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A comfort-based, energy-aware HVAC agent and its applications in the smart grid

A comfort-based, energy-aware HVAC agent and its applications in the smart grid
A comfort-based, energy-aware HVAC agent and its applications in the smart grid
In this thesis, we introduce a novel heating, ventilation and air conditioning (HVAC) agent that maintains a comfortable thermal environmant for its users while minimising energy consumption of the HVAC system and incorporating demand side management (DSM) signals to shift HVAC loads towards achieving more desirable overall load profiles. To do so, the agent needs to be able to accurately predict user comfort, for example by using a thermal comfort model. Existing thermal comfort models are usually built using broad population statistics, meaning that they 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 using a Bayesian network learns 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. We extend this algorithm to incorporate DSM signals into its scheduling, allowing it to shift HVAC loads towards more desirable load profiles, reduce peaks or make better use of energy produced from renewable sources. 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 comfort model is consistently 13.2% to 25.8% more accurate than current models and that the alternative comfort scale can increase our model's accuracy. Through simulations we show that when using the comfort model instead of a fixed set point, our HVAC control algorithm can reduce energy consumption of the HVAC system by 11% while decreasing user discomfort by 17.5%, achieve a load profile 39.9% closer to a specified target profile and efficiently reduce peaks in the load profile.
University of Southampton
Auffenberg, Frederik
98237584-a003-4149-99bc-c4521eb0527d
Auffenberg, Frederik
98237584-a003-4149-99bc-c4521eb0527d
Stein, Sebastian
cb2325e7-5e63-475e-8a69-9db2dfbdb00b
Rogers, Alexander
f9130bc6-da32-474e-9fab-6c6cb8077fdc

Auffenberg, Frederik (2017) A comfort-based, energy-aware HVAC agent and its applications in the smart grid. University of Southampton, Doctoral Thesis, 125pp.

Record type: Thesis (Doctoral)

Abstract

In this thesis, we introduce a novel heating, ventilation and air conditioning (HVAC) agent that maintains a comfortable thermal environmant for its users while minimising energy consumption of the HVAC system and incorporating demand side management (DSM) signals to shift HVAC loads towards achieving more desirable overall load profiles. To do so, the agent needs to be able to accurately predict user comfort, for example by using a thermal comfort model. Existing thermal comfort models are usually built using broad population statistics, meaning that they 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 using a Bayesian network learns 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. We extend this algorithm to incorporate DSM signals into its scheduling, allowing it to shift HVAC loads towards more desirable load profiles, reduce peaks or make better use of energy produced from renewable sources. 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 comfort model is consistently 13.2% to 25.8% more accurate than current models and that the alternative comfort scale can increase our model's accuracy. Through simulations we show that when using the comfort model instead of a fixed set point, our HVAC control algorithm can reduce energy consumption of the HVAC system by 11% while decreasing user discomfort by 17.5%, achieve a load profile 39.9% closer to a specified target profile and efficiently reduce peaks in the load profile.

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Published date: March 2017

Identifiers

Local EPrints ID: 415753
URI: https://eprints.soton.ac.uk/id/eprint/415753
PURE UUID: 874cd4cb-7014-441d-a1b9-a89e80b16507

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Date deposited: 22 Nov 2017 17:30
Last modified: 13 Mar 2019 19:14

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