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A comfort-based approach to smart heating and air conditioning

A comfort-based approach to smart heating and air conditioning
A comfort-based approach to smart heating and air conditioning
In this paper, we address the interrelated challenges of predicting user comfort and using this to reduce energy consumption in smart heating, ventilation and air conditioning (HVAC) systems. At present, such systems use simple models of user comfort when deciding on a set point temperature. Being built using broad population statistics, these models generally fail to represent individual users’ preferences, resulting in poor estimates of the users’ preferred temperatures. To address this issue, we propose the Bayesian Comfort Model (BCM). This personalised thermal comfort model uses a Bayesian network to learn from a user’s feedback, allowing it to adapt to the users’ individual preferences over time. We further propose an alternative to the ASHRAE 7-point scale used to assess user comfort. Using this model, we create an optimal HVAC control algorithm that minimizes energy consumption while preserving user comfort. Through an empirical evaluation based on the ASHRAE RP-884 data set and data collected in a separate deployment by us, we show that our model is consistently 13.2 to 25.8% more accurate than current models and how using our alternative comfort scale can increase our model’s accuracy. Through simulations we show that using this model, our HVAC control algorithm can reduce energy consumption by 7.3% to 13.5% while decreasing user discomfort by 24.8% simultaneously.
2157-6904
Auffenberg, Frederik
98237584-a003-4149-99bc-c4521eb0527d
Snow, Stephen
1ba928e0-a4d7-4392-ae59-31ac8467eb94
Stein, Sebastian
cb2325e7-5e63-475e-8a69-9db2dfbdb00b
Rogers, Alex
60b99721-b556-4805-ab34-deb808a8666c
Auffenberg, Frederik
98237584-a003-4149-99bc-c4521eb0527d
Snow, Stephen
1ba928e0-a4d7-4392-ae59-31ac8467eb94
Stein, Sebastian
cb2325e7-5e63-475e-8a69-9db2dfbdb00b
Rogers, Alex
60b99721-b556-4805-ab34-deb808a8666c

Auffenberg, Frederik, Snow, Stephen, Stein, Sebastian and Rogers, Alex (2018) A comfort-based approach to smart heating and air conditioning. ACM Transactions on Intelligent Systems and Technology, 9 (3). (doi:10.1145/3057730).

Record type: Article

Abstract

In this paper, we address the interrelated challenges of predicting user comfort and using this to reduce energy consumption in smart heating, ventilation and air conditioning (HVAC) systems. At present, such systems use simple models of user comfort when deciding on a set point temperature. Being built using broad population statistics, these models generally fail to represent individual users’ preferences, resulting in poor estimates of the users’ preferred temperatures. To address this issue, we propose the Bayesian Comfort Model (BCM). This personalised thermal comfort model uses a Bayesian network to learn from a user’s feedback, allowing it to adapt to the users’ individual preferences over time. We further propose an alternative to the ASHRAE 7-point scale used to assess user comfort. Using this model, we create an optimal HVAC control algorithm that minimizes energy consumption while preserving user comfort. Through an empirical evaluation based on the ASHRAE RP-884 data set and data collected in a separate deployment by us, we show that our model is consistently 13.2 to 25.8% more accurate than current models and how using our alternative comfort scale can increase our model’s accuracy. Through simulations we show that using this model, our HVAC control algorithm can reduce energy consumption by 7.3% to 13.5% while decreasing user discomfort by 24.8% simultaneously.

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More information

Accepted/In Press date: 20 February 2017
e-pub ahead of print date: 13 February 2018
Organisations: Agents, Interactions & Complexity, Electronics & Computer Science

Identifiers

Local EPrints ID: 406917
URI: https://eprints.soton.ac.uk/id/eprint/406917
ISSN: 2157-6904
PURE UUID: 47a23566-0952-43b3-bdf9-d1e6e29b167b

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Date deposited: 28 Mar 2017 01:04
Last modified: 22 Mar 2018 05:01

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Contributors

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

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