Personal thermal comfort models using digital twins: Preference prediction with BIM-extracted spatial–temporal proximity data from Build2Vec

Conventional thermal preference prediction in buildings has limitations due to the difficulty in capturing all environmental and personal factors. New model features can improve the ability of a machine learning model to classify a person’s thermal preference. The spatial context of a building can p...

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Vydáno v:	Building and environment Ročník 207; s. 108532
Hlavní autoři:	Abdelrahman, Mahmoud M., Chong, Adrian, Miller, Clayton
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Oxford Elsevier Ltd 01.01.2022 Elsevier BV
Témata:	Building information modeling Building information models Building management systems Diffusers Digital twin Digital twins Graph network structure Heterogeneity Indoor environments Learning algorithms Localization Machine learning Personal thermal comfort model Position sensing Predictions Preferences Spatial data Spatial heterogeneity Spatial–temporal modeling Spatiotemporal data Thermal comfort Spatial–temporal modeling Digital twin Building information models Graph network structure Personal thermal comfort model
ISSN:	0360-1323, 1873-684X
On-line přístup:	Získat plný text
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