Lessons Learned from Data-Driven Building Control Experiments:
Contrasting Gaussian Process-based MPC, Bilevel DeePC, and Deep Reinforcement
Learning
- URL: http://arxiv.org/abs/2205.15703v1
- Date: Tue, 31 May 2022 11:40:22 GMT
- Title: Lessons Learned from Data-Driven Building Control Experiments:
Contrasting Gaussian Process-based MPC, Bilevel DeePC, and Deep Reinforcement
Learning
- Authors: Loris Di Natale, Yingzhao Lian, Emilio T. Maddalena, Jicheng Shi and
Colin N. Jones
- Abstract summary: This manuscript offers the perspective of experimentalists on a number of modern data-driven techniques.
It is compared in terms of data requirements, ease of use, computational burden, and robustness in the context of real-world applications.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: This manuscript offers the perspective of experimentalists on a number of
modern data-driven techniques: model predictive control relying on Gaussian
processes, adaptive data-driven control based on behavioral theory, and deep
reinforcement learning. These techniques are compared in terms of data
requirements, ease of use, computational burden, and robustness in the context
of real-world applications. Our remarks and observations stem from a number of
experimental investigations carried out in the field of building control in
diverse environments, from lecture halls and apartment spaces to a hospital
surgery center. The final goal is to support others in identifying what
technique is best suited to tackle their own problems.
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