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Autonomous pre-conditioning and improved personalization in shared workspaces through data-driven predictive control
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DOI：10.1016/j.enbuild.2023.112897.png)
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Abstract
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This paper studies the problem of indoor zone temperature control in shared workspaces equipped with heterogeneous heating and cooling sources with the goal of increased energy savings and environment personalization. Shared workspaces typically witness distinct, pre-scheduled intervals when they are occupied or are unoccupied. In this work, we develop indoor climate control strategies for each of these intervals. For the interval when the workspace is unoccupied, we propose multiple time-bound control strategies for pre-conditioning the workspace in preparation for a scheduled activity (Phase I). For the interval when the workspace is occupied, we propose a separate control strategy which enhances the thermal comfort of the occupants by harnessing the spatial differentiation of the thermal environment to satisfy the different temperature preferences of the individuals (Phase II). Utilizing a physical test-bed and data-driven model learning, we show that our proposed pre-conditioning strategies in Phase I are less computationally expensive than conventional model predictive control (MPC). For Phase II, we use a low complexity quadratic program to minimize the thermal discomfort experienced by individuals based on their temperature preferences. The experimental results show that for Phase I, the proposed control policies can save a significant amount of energy and achieve the desired mean temperature in the space fairly accurately. We further note that for Phase II, the control scheme can achieve a significant spatial differentiation in temperature towards satisfying the occupants' thermal preferences.(c) 2023 Elsevier B.V. All rights reserved.
Keywords:
Energy efficiency
Indoor climate control
HVAC
Environment personalization
MPC
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