{"id":"https://openalex.org/W3090921757","doi":"https://doi.org/10.1109/access.2020.3027061","title":"Ultra-Short-Term Building Cooling Load Prediction Model Based on Feature Set Construction and Ensemble Machine Learning","display_name":"Ultra-Short-Term Building Cooling Load Prediction Model Based on Feature Set Construction and Ensemble Machine Learning","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3090921757","doi":"https://doi.org/10.1109/access.2020.3027061","mag":"3090921757"},"language":"en","primary_location":{"id":"doi:10.1109/access.2020.3027061","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.3027061","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09206575.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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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/ielx7/6287639/8948470/09206575.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101777680","display_name":"Yan Ding","orcid":"https://orcid.org/0000-0002-2141-147X"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yan Ding","raw_affiliation_strings":["Tianjin Key Laboratory of Built Environment and Energy Application, School of Environmental Science and Engineering, Tianjin University, Tianjin, China"],"raw_orcid":"https://orcid.org/0000-0002-2141-147X","affiliations":[{"raw_affiliation_string":"Tianjin Key Laboratory of Built Environment and Energy Application, School of Environmental Science and Engineering, Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101832829","display_name":"Hao Su","orcid":"https://orcid.org/0000-0002-2080-0561"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hao Su","raw_affiliation_strings":["Tianjin Key Laboratory of Built Environment and Energy Application, School of Environmental Science and Engineering, Tianjin University, Tianjin, China"],"raw_orcid":"https://orcid.org/0000-0002-2080-0561","affiliations":[{"raw_affiliation_string":"Tianjin Key Laboratory of Built Environment and Energy Application, School of Environmental Science and Engineering, Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100689088","display_name":"Xiangfei Kong","orcid":"https://orcid.org/0000-0002-1550-4071"},"institutions":[{"id":"https://openalex.org/I184843921","display_name":"Hebei University of Technology","ror":"https://ror.org/018hded08","country_code":"CN","type":"education","lineage":["https://openalex.org/I184843921"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiangfei Kong","raw_affiliation_strings":["School of Energy and Environmental Engineering, Hebei University of Technology, Tianjin, China"],"raw_orcid":"https://orcid.org/0000-0002-1550-4071","affiliations":[{"raw_affiliation_string":"School of Energy and Environmental Engineering, Hebei University of Technology, Tianjin, China","institution_ids":["https://openalex.org/I184843921"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5029102141","display_name":"Zhenqin Zhang","orcid":"https://orcid.org/0000-0003-1796-2743"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhenqin Zhang","raw_affiliation_strings":["Tianjin Key Laboratory of Built Environment and Energy Application, School of Environmental Science and Engineering, Tianjin University, Tianjin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tianjin Key Laboratory of Built Environment and Energy Application, School of Environmental Science and Engineering, Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.9085,"has_fulltext":true,"cited_by_count":20,"citation_normalized_percentile":{"value":0.74377335,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"8","issue":null,"first_page":"178733","last_page":"178745"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.8794999718666077,"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":0.8794999718666077,"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/T13955","display_name":"Evaluation Methods in Various Fields","score":0.7702999711036682,"subfield":{"id":"https://openalex.org/subfields/2302","display_name":"Ecological Modeling"},"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/term","display_name":"Term (time)","score":0.7353581786155701},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7015728950500488},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5733942985534668},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5222828388214111},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5029155611991882},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4436931610107422},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.4225742220878601}],"concepts":[{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.7353581786155701},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7015728950500488},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5733942985534668},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5222828388214111},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5029155611991882},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4436931610107422},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.4225742220878601},{"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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2020.3027061","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.3027061","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09206575.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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:756b94caae6b4c0a80805317272e7d2c","is_oa":true,"landing_page_url":"https://doaj.org/article/756b94caae6b4c0a80805317272e7d2c","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 8, Pp 178733-178745 (2020)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2020.3027061","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.3027061","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09206575.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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.4699999988079071,"display_name":"Sustainable cities and communities"}],"awards":[{"id":"https://openalex.org/G6114774598","display_name":null,"funder_award_id":"51678396","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3090921757.pdf","grobid_xml":"https://content.openalex.org/works/W3090921757.grobid-xml"},"referenced_works_count":50,"referenced_works":["https://openalex.org/W1236934511","https://openalex.org/W1969230401","https://openalex.org/W1998931125","https://openalex.org/W2002841906","https://openalex.org/W2008869685","https://openalex.org/W2047143310","https://openalex.org/W2051395647","https://openalex.org/W2063048614","https://openalex.org/W2071949631","https://openalex.org/W2079679946","https://openalex.org/W2100805904","https://openalex.org/W2124833832","https://openalex.org/W2181468267","https://openalex.org/W2187089797","https://openalex.org/W2294173671","https://openalex.org/W2295598076","https://openalex.org/W2324591127","https://openalex.org/W2463235918","https://openalex.org/W2508187212","https://openalex.org/W2509638679","https://openalex.org/W2549906944","https://openalex.org/W2560648492","https://openalex.org/W2560801424","https://openalex.org/W2595984151","https://openalex.org/W2761146210","https://openalex.org/W2767542146","https://openalex.org/W2771841295","https://openalex.org/W2790123320","https://openalex.org/W2790625050","https://openalex.org/W2791014378","https://openalex.org/W2791701972","https://openalex.org/W2796018543","https://openalex.org/W2810350849","https://openalex.org/W2836487175","https://openalex.org/W2895889688","https://openalex.org/W2900724415","https://openalex.org/W2921516590","https://openalex.org/W2930922598","https://openalex.org/W2944283577","https://openalex.org/W2946631449","https://openalex.org/W2972001256","https://openalex.org/W2992033075","https://openalex.org/W2997327172","https://openalex.org/W2999752491","https://openalex.org/W3002730961","https://openalex.org/W3022044990","https://openalex.org/W3102476541","https://openalex.org/W6679060694","https://openalex.org/W6685680431","https://openalex.org/W6960065523"],"related_works":["https://openalex.org/W4376643315","https://openalex.org/W4324137541","https://openalex.org/W2900445707","https://openalex.org/W4285741730","https://openalex.org/W1191482210","https://openalex.org/W4285046548","https://openalex.org/W4210302090","https://openalex.org/W4388321867","https://openalex.org/W3092276832","https://openalex.org/W4375951447"],"abstract_inverted_index":{"As":[0],"the":[1,4,11,22,32,37,76,84,87,103,111,129,133,136,147,157,161],"requirements":[2],"for":[3,145],"optimal":[5],"control":[6],"of":[7,15,24,59,86,102,135,169],"building":[8,47],"systems":[9],"increase,":[10],"accuracy":[12,23,78,113],"and":[13,71,106,132,166,171],"speed":[14],"load":[16,25,49],"predictions":[17,26],"should":[18],"also":[19,36,115],"increase.":[20],"However,":[21],"is":[27,92,120,139,143],"related":[28],"to":[29],"not":[30],"only":[31],"prediction":[33,50,77,96,107,112,118,137,155],"algorithm,":[34,131],"but":[35],"feature":[38,54,62,88,148],"set":[39,55,63,89],"construction.":[40,56],"Therefore,":[41],"this":[42],"study":[43],"develops":[44],"a":[45],"short-term":[46],"cooling":[48],"model":[51,119,138,159],"based":[52,122],"on":[53,110,123],"The":[57,100,117],"impacts":[58],"four":[60],"different":[61,95],"construction":[64,90],"methods-feature":[65],"extraction,":[66],"correlation":[67],"analysis,":[68],"K-means":[69],"clustering,":[70],"discrete":[72],"wavelet":[73],"transform":[74],"(DWT)-on":[75],"are":[79,98,114],"compared.":[80],"To":[81],"ensure":[82],"that":[83],"effect":[85],"method":[91],"universal,":[93],"three":[94],"algorithms":[97],"used.":[99],"influences":[101],"sample":[104],"dimension":[105],"time":[108],"horizon":[109],"analysed.":[116],"developed":[121],"an":[124],"ensemble":[125],"learning":[126],"algorithm":[127],"utilising":[128],"cubist":[130],"performance":[134],"improved":[140],"when":[141],"DWT":[142],"used":[144,154],"constructing":[146],"set.":[149],"Compared":[150],"with":[151,164],"other":[152],"commonly":[153],"models,":[156],"proposed":[158],"exhibits":[160],"best":[162],"performance,":[163],"R-squared":[165],"CV-RMSE":[167],"values":[168],"99.8%":[170],"1.5%,":[172],"respectively.":[173]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
