_name":"Mayken Espinoza\u2010Andaluz","orcid":"https://orcid.org/0000-0001-7809-8659"},"institutions":[{"id":"https://openalex.org/I2135383","display_name":"Escuela Superior Politecnica del Litoral","ror":"https://ror.org/04qenc566","country_code":"EC","type":"education","lineage":["https://openalex.org/I2135383"]}],"countries":["EC"],"is_corresponding":false,"raw_author_name":"Mayken Espinoza-Andaluz","raw_affiliation_strings":["Centro de Energ&#x00ED;as Renovables y Alternativas,Facultad de Ingenier&#x00ED;a Mec&#x00E1;nica y Ciencias de la Producci&#x00F3;n, Escuela Superior Polit&#x00E9;cnica del Litoral,Guayaquil,Ecuador"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Centro de Energ&#x00ED;as Renovables y Alternativas,Facultad de Ingenier&#x00ED;a Mec&#x00E1;nica y Ciencias de la Producci&#x00F3;n, Escuela Superior Polit&#x00E9;cnica del Litoral,Guayaquil,Ecuador","institution_ids":["https://openalex.org/I2135383"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5070227615","display_name":"Julio Barzola\u2013Monteses","orcid":"https://orcid.org/0000-0003-2732-979X"},"institutions":[{"id":"https://openalex.org/I2405095","display_name":"University of Guayaquil","ror":"https://ror.org/047kyg834","country_code":"EC","type":"education","lineage":["https://openalex.org/I2405095"]}],"countries":["EC"],"is_corresponding":false,"raw_author_name":"Julio Barzola-Monteses","raw_affiliation_strings":["University of Guayaquil,Artificial Intelligence and Information Technology Research Group,Guayaquil,Ecuador","Artificial Intelligence and Information Technology Research Group, University of Guayaquil, Guayaquil, Ecuador"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Guayaquil,Artificial Intelligence and Information Technology Research Group,Guayaquil,Ecuador","institution_ids":["https://openalex.org/I2405095"]},{"raw_affiliation_string":"Artificial Intelligence and Information Technology Research Group, University of Guayaquil, Guayaquil, Ecuador","institution_ids":["https://openalex.org/I2405095"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.6026,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.90521209,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10121","display_name":"Building Energy and Comfort Optimization","score":0.9990000128746033,"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"}},"topics":[{"id":"https://openalex.org/T10121","display_name":"Building Energy and Comfort Optimization","score":0.9990000128746033,"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/T11052","display_name":"Energy Load and Power Forecasting","score":0.9930999875068665,"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/T12120","display_name":"Air Quality Monitoring and Forecasting","score":0.9851999878883362,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.7062646150588989},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.6733206510543823},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6586955785751343},{"id":"https://openalex.org/keywords/gradient-boosting","display_name":"Gradient boosting","score":0.639103889465332},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.6307588815689087},{"id":"https://openalex.org/keywords/energy-consumption","display_name":"Energy consumption","score":0.6238305568695068},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5567773580551147},{"id":"https://openalex.org/keywords/extreme-learning-machine","display_name":"Extreme learning machine","score":0.5444189310073853},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5305688381195068},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.46334490180015564},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.46150970458984375},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.4587133526802063},{"id":"https://openalex.org/keywords/ensemble-forecasting","display_name":"Ensemble forecasting","score":0.45720013976097107},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.4418531656265259},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.4242461323738098},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1991363763809204},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1961376667022705},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1349239945411682},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.10373741388320923}],"concepts":[{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.7062646150588989},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.6733206510543823},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6586955785751343},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.639103889465332},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.6307588815689087},{"id":"https://openalex.org/C2780165032","wikidata":"https://www.wikidata.org/wiki/Q16869822","display_name":"Energy consumption","level":2,"score":0.6238305568695068},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5567773580551147},{"id":"https://openalex.org/C2780150128","wikidata":"https://www.wikidata.org/wiki/Q21948731","display_name":"Extreme learning machine","level":3,"score":0.5444189310073853},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5305688381195068},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.46334490180015564},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46150970458984375},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.4587133526802063},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.45720013976097107},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.4418531656265259},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.4242461323738098},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1991363763809204},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1961376667022705},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1349239945411682},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.10373741388320923},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/la-cci54402.2022.9981839","is_oa":false,"landing_page_url":"https://doi.org/10.1109/la-cci54402.2022.9981839","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE Latin American Conference on Computational Intelligence (LA-CCI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7","score":0.7400000095367432}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W167598277","https://openalex.org/W1582944457","https://openalex.org/W2767678610","https://openalex.org/W2778221222","https://openalex.org/W2801749227","https://openalex.org/W2807115920","https://openalex.org/W2894665398","https://openalex.org/W2925435752","https://openalex.org/W2974580236","https://openalex.org/W3113052689","https://openalex.org/W3161051625","https://openalex.org/W3202869083","https://openalex.org/W3215810333","https://openalex.org/W4200141454","https://openalex.org/W4232478844","https://openalex.org/W4235256446","https://openalex.org/W6606800860"],"related_works":["https://openalex.org/W2967733078","https://openalex.org/W3204430031","https://openalex.org/W3137904399","https://openalex.org/W4310492845","https://openalex.org/W2885778889","https://openalex.org/W2766514146","https://openalex.org/W2885516856","https://openalex.org/W4289703016","https://openalex.org/W3094138326","https://openalex.org/W4296079469"],"abstract_inverted_index":{"During":[0],"2020,":[1],"the":[2,28,57,126,141,159,177,184],"construction":[3],"and":[4,20,79,91,102,108,116,148],"operation":[5],"of":[6,16,22,60,86,99,105,125,130,179],"buildings":[7,42,174],"globally":[8],"accounted":[9],"for":[10,82],"more":[11],"than":[12],"a":[13,34,61,131],"third":[14],"(36%)":[15],"final":[17],"energy":[18,39,58,87,181],"used":[19],"37%":[21],"carbon":[23],"dioxide":[24],"emissions.":[25],"Hence,":[26,93],"in":[27,37,41,89,155,168,173,183],"last":[29],"decade,":[30],"there":[31],"has":[32,68],"been":[33,69],"great":[35],"interest":[36],"analyzing":[38],"efficiency":[40],"from":[43],"different":[44],"approaches.":[45],"In":[46,64],"this":[47,94,151],"paper,":[48],"machine":[49,72],"learning-based":[50],"black-box":[51],"methods":[52],"are":[53],"proposed":[54],"to":[55,122,175],"predict":[56],"consumption":[59,88,182],"university":[62,132],"building.":[63,133],"related":[65],"works,":[66],"little":[67],"explored":[70],"on":[71],"learning":[73,81,110],"techniques":[74,111],"such":[75,112],"as":[76,113],"decision":[77,106,160],"trees":[78],"ensemble":[80,109],"predicting":[83],"time":[84,123],"series":[85,124],"homes":[90],"buildings.":[92],"work":[95],"proposes":[96],"an":[97],"analysis":[98],"forecast":[100,143,176],"accuracy":[101],"computational":[103,156],"times":[104],"tree":[107,161],"Random":[114,138],"Forest":[115,139],"Extreme":[117],"Gradient":[118],"Boosting":[119],"applied":[120],"particularly":[121],"total":[127],"active":[128],"power":[129],"The":[134],"results":[135],"show":[136],"that":[137],"presents":[140],"best":[142],"error":[144],"metrics":[145],"RMSE,":[146],"MAE,":[147],"MAPE.":[149],"However,":[150],"model":[152],"ranks":[153],"second":[154],"time,":[157],"below":[158],"technique.":[162],"These":[163],"models":[164],"can":[165],"be":[166],"beneficial":[167],"predictive":[169],"control":[170],"systems":[171],"considered":[172],"behavior":[178],"buildings'":[180],"short":[185],"term":[186],"with":[187],"outstanding":[188],"precision.":[189]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
