期刊
BUILDING AND ENVIRONMENT
卷 99, 期 -, 页码 98-106出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.buildenv.2016.01.022
关键词
Personalised conditioning system (PCS); Personal thermal sensation; Modelling method; C-Support Vector Classification
资金
- UK Engineering and Physical Sciences Research Council (EPSRC) Doctor Training Grant [EP/J500501/1, EP/K503125/1]
- Engineering and Physical Sciences Research Council [1230588, 1632842] Funding Source: researchfish
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. (C) 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
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