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A personalised thermal comfort model using a Bayesian network

A personalised thermal comfort model using a Bayesian network
A personalised thermal comfort model using a Bayesian network
In this paper, we address the challenge of predicting optimal comfort temperatures of individual users of a smart heating system. At present, such systems use simple models of user comfort when deciding on a set point temperature. These models generally fail to adapt to an individual user’s preferences, resulting in poor estimates of a user’s preferred temperature. To address this issue, we propose a personalised thermal comfort model that uses a Bayesian network to learn and adapt to a user’s individual preferences. Through an empirical evaluation based on the ASHRAE RP-884 data-set, we show that our model is consistently 17.5-23.5% more accurate than current models, regardless of environmental conditions and the type of heating system used. Our model is not limited to a single metric but can also infer information about expected user feedback, optimal comfort temperature and thermal sensitivity at the same time, which can be used to reduce energy used for heating with minimal comfort loss.
978-1-57735-738-4
2547-2553
Auffenberg, Frederik
98237584-a003-4149-99bc-c4521eb0527d
Stein, Sebastian
cb2325e7-5e63-475e-8a69-9db2dfbdb00b
Rogers, Alex
f9130bc6-da32-474e-9fab-6c6cb8077fdc
Auffenberg, Frederik
98237584-a003-4149-99bc-c4521eb0527d
Stein, Sebastian
cb2325e7-5e63-475e-8a69-9db2dfbdb00b
Rogers, Alex
f9130bc6-da32-474e-9fab-6c6cb8077fdc

Auffenberg, Frederik, Stein, Sebastian and Rogers, Alex (2015) A personalised thermal comfort model using a Bayesian network. International Joint Conference on Artificial Intelligence, Argentina. 25 - 31 Jul 2015. pp. 2547-2553 .

Record type: Conference or Workshop Item (Paper)

Abstract

In this paper, we address the challenge of predicting optimal comfort temperatures of individual users of a smart heating system. At present, such systems use simple models of user comfort when deciding on a set point temperature. These models generally fail to adapt to an individual user’s preferences, resulting in poor estimates of a user’s preferred temperature. To address this issue, we propose a personalised thermal comfort model that uses a Bayesian network to learn and adapt to a user’s individual preferences. Through an empirical evaluation based on the ASHRAE RP-884 data-set, we show that our model is consistently 17.5-23.5% more accurate than current models, regardless of environmental conditions and the type of heating system used. Our model is not limited to a single metric but can also infer information about expected user feedback, optimal comfort temperature and thermal sensitivity at the same time, which can be used to reduce energy used for heating with minimal comfort loss.

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More information

Accepted/In Press date: 17 April 2015
e-pub ahead of print date: 25 July 2015
Published date: 25 July 2015
Venue - Dates: International Joint Conference on Artificial Intelligence, Argentina, 2015-07-25 - 2015-07-31
Organisations: Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 376632
URI: https://eprints.soton.ac.uk/id/eprint/376632
ISBN: 978-1-57735-738-4
PURE UUID: 5ccdb19b-7219-4b3f-aff0-9f50e4299da9

Catalogue record

Date deposited: 06 May 2015 10:22
Last modified: 17 May 2018 16:32

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Contributors

Author: Frederik Auffenberg
Author: Sebastian Stein
Author: Alex Rogers

University divisions

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