Rethinking comfort profiles in adaptive building energy management systems
Rethinking comfort profiles in adaptive building energy management systems
As standard building occupancy schedules continue to change from static closed-door offices to dynamic open office layouts, we face new challenges for developing smart building energy management systems (BEMS) that can simultaneously adapt to save energy costs, while also incorporating the comfort preferences of the occupants. This is especially true for certain building types which by design are open layout, or partially-open layout such as schools, hospitals, and libraries. In this paper, we identify and explain three of the most critical challenges that specifically relate to incorporating feedback from building occupants into an interactive reinforcement learning algorithm. For each challenge, we propose how the challenge could be dealt with practically, within the context of our ongoing work and experimentation in this area. Overcoming these challenges opens new opportunities for artificial intelligence solutions that will place citizens in the centre and also help smart building
designers move toward net-zero goals.
26--32
Williams, Jennifer
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Shafipour Yourdshahi, Elnaz
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Shi, Gongwei
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Stein, Sebastian
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30 May 2023
Williams, Jennifer
3a1568b4-8a0b-41d2-8635-14fe69fbb360
Shafipour Yourdshahi, Elnaz
a2e1dea9-d3c0-4288-afdc-197df65f2556
Shi, Gongwei
8d7c6973-63eb-462d-803b-48bcc8b5206f
Stein, Sebastian
cb2325e7-5e63-475e-8a69-9db2dfbdb00b
Williams, Jennifer, Shafipour Yourdshahi, Elnaz, Shi, Gongwei and Stein, Sebastian
(2023)
Rethinking comfort profiles in adaptive building energy management systems.
In Proceedings of the International Workshop on Citizen-Centric Multiagent Systems 2023.
.
(doi:10.6084/m9.figshare.22811324.v1).
Record type:
Conference or Workshop Item
(Paper)
Abstract
As standard building occupancy schedules continue to change from static closed-door offices to dynamic open office layouts, we face new challenges for developing smart building energy management systems (BEMS) that can simultaneously adapt to save energy costs, while also incorporating the comfort preferences of the occupants. This is especially true for certain building types which by design are open layout, or partially-open layout such as schools, hospitals, and libraries. In this paper, we identify and explain three of the most critical challenges that specifically relate to incorporating feedback from building occupants into an interactive reinforcement learning algorithm. For each challenge, we propose how the challenge could be dealt with practically, within the context of our ongoing work and experimentation in this area. Overcoming these challenges opens new opportunities for artificial intelligence solutions that will place citizens in the centre and also help smart building
designers move toward net-zero goals.
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Accepted/In Press date: 27 March 2023
Published date: 30 May 2023
Venue - Dates:
22nd International Conference on Autonomous Agents and Multiagent Systems, London ExCeL conference centre, London, United Kingdom, 2023-05-29 - 2023-06-02
Identifiers
Local EPrints ID: 480535
URI: http://eprints.soton.ac.uk/id/eprint/480535
PURE UUID: 919be6c2-777a-42fb-acfb-818848219b23
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Date deposited: 04 Aug 2023 16:35
Last modified: 18 Mar 2024 03:09
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Contributors
Author:
Jennifer Williams
Author:
Elnaz Shafipour Yourdshahi
Author:
Gongwei Shi
Author:
Sebastian Stein
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