no code implementations • 26 Jul 2017 • Frederik Ruelens, Bert J. Claessens, Peter Vrancx, Fred Spiessens, Geert Deconinck
This paper considers a demand response agent that must find a near-optimal sequence of decisions based on sparse observations of its environment.
no code implementations • 27 Jan 2017 • Bert J. Claessens, Dirk Vanhoudt, Johan Desmedt, Frederik Ruelens
Optimal control of thermostatically controlled loads connected to a district heating network is considered a sequential decision- making problem under uncertainty.
1 code implementation • 28 Apr 2016 • Bert J. Claessens, Peter Vrancx, Frederik Ruelens
Direct load control of a heterogeneous cluster of residential demand flexibility sources is a high-dimensional control problem with partial observability.
no code implementations • 29 Nov 2015 • Frederik Ruelens, Bert Claessens, Salman Quaiyum, Bart De Schutter, Robert Babuska, Ronnie Belmans
A wellknown batch reinforcement learning technique, fitted Q-iteration, is used to find a control policy, given this feature representation.
no code implementations • 13 Jul 2015 • Giuseppe Tommaso Costanzo, Sandro Iacovella, Frederik Ruelens, T. Leurs, Bert Claessens
From the quantitative analysis it has been found that the control approach converges in approximately 20 days to obtain a control policy with a performance within 90% of the mathematical optimum.
no code implementations • 8 Apr 2015 • Frederik Ruelens, Bert Claessens, Stijn Vandael, Bart De Schutter, Robert Babuska, Ronnie Belmans
We propose a model-free Monte-Carlo estimator method that uses a metric to construct artificial trajectories and we illustrate this method by finding the day-ahead schedule of a heat-pump thermostat.