Frederik Ruelens

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A demand response aggregator, that manages a large cluster of heterogeneous flexibility carriers, faces a complex optimal control problem. Moreover, in most applications of demand response an exact description of the system dynamics and constraints is unavailable, and information comes mostly from observations of system trajectories. This paper presents a(More)
Driven by recent advances in batch Reinforcement Learning (RL), this paper contributes to the application of batch RL to demand response. In contrast to conventional modelbased approaches, batch RL techniques do not require a system identification step, which makes them more suitable for a largescale implementation. This paper extends fitted Q-iteration, a(More)
Driven by the opportunity to harvest the flexibility related to building climate control for demand response applications, this work presents a data-driven control approach building upon recent advancements in reinforcement learning. More specifically, modelassisted batch reinforcement learning is applied to the setting of building climate control subjected(More)