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A heating agent using a personalised thermal comfort model to save energy

A heating agent using a personalised thermal comfort model to save energy
A heating agent using a personalised thermal comfort model to save energy
We present a novel, personalised thermal comfort model and a heating agent using this model to reduce energy consumption with minimal comfort loss. At present, heating agents typically use simple models of user comfort when deciding on a set point temperature for the heating or cooling system. These models however generally fail to adapt to an individual user's preferences, resulting in poor performance. To address this issue, we propose a personalised thermal comfort model using 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 17.5-23.5% more accurate than current models, regardless of environmental conditions and type of heating system. Further, our model has several additional outputs such as expected user feedback, optimal comfort temperature and thermal sensitivity that allow it to save between 18-20% of energy while still maintaining comfort.
smart heating, thermal comfort, Bayesian networks, agent-based control, human-agent collectives
1799-1800
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 heating agent using a personalised thermal comfort model to save energy. International Conference on Autonomous Agents & Multiagent Systems, Istanbul, Turkey. pp. 1799-1800 .

Record type: Conference or Workshop Item (Paper)

Abstract

We present a novel, personalised thermal comfort model and a heating agent using this model to reduce energy consumption with minimal comfort loss. At present, heating agents typically use simple models of user comfort when deciding on a set point temperature for the heating or cooling system. These models however generally fail to adapt to an individual user's preferences, resulting in poor performance. To address this issue, we propose a personalised thermal comfort model using 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 17.5-23.5% more accurate than current models, regardless of environmental conditions and type of heating system. Further, our model has several additional outputs such as expected user feedback, optimal comfort temperature and thermal sensitivity that allow it to save between 18-20% of energy while still maintaining comfort.

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More information

Accepted/In Press date: 28 January 2015
e-pub ahead of print date: 1 May 2015
Venue - Dates: International Conference on Autonomous Agents & Multiagent Systems, Istanbul, Turkey, 2015-01-28
Keywords: smart heating, thermal comfort, Bayesian networks, agent-based control, human-agent collectives
Organisations: Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 376541
URI: https://eprints.soton.ac.uk/id/eprint/376541
PURE UUID: 53c84dd8-c50f-4abc-9a03-195679522010

Catalogue record

Date deposited: 06 May 2015 10:15
Last modified: 19 Jul 2019 20:46

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Contributors

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

University divisions

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