Modelling personal thermal sensations using C-Support Vector Classification (C-SVC) algorithm

Jiang, L. and Yao, R. ORCID: https://orcid.org/0000-0003-4269-7224 (2016) Modelling personal thermal sensations using C-Support Vector Classification (C-SVC) algorithm. Building and Environment, 99. pp. 98-106. ISSN 0360-1323 doi: 10.1016/j.buildenv.2016.01.022

Abstract/Summary

The personalised conditioning system (PCS) is widely studied. Potentially, it is able to reduce energy consumption while securing occupants’ thermal comfort requirements. It has been suggested that automatic optimised operation schemes for PCS should be introduced to avoid energy wastage and discomfort caused by inappropriate operation. In certain automatic operation schemes, personalised thermal sensation models are applied as key components to help in setting targets for PCS operation. In this research, a novel personal thermal sensation modelling method based on the C-Support Vector Classification (C-SVC) algorithm has been developed for PCS control. The personal thermal sensation modelling has been regarded as a classification problem. During the modelling process, the method ‘learns’ an occupant’s thermal preferences from his/her feedback, environmental parameters and personal physiological and behavioural factors. The modelling method has been verified by comparing the actual thermal sensation vote (TSV) with the modelled one based on 20 individual cases. Furthermore, the accuracy of each individual thermal sensation model has been compared with the outcomes of the PMV model. The results indicate that the modelling method presented in this paper is an effective tool to model personal thermal sensations and could be integrated within the PCS for optimised system operation and control.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/53605
Identification Number/DOI	10.1016/j.buildenv.2016.01.022
Refereed	Yes
Divisions	Science > School of the Built Environment > Energy and Environmental Engineering group
Publisher	Elsevier
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