ESTIMATION OF OCCUPANCY STATUS AND LEVELS FOR INDOOR SPACES

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dc.contributor.authorIsikdag, U.
dc.contributor.authorApak, Sudi
dc.date.accessioned2025-03-26T17:35:08Z
dc.date.available2025-03-26T17:35:08Z
dc.date.issued2020
dc.departmentİstanbul Esenyurt Üniversitesi, Fakülteler, İşletme ve Yönetim Bilimleri Fakültesi, Ekonomi ve Finans Bölümü
dc.description.abstractBuilding energy use today accounts for over 40% of total primary energy consumption. The energy demand for buildings can be decreased through efficient building and facility management. The knowledge related to the use of indoor spaces is a key to successful management. The research aimed to investigate whether the occupancy levels of an indoor space can automatically be determined via machine learning algorithms based on data acquired from multiple indoor sensors. The study involved indoor data collection and a machine learning experiment. The results indicated that machine learning can be considered as a promising approach for the detection of indoor occupancy status and levels.
dc.identifier.endpage1341
dc.identifier.issn1311-5065
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85097950520
dc.identifier.scopusqualityQ3
dc.identifier.startpage1336
dc.identifier.urihttps://hdl.handle.net/20.500.14704/1038
dc.identifier.volume21
dc.identifier.wosWOS:000588763300018
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherScibulcom Ltd
dc.relation.ispartofJournal of Environmental Protection and Ecology
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250326
dc.subjectindoor; building; occupancy; machine learning; classification
dc.titleESTIMATION OF OCCUPANCY STATUS AND LEVELS FOR INDOOR SPACES
dc.typeArticle

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