1. Data-driven adaptive building thermal controller tuning with constraints: A primal–dual contextual Bayesian optimization approach.
- Author
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Xu, Wenjie, Svetozarevic, Bratislav, Di Natale, Loris, Heer, Philipp, and Jones, Colin N.
- Subjects
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ENERGY consumption , *THERMAL comfort , *CONSTRAINED optimization , *ALTERNATIVE fuels , *PROBLEM solving - Abstract
We study the problem of tuning the parameters of a room temperature controller to minimize its energy consumption, subject to the constraint that the daily cumulative thermal discomfort of the occupants is below a given threshold. We formulate it as an online constrained black-box optimization problem where, on each day, we observe some relevant environmental context and adaptively select the controller parameters. In this paper, we propose to use a data-driven P rimal- D ual C ontextual B ayesian O ptimization (PDCBO) approach to solve this problem. In a simulation case study on a single room, we apply our algorithm to tune the parameters of a Proportional Integral (PI) heating controller and the pre-heating time. Our results show that PDCBO can save up to 4.7% energy consumption compared to other state-of-the-art Bayesian optimization-based methods while keeping the daily thermal discomfort below the given tolerable threshold on average. Additionally, PDCBO can automatically track time-varying tolerable thresholds while existing methods fail to do so. We then study an alternative constrained tuning problem where we aim to minimize the thermal discomfort with a given energy budget. With this formulation, PDCBO reduces the average discomfort by up to 63% compared to state-of-the-art safe optimization methods while keeping the average daily energy consumption below the required threshold. [Display omitted] • A primal–dual contextual Bayesian optimization framework to tune building controllers is proposed. • It asymptotically achieves the optimal performance while satisfying constraints on average. • It saves up to 4.7% energy compared to alternatives, satisfying thermal comfort constraints on average. • Alternatively, it can reduce the thermal discomfort by 63% within a given energy budget. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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