{"id":"https://openalex.org/W4388320615","doi":"https://doi.org/10.1145/3600100.3626271","title":"Thermal Preference Prediction with Machine Learning","display_name":"Thermal Preference Prediction with Machine Learning","publication_year":2023,"publication_date":"2023-11-03","ids":{"openalex":"https://openalex.org/W4388320615","doi":"https://doi.org/10.1145/3600100.3626271"},"language":"en","primary_location":{"id":"doi:10.1145/3600100.3626271","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3600100.3626271","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation","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":null,"display_name":"Julianah Odeyemi","orcid":"https://orcid.org/0009-0008-3661-9768"},"institutions":[{"id":"https://openalex.org/I4210089692","display_name":"Einstein Center Digital Future","ror":"https://ror.org/0086bb350","country_code":"DE","type":"facility","lineage":["https://openalex.org/I39343248","https://openalex.org/I4210089692","https://openalex.org/I46043019","https://openalex.org/I75951250","https://openalex.org/I7877124"]},{"id":"https://openalex.org/I4577782","display_name":"Technische Universit\u00e4t Berlin","ror":"https://ror.org/03v4gjf40","country_code":"DE","type":"education","lineage":["https://openalex.org/I4577782"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Julianah Odeyemi","raw_affiliation_strings":["Einstein Center Digital Future (ECDF), Germany and Technische Universit\u00e4t Berlin, Germany"],"affiliations":[{"raw_affiliation_string":"Einstein Center Digital Future (ECDF), Germany and Technische Universit\u00e4t Berlin, Germany","institution_ids":["https://openalex.org/I4210089692","https://openalex.org/I4577782"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5033273272","display_name":"Rita Streblow","orcid":"https://orcid.org/0000-0001-7640-0930"},"institutions":[{"id":"https://openalex.org/I4577782","display_name":"Technische Universit\u00e4t Berlin","ror":"https://ror.org/03v4gjf40","country_code":"DE","type":"education","lineage":["https://openalex.org/I4577782"]},{"id":"https://openalex.org/I4210089692","display_name":"Einstein Center Digital Future","ror":"https://ror.org/0086bb350","country_code":"DE","type":"facility","lineage":["https://openalex.org/I39343248","https://openalex.org/I4210089692","https://openalex.org/I46043019","https://openalex.org/I75951250","https://openalex.org/I7877124"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Rita Streblow","raw_affiliation_strings":["Einstein Center Digital Future (ECDF), Germany and Technische Universit\u00e4t Berlin, Germany"],"affiliations":[{"raw_affiliation_string":"Einstein Center Digital Future (ECDF), Germany and Technische Universit\u00e4t Berlin, Germany","institution_ids":["https://openalex.org/I4210089692","https://openalex.org/I4577782"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210089692","https://openalex.org/I4577782"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.16439289,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"303","last_page":"304"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10121","display_name":"Building Energy and Comfort Optimization","score":0.9998000264167786,"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":0.9998000264167786,"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/T10766","display_name":"Urban Heat Island Mitigation","score":0.9926999807357788,"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"}},{"id":"https://openalex.org/T11109","display_name":"Thermoregulation and physiological responses","score":0.9251000285148621,"subfield":{"id":"https://openalex.org/subfields/2737","display_name":"Physiology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.6681574583053589},{"id":"https://openalex.org/keywords/predictive-power","display_name":"Predictive power","score":0.6677659749984741},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6602996587753296},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6316930055618286},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.6296036243438721},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6190152764320374},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6153524518013},{"id":"https://openalex.org/keywords/macro","display_name":"Macro","score":0.5821050405502319},{"id":"https://openalex.org/keywords/recall","display_name":"Recall","score":0.5655531883239746},{"id":"https://openalex.org/keywords/precision-and-recall","display_name":"Precision and recall","score":0.5534892678260803},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.5158175826072693},{"id":"https://openalex.org/keywords/preference","display_name":"Preference","score":0.4679611623287201},{"id":"https://openalex.org/keywords/f1-score","display_name":"F1 score","score":0.4447656571865082},{"id":"https://openalex.org/keywords/relevance-vector-machine","display_name":"Relevance vector machine","score":0.4288625717163086},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1498912274837494},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13228276371955872},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.08494099974632263}],"concepts":[{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6681574583053589},{"id":"https://openalex.org/C2778136018","wikidata":"https://www.wikidata.org/wiki/Q10350689","display_name":"Predictive power","level":2,"score":0.6677659749984741},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6602996587753296},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6316930055618286},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.6296036243438721},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6190152764320374},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6153524518013},{"id":"https://openalex.org/C166955791","wikidata":"https://www.wikidata.org/wiki/Q629579","display_name":"Macro","level":2,"score":0.5821050405502319},{"id":"https://openalex.org/C100660578","wikidata":"https://www.wikidata.org/wiki/Q18733","display_name":"Recall","level":2,"score":0.5655531883239746},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.5534892678260803},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.5158175826072693},{"id":"https://openalex.org/C2781249084","wikidata":"https://www.wikidata.org/wiki/Q908656","display_name":"Preference","level":2,"score":0.4679611623287201},{"id":"https://openalex.org/C148524875","wikidata":"https://www.wikidata.org/wiki/Q6975395","display_name":"F1 score","level":2,"score":0.4447656571865082},{"id":"https://openalex.org/C14948415","wikidata":"https://www.wikidata.org/wiki/Q7310972","display_name":"Relevance vector machine","level":3,"score":0.4288625717163086},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1498912274837494},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13228276371955872},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.08494099974632263},{"id":"https://openalex.org/C180747234","wikidata":"https://www.wikidata.org/wiki/Q23373","display_name":"Cognitive psychology","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/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3600100.3626271","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3600100.3626271","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/13","display_name":"Climate action","score":0.8500000238418579}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":3,"referenced_works":["https://openalex.org/W2913198854","https://openalex.org/W2962212997","https://openalex.org/W4310248773"],"related_works":["https://openalex.org/W4387490204","https://openalex.org/W3212239346","https://openalex.org/W4389848424","https://openalex.org/W4386414453","https://openalex.org/W4388937883","https://openalex.org/W2899594586","https://openalex.org/W4385625287","https://openalex.org/W4293205612","https://openalex.org/W4352976590","https://openalex.org/W4297839701"],"abstract_inverted_index":{"This":[0],"study":[1,39],"aimed":[2],"to":[3,35],"develop":[4],"a":[5],"personal":[6],"comfort":[7],"model":[8,112],"(PCM)":[9],"using":[10],"machine":[11],"learning":[12],"techniques":[13],"on":[14,22,26],"an":[15,59,110],"open-access":[16],"longitudinal":[17],"dataset.":[18],"Most":[19],"prior":[20],"studies":[21],"PCMs":[23],"were":[24],"based":[25],"controlled":[27],"climate":[28],"chamber":[29],"data,":[30],"which":[31],"limits":[32],"their":[33],"generalisability":[34],"real-world":[36],"settings.":[37],"The":[38,49],"examined":[40],"individual":[41,111],"and":[42,71,93,107],"ensemble":[43,98],"classifiers":[44],"for":[45],"predicting":[46,116],"thermal":[47,117],"preference.":[48,118],"Support":[50],"Vector":[51],"Classifier":[52],"(SVC)":[53],"displayed":[54],"strong":[55],"predictive":[56],"power":[57],"with":[58,87],"accuracy":[60,84],"of":[61,85],"0.843,":[62],"as":[63,65,101,109],"well":[64],"macro":[66,88],"precision":[67,89],"(0.724),":[68],"recall":[69,91],"(0.847),":[70],"F1":[72,94],"score":[73,95],"(0.763).":[74],"Similarly,":[75],"the":[76,82],"Extra":[77,102],"Trees":[78],"(ET)":[79],"classifier":[80],"achieved":[81],"highest":[83],"0.924,":[86],"(0.865),":[90],"(0.915),":[92],"(0.887).":[96],"Overall,":[97],"methods":[99],"such":[100],"Trees,":[103],"Extreme":[104],"Gradient":[105],"Boost,":[106],"SVC":[108],"proved":[113],"effective":[114],"in":[115]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
