Reinforcement Learning for Model-Free Control of a Cooling Network with Uncertain Future Demands

Jeroen Willems, Denis Steckelmacher, Wouter Scholte, Bruno Depraetere, Edward Kikken, Abdellatif Bey-Temsamani, Ann Nowé

2025

Abstract

Optimal control of complex systems often requires access to a high-fidelity model, and information about the (future) external stimuli applied to the system (load, demand, ...). An example of such a system is a cooling network, in which one or more chillers provide cooled liquid to a set of users with a variable demand. In this paper, we propose a Reinforcement Learning (RL) method for such a system with 3 chillers. It does not assume any model, and does not observe the future cooling demand, nor approximations of it. Still, we show that, after a training phase in a simulator, the learned controller achieves a performance better than classical rule-based controllers, and similar to a model predictive controller that does rely on a model and demand predictions. We show that the RL algorithm has learned implicitly how to anticipate, without requiring explicit predictions. This demonstrates that RL can allow to produce high-quality controllers in challenging industrial contexts.

Paper Citation

in Harvard Style

Willems J., Steckelmacher D., Scholte W., Depraetere B., Kikken E., Bey-Temsamani A. and Nowé A. (2025). Reinforcement Learning for Model-Free Control of a Cooling Network with Uncertain Future Demands. In Proceedings of the 22nd International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO; ISBN 978-989-758-770-2, SciTePress, pages 59-70. DOI: 10.5220/0013708300003982

in Bibtex Style

@conference{icinco25,
author={Jeroen Willems and Denis Steckelmacher and Wouter Scholte and Bruno Depraetere and Edward Kikken and Abdellatif Bey-Temsamani and Ann Nowé},
title={Reinforcement Learning for Model-Free Control of a Cooling Network with Uncertain Future Demands},
booktitle={Proceedings of the 22nd International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO},
year={2025},
pages={59-70},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013708300003982},
isbn={978-989-758-770-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 22nd International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO
TI - Reinforcement Learning for Model-Free Control of a Cooling Network with Uncertain Future Demands
SN - 978-989-758-770-2
AU - Willems J.
AU - Steckelmacher D.
AU - Scholte W.
AU - Depraetere B.
AU - Kikken E.
AU - Bey-Temsamani A.
AU - Nowé A.
PY - 2025
SP - 59
EP - 70
DO - 10.5220/0013708300003982
PB - SciTePress