5 papers
Direct Load Control of Thermostatically Controlled Loads Based on Sparse Observations Using Deep Reinforcement Learning
Frederik Ruelens, Bert J. Claessens, Peter Vrancx +2
This paper considers a demand response agent that must find a near-optimal sequence of decisions based on sparse observations of its environment. Extracting a relevant set of featu…
Model-Free Control of Thermostatically Controlled Loads Connected to a District Heating Network
Bert J. Claessens, Dirk Vanhoudt, Johan Desmedt +1
Optimal control of thermostatically controlled loads connected to a district heating network is considered a sequential decision- making problem under uncertainty. The practicality…
Convolutional Neural Networks For Automatic State-Time Feature Extraction in Reinforcement Learning Applied to Residential Load Control
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. This work proposes a nove…
Reinforcement Learning Applied to an Electric Water Heater: From Theory to Practice
Frederik Ruelens, Bert Claessens, Salman Quaiyum +3
Electric water heaters have the ability to store energy in their water buffer without impacting the comfort of the end user. This feature makes them a prime candidate for residenti…
Experimental analysis of data-driven control for a building heating system
Giuseppe Tommaso Costanzo, Sandro Iacovella, Frederik Ruelens +2
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 buildin…