Low-carbon comfort management for smart buildings
Low-carbon comfort management for smart buildings
We present critical research challenges for the development of smart building management systems (BMS) to achieve low-carbon comfort. To date, work in this area has focused on optimising single-scope aspects of building resources, such as energy usage or thermal comfort, but there is a recent shift toward BMS design that could simultaneously address many aspects of building resources and comfort dimensions for occupants, such as air quality, temperature, humidity, audible noise levels, and related automated safety features. In this paper, we discuss four research directions highlighting current challenges in this domain that present opportunities for research: (A) data limitations for machine learning, (B) multiple definitions of comfort, (C) BMS usability and interfaces, and (D) safety and security of automated BMS decision-making. Addressing these challenges will enable the development of advanced human-centred energy-saving buildings that meet the needs of occupants.
Agent-based modeling, energy management, environmental monitoring, green buildings, human in the loop
Williams, Jennifer
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Lellouch, Benjamin
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Stein, Sebastian
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Vanderwel, Christina
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Gauthier, Stephanie
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Williams, Jennifer
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Lellouch, Benjamin
beb180ae-7699-456e-941c-c58ed4f9cb39
Stein, Sebastian
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Vanderwel, Christina
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Gauthier, Stephanie
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Williams, Jennifer, Lellouch, Benjamin, Stein, Sebastian, Vanderwel, Christina and Gauthier, Stephanie
(2022)
Low-carbon comfort management for smart buildings.
IEEE Smart Cities.
26 - 29 Sep 2022.
(doi:10.1109/ISC255366.2022.9922474).
Record type:
Conference or Workshop Item
(Paper)
Abstract
We present critical research challenges for the development of smart building management systems (BMS) to achieve low-carbon comfort. To date, work in this area has focused on optimising single-scope aspects of building resources, such as energy usage or thermal comfort, but there is a recent shift toward BMS design that could simultaneously address many aspects of building resources and comfort dimensions for occupants, such as air quality, temperature, humidity, audible noise levels, and related automated safety features. In this paper, we discuss four research directions highlighting current challenges in this domain that present opportunities for research: (A) data limitations for machine learning, (B) multiple definitions of comfort, (C) BMS usability and interfaces, and (D) safety and security of automated BMS decision-making. Addressing these challenges will enable the development of advanced human-centred energy-saving buildings that meet the needs of occupants.
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e-pub ahead of print date: 26 October 2022
Additional Information:
Funding Information:
This work is supported by the UK Engineering and Physical Sciences Research Council (EPSRC) through the Trustworthy Autonomous Systems Hub (EP/V00784X/1) and a Turing AI Acceleration Fellowship on Citizen-Centric AI Systems (EP/V022067/1). For the purpose of open access, the authors have applied a creative commons attribution (CC BY) licence to any accepted manuscript version arising.
Publisher Copyright:
© 2022 IEEE.
Venue - Dates:
IEEE Smart Cities, 2022-09-26 - 2022-09-29
Keywords:
Agent-based modeling, energy management, environmental monitoring, green buildings, human in the loop
Identifiers
Local EPrints ID: 469419
URI: http://eprints.soton.ac.uk/id/eprint/469419
PURE UUID: 37e9507d-5ccc-4937-9a5d-73e9cab33132
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Date deposited: 14 Sep 2022 16:48
Last modified: 17 Mar 2024 04:12
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Contributors
Author:
Jennifer Williams
Author:
Benjamin Lellouch
Author:
Sebastian Stein
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