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research article
Data-driven adaptive building thermal controller tuning with constraints: A primal-dual contextual Bayesian optimization approach
January 9, 2024
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.
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Type
research article
Web of Science ID
WOS:001154992100001
Authors
Publication date
2024-01-09
Publisher
Published in
Volume
358
Article Number
122493
Peer reviewed
REVIEWED
EPFL units
Funder | Grant Number |
Swiss National Science Foundation, Switzerland under NCCR Automation | 51NF40_180545 |
Swiss Data Science Center, Switzerland | C20-13 |
Available on Infoscience
February 23, 2024