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...

Full description

Saved in:

Bibliographic Details
Published in:	Building and environment Vol. 207; p. 108532
Main Authors:	Abdelrahman, Mahmoud M., Chong, Adrian, Miller, Clayton
Format:	Journal Article
Language:	English
Published:	Oxford Elsevier Ltd 01.01.2022 Elsevier BV
Subjects:	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
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!