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Poster Abstract: Data Efficient HVAC Control using Gaussian Process-based Reinforcement Learning

Published: 26 April 2024 Publication History

Abstract

Model-based Reinforcement Learning (MBRL) has been widely studied for energy-efficient control of the Heating, Ventilation, and Air Conditioning (HVAC) systems. One of the fundamental issues of the current approaches is the large amount of data required to train an accurate building system dynamics model. In this work, we developed a data-efficient system capable of excellent HVAC control performance with only days of training data. We use a Gaussian Process (GP) as the dynamics model which provides uncertainty for each prediction. To improve the data efficiency, we designed a meta kernel learning technique for GP kernel selection. To incorporate uncertainty in the control decisions, we designed a model predictive control method that considers the uncertainty of every prediction. Simulation experiments show that our method achieves excellent data efficiency, yielding similar energy savings and 12.07% less human comfort violation compared with the state-of-the-art MBRL method, while only trained on a seven-day training dataset.

References

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Zhiyu An, Xianzhong Ding, Arya Rathee, and Wan Du. 2023. Clue: safe modelbased rl hvac control using epistemic uncertainty estimation. In ACM BuildSys.
[2]
Xianzhong Ding, Wan Du, and Alberto Cerpa. 2019. Octopus: deep reinforcement learning for holistic smart building control. In ACM BuildSys, 326--335.
[3]
Xianzhong Ding, Wan Du, and Alberto E Cerpa. 2020. Mb2c: model-based deep reinforcement learning for multi-zone building control. In ACM BuildSys, 50--59.
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DoE. 2010. Energyplus input output reference. US Department of Energy.
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Miaomiao Liu, Sikai Yang, Wyssanie Chomsin, and Wan Du. 2022. Real-time tracking of smartwatch orientation and location by multitask learning. In SenSys, 120--133.
[6]
ASHRAE STANDARD. 2020. Ansi/ashrae addendum a to ansi/ashrae standard 169-2020. ASHRAE Standing Standard Project Committee.

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Published In

cover image ACM Conferences
SenSys '23: Proceedings of the 21st ACM Conference on Embedded Networked Sensor Systems
November 2023
574 pages
ISBN:9798400704147
DOI:10.1145/3625687
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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 April 2024

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Author Tags

  1. epistemic uncertainty estimation
  2. model-based reinforcement learning
  3. HVAC control
  4. model predictive control

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Overall Acceptance Rate 198 of 990 submissions, 20%

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