Gaussian Process models for ubiquitous user comfort preference sampling; global priors, active sampling and outlier rejection.

Fay, D., O'Toole, L. and Brown, K.N., 2016. Gaussian Process models for ubiquitous user comfort preference sampling; global priors, active sampling and outlier rejection. Pervasive and Mobile Computing. (In Press)

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Abstract

This paper presents a ubiquitous thermal comfort preference learning study in a noisy environment. We introduce Gaussian Process models into this field and show they are ideal, allowing rejection of outliers, deadband samples, and produce excellent estimates of a users preference function. In addition, informative combinations of users preferences becomes possible, some of which demonstrate well defined maxima ideal for control signals. Interestingly, while those users studied have differing preferences, their hyperparameters are concentrated allowing priors for new users. In addition, we present an active learning algorithm which estimates when to poll users to maximise the information returned.

Item Type:Article
ISSN:1574-1192
Uncontrolled Keywords:Active learning; Gaussian process models; thermal preference; ASHRAE; PMV
Subjects:UNSPECIFIED
Group:Faculty of Science & Technology
ID Code:24606
Deposited By: Unnamed user with email symplectic@symplectic
Deposited On:31 Aug 2016 11:54
Last Modified:28 Sep 2016 14:26

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