{"id":"https://openalex.org/W2903969466","doi":"https://doi.org/10.1109/access.2018.2887023","title":"Evolutionary Deep Learning-Based Energy Consumption Prediction for Buildings","display_name":"Evolutionary Deep Learning-Based Energy Consumption Prediction for Buildings","publication_year":2018,"publication_date":"2018-12-18","ids":{"openalex":"https://openalex.org/W2903969466","doi":"https://doi.org/10.1109/access.2018.2887023","mag":"2903969466"},"language":"en","primary_location":{"id":"doi:10.1109/access.2018.2887023","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2018.2887023","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08579122.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08579122.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5090324328","display_name":"Abdulaziz Almalaq","orcid":"https://orcid.org/0000-0001-8153-4327"},"institutions":[{"id":"https://openalex.org/I131651094","display_name":"University of Denver","ror":"https://ror.org/04w7skc03","country_code":"US","type":"education","lineage":["https://openalex.org/I131651094"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Abdulaziz Almalaq","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Denver, Denver, CO, USA"],"raw_orcid":"https://orcid.org/0000-0001-8153-4327","affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Denver, Denver, CO, USA","institution_ids":["https://openalex.org/I131651094"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5007781850","display_name":"Jun Jason Zhang","orcid":"https://orcid.org/0000-0001-6908-2671"},"institutions":[{"id":"https://openalex.org/I131651094","display_name":"University of Denver","ror":"https://ror.org/04w7skc03","country_code":"US","type":"education","lineage":["https://openalex.org/I131651094"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jun Jason Zhang","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Denver, Denver, CO, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Denver, Denver, CO, USA","institution_ids":["https://openalex.org/I131651094"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I131651094"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":6.6003,"has_fulltext":true,"cited_by_count":123,"citation_normalized_percentile":{"value":0.9720127,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"7","issue":null,"first_page":"1520","last_page":"1531"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10121","display_name":"Building Energy and Comfort Optimization","score":0.9991999864578247,"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":0.9991999864578247,"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":0.9991999864578247,"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/T11276","display_name":"Solar Radiation and Photovoltaics","score":0.9828000068664551,"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/computer-science","display_name":"Computer science","score":0.7968738079071045},{"id":"https://openalex.org/keywords/energy-consumption","display_name":"Energy consumption","score":0.6883978247642517},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.6053628325462341},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.567672848701477},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5576401948928833},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5473225116729736},{"id":"https://openalex.org/keywords/genetic-algorithm","display_name":"Genetic algorithm","score":0.5458014011383057},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5201423764228821},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.4995605945587158},{"id":"https://openalex.org/keywords/evolutionary-algorithm","display_name":"Evolutionary algorithm","score":0.4495592415332794},{"id":"https://openalex.org/keywords/consumption","display_name":"Consumption (sociology)","score":0.4166911840438843},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.11681395769119263}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7968738079071045},{"id":"https://openalex.org/C2780165032","wikidata":"https://www.wikidata.org/wiki/Q16869822","display_name":"Energy consumption","level":2,"score":0.6883978247642517},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.6053628325462341},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.567672848701477},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5576401948928833},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5473225116729736},{"id":"https://openalex.org/C8880873","wikidata":"https://www.wikidata.org/wiki/Q187787","display_name":"Genetic algorithm","level":2,"score":0.5458014011383057},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5201423764228821},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.4995605945587158},{"id":"https://openalex.org/C159149176","wikidata":"https://www.wikidata.org/wiki/Q14489129","display_name":"Evolutionary algorithm","level":2,"score":0.4495592415332794},{"id":"https://openalex.org/C30772137","wikidata":"https://www.wikidata.org/wiki/Q5164762","display_name":"Consumption (sociology)","level":2,"score":0.4166911840438843},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.11681395769119263},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","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},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"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/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2018.2887023","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2018.2887023","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08579122.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:f2f7d1901a9c4d63a807a069892483ce","is_oa":true,"landing_page_url":"https://doaj.org/article/f2f7d1901a9c4d63a807a069892483ce","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 7, Pp 1520-1531 (2019)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2018.2887023","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2018.2887023","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08579122.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Affordable and clean energy","score":0.7599999904632568,"id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":60,"referenced_works":["https://openalex.org/W1567845545","https://openalex.org/W1728842521","https://openalex.org/W1972925007","https://openalex.org/W1976161617","https://openalex.org/W1977686954","https://openalex.org/W1980618555","https://openalex.org/W1991873640","https://openalex.org/W2000548672","https://openalex.org/W2017561014","https://openalex.org/W2033065921","https://openalex.org/W2039346833","https://openalex.org/W2060774500","https://openalex.org/W2067878879","https://openalex.org/W2069304223","https://openalex.org/W2071064323","https://openalex.org/W2089792825","https://openalex.org/W2107779924","https://openalex.org/W2109042184","https://openalex.org/W2117825288","https://openalex.org/W2121844625","https://openalex.org/W2126831543","https://openalex.org/W2150295936","https://openalex.org/W2161257182","https://openalex.org/W2164709595","https://openalex.org/W2168138569","https://openalex.org/W2194775991","https://openalex.org/W2283229106","https://openalex.org/W2321147838","https://openalex.org/W2398690169","https://openalex.org/W2414319411","https://openalex.org/W2543643230","https://openalex.org/W2553165053","https://openalex.org/W2563738891","https://openalex.org/W2566804351","https://openalex.org/W2587347436","https://openalex.org/W2597866042","https://openalex.org/W2599743206","https://openalex.org/W2601171548","https://openalex.org/W2612810742","https://openalex.org/W2742473260","https://openalex.org/W2769821199","https://openalex.org/W2773038010","https://openalex.org/W2782902016","https://openalex.org/W2807252330","https://openalex.org/W2808628700","https://openalex.org/W2811193345","https://openalex.org/W2891929938","https://openalex.org/W2896419919","https://openalex.org/W2901867967","https://openalex.org/W3100270150","https://openalex.org/W3104996215","https://openalex.org/W3120740533","https://openalex.org/W3150435615","https://openalex.org/W4229740899","https://openalex.org/W6637572315","https://openalex.org/W6676279030","https://openalex.org/W6731432862","https://openalex.org/W6752338972","https://openalex.org/W6752761967","https://openalex.org/W6756003323"],"related_works":["https://openalex.org/W4375867731","https://openalex.org/W2611989081","https://openalex.org/W4230611425","https://openalex.org/W2731899572","https://openalex.org/W4294635752","https://openalex.org/W4304166257","https://openalex.org/W4383066092","https://openalex.org/W3215138031","https://openalex.org/W2804383999","https://openalex.org/W3136570575"],"abstract_inverted_index":{"Today\u2019s":[0],"energy":[1,10,22,36,48,59,74],"resources":[2],"are":[3,77,82],"closer":[4],"to":[5,8,32,55,106,109],"consumers":[6],"due":[7],"sustainable":[9],"and":[11,30,98,140,149,170,177,191],"advanced":[12],"technology.":[13],"To":[14],"that":[15,81,131,181],"end,":[16],"ensuring":[17],"a":[18,39,111,118],"precise":[19],"prediction":[20,76,79,121,193],"of":[21,50,68,102,156,168],"consumption":[23,49,60,75],"at":[24],"the":[25,34,47,57,69,87,91,94,99,125,150,157,178,182],"buildings\u2019":[26],"level":[27],"is":[28,132,162],"vital":[29],"significant":[31],"manage":[33],"consumed":[35],"efficiently":[37],"using":[38,62,164],"robust":[40],"predictive":[41,65,160],"model.":[42,66],"Growing":[43],"concern":[44],"about":[45,90],"reducing":[46],"buildings":[51,172],"makes":[52],"it":[53],"necessary":[54],"predict":[56],"future":[58],"precisely":[61],"an":[63],"optimizable":[64],"Most":[67],"previously":[70],"proposed":[71],"methods":[72,80,104],"for":[73,173],"conventional":[78,190],"normally":[83],"designed":[84],"based":[85,123],"on":[86,124],"developer\u2019s":[88],"knowledge":[89],"hyper-parameters.":[92],"However,":[93],"time":[95,146],"lag":[96],"inputs":[97],"network\u2019s":[100,151],"hyper-parameters":[101],"learning":[103,128],"need":[105],"be":[107],"adjusted":[108],"have":[110,186],"more":[112],"accurate":[113],"prediction.":[114],"This":[115],"paper":[116],"proposes":[117],"novel":[119],"hybrid":[120],"approach":[122],"evolutionary":[126,183],"deep":[127],"(DL)":[129],"method":[130],"combining":[133],"genetic":[134],"algorithm":[135],"with":[136,145],"long":[137],"short-term":[138,175],"memory":[139],"optimizing":[141],"its":[142],"objective":[143],"function":[144],"window":[147],"lags":[148],"hidden":[152],"neurons.":[153],"The":[154],"performance":[155,188],"presented":[158],"optimization":[159],"model":[161],"investigated":[163],"public":[165],"building":[166],"datasets":[167],"residential":[169],"commercial":[171],"very":[174],"prediction,":[176],"results":[179],"indicate":[180],"DL":[184],"models":[185],"better":[187],"than":[189],"regular":[192],"models.":[194]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":17},{"year":2024,"cited_by_count":17},{"year":2023,"cited_by_count":23},{"year":2022,"cited_by_count":21},{"year":2021,"cited_by_count":21},{"year":2020,"cited_by_count":12},{"year":2019,"cited_by_count":8}],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2025-10-10T00:00:00"}