Improvement of Refrigeration Efficiency by Combining Reinforcement Learning with a Coarse Model

Zhang, Dapeng and Gao, Zhiwei (2019) Improvement of Refrigeration Efficiency by Combining Reinforcement Learning with a Coarse Model. Processes, 7 (12). p. 967. ISSN 2227-9717

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Official URL: https://doi.org/10.3390/PR7120967

Abstract

It is paramount to improve operational conversion efficiency in air-conditioning refrigeration. It is noticed that control efficiency for model-based methods highly relies on the accuracy of the mechanism model, and data-driven methods would face challenges using the limited collected data to identify the information beyond. In this study, a hybrid novel approach is presented, which is to integrate a data-driven method with a coarse model. Specifically, reinforcement learning is used to exploit/explore the conversion efficiency of the refrigeration, and a coarse model is utilized to evaluate the reward, by which the requirement of the model accuracy is reduced and the model information is better used. The proposed approach is implemented based on a hierarchical control strategy which is divided into a process level and a loop level. The simulation of a test bed shows the proposed approach can achieve better conversion efficiency of refrigeration than the conventional methods.

Item Type: Article
Additional Information: Funding Information: Funding: This research was funded by the National Science Foundation of China under grant 61673074. Funding Information: Acknowledgments: The authors would like to acknowledge the research support from the School of Electrical Engineering and Automation at Tianjin University, the National Science Foundation of China under grant 61673074; and the Faculty of Engineering and Environment at the University of Northumbria, Newcastle.
Uncontrolled Keywords: Coarse model, Data-driven methods, Refrigeration, Reinforcement learning
Subjects: H800 Chemical, Process and Energy Engineering
H900 Others in Engineering
Department: Faculties > Engineering and Environment > Mathematics, Physics and Electrical Engineering
Depositing User: Rachel Branson
Date Deposited: 01 Mar 2021 11:07
Last Modified: 31 May 2021 14:42
URI: http://nrl.northumbria.ac.uk/id/eprint/45558

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