Authors:
Jeroen Willems
1
;
Denis Steckelmacher
2
;
Wouter Scholte
1
;
Bruno Depraetere
1
;
Edward Kikken
1
;
Abdellatif Bey-Temsamani
1
and
Ann Nowé
2
Affiliations:
1
Flanders Make, Lommel, Belgium
;
2
Vrije Universiteit Brussel, Belgium
Keyword(s):
Predictive Control, Reinforcement Learning, Thermal Systems Control.
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.