Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/89559
Title: | Bayesian updates for indoor thermal comfort models | Authors: | Mui, KW Tsang, TW Wong, LT |
Issue Date: | May-2020 | Source: | Journal of building engineering, May 2020, v. 29, 101117 | Abstract: | Achieving thermal comfort through sustainable indoor design is an increasing concern. Thermal comfort modelling is crucial for achieving building energy saving. This study reviews and categorizes major developments and trends in the field of thermal comfort research in recent years. Discrepancies between actual and predicted results of thermal sensation and thermal satisfaction suggests a performance gap in Fanger's model. Based on the current research gaps identified, a practical solution is proposed to improve the reliability of thermal comfort predictions. Two Bayesian updating protocols, namely individual updating and global updating, are put forward and the use of Bayesian approach to systemically update current thermal comfort beliefs with openly available field data is demonstrated. Besides being a practical tool for modelling thermal comfort using the best information available (i.e. existing models and field survey data), the proposed Bayesian updating provides an achievable solution to the present challenges in establishing a reliable thermal comfort prediction model. | Keywords: | Acceptance Bayesian updating Prediction Thermal comfort |
Publisher: | Elsevier | Journal: | Journal of building engineering | EISSN: | 2352-7102 | DOI: | 10.1016/j.jobe.2019.101117 | Rights: | © 2019 Elsevier Ltd. All rights reserved. © 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/. The following publication Mui, K. W., Tsang, T. W., & Wong, L. T. (2020). Bayesian updates for indoor thermal comfort models. Journal of Building Engineering, 29, 101117 is available at https://dx.doi.org/10.1016/j.jobe.2019.101117. |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Mui_Bayesian_Updates_Indoor.pdf | Pre-Published version | 1.59 MB | Adobe PDF | View/Open |
Page views
68
Last Week
0
0
Last month
Citations as of Apr 28, 2024
Downloads
45
Citations as of Apr 28, 2024
SCOPUSTM
Citations
29
Citations as of Apr 26, 2024
WEB OF SCIENCETM
Citations
25
Citations as of May 2, 2024
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.