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Data-driven adaptive building thermal controller tuning with constraints: A primal–dual contextual Bayesian optimization approach

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  • Xu, Wenjie
  • Svetozarevic, Bratislav
  • Di Natale, Loris
  • Heer, Philipp
  • Jones, Colin N.

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 Primal-Dual Contextual Bayesian Optimization (PDCBO) approach to solve this problem.

Suggested Citation

  • Xu, Wenjie & Svetozarevic, Bratislav & Di Natale, Loris & Heer, Philipp & Jones, Colin N., 2024. "Data-driven adaptive building thermal controller tuning with constraints: A primal–dual contextual Bayesian optimization approach," Applied Energy, Elsevier, vol. 358(C).
    • Handle: RePEc:eee:appene:v:358:y:2024:i:c:s0306261923018573
    DOI: 10.1016/j.apenergy.2023.122493
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