203 citations · 527 across the 13 of their papers we have counts for
15 papers
PhysQ: A Physics Informed Reinforcement Learning Framework for Building Control
Gargya Gokhale, Bert Claessens, Chris Develder
Large-scale integration of intermittent renewable energy sources calls for substantial demand side flexibility. Given that the built environment accounts for approximately 40% of t…
Physics Informed Neural Networks for Control Oriented Thermal Modeling of Buildings
Gargya Gokhale, Bert Claessens, Chris Develder
This paper presents a data-driven modeling approach for developing control-oriented thermal models of buildings. These models are developed with the objective of reducing energy co…
Optimal Combination of Frequency Control and Peak Shaving with Battery Storage Systems
Jonas Engels, Bert Claessens, Geert Deconinck
Combining revenue streams by providing multiple services with battery storage systems increases profitability and enhances the investment case. In this work, we present a novel opt…
Grid-Constrained Distributed Optimization for Frequency Control with Low-Voltage Flexibility
Jonas Engels, Bert Claessens, Geert Deconinck
Providing frequency control services with flexible assets connected to the low-voltage distribution grid, amongst which residential battery storage or electrical hot water boilers,…
Techno-Economic Analysis and Optimal Control of Battery Storage for Frequency Control Services, Applied to the German Market
Jonas Engels, Bert Claessens, Geert Deconinck
Optimal investment in battery energy storage systems, taking into account degradation, sizing and control, is crucial for the deployment of battery storage, of which providing freq…
Trajectory tracking with an aggregation of domestic hot water heaters: Combining model-based and model-free control in a commercial deployment
Mingxi Liu, Stef Peeters, Duncan S. Callaway +1
Scalable demand response of residential electric loads has been a timely research topic in recent years. The commercial coming of age or residential demand response requires a scal…
Combined Stochastic Optimization of Frequency Control and Self-Consumption with a Battery
Jonas Engels, Bert Claessens, Geert Deconinck
Optimally combining frequency control with self-consumption can increase revenues from battery storage systems installed behind-the-meter. This work presents an optimized control s…
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…
The use of distributed thermal storage in district heating grids for demand side management
Dirk Vanhoudt, Bert Claessens, Robbe Salenbien +1
The work presented in this paper relates to a small scale district heating network heated by a gas fired CHP. In most common situations, such a CHP is heat driven operated, meaning…
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…
Learning Agent for a Heat-Pump Thermostat With a Set-Back Strategy Using Model-Free Reinforcement Learning
Frederik Ruelens, Sandro Iacovella, Bert J. Claessens +1
The conventional control paradigm for a heat pump with a less efficient auxiliary heating element is to keep its temperature set point constant during the day. This constant temper…
Residential Demand Response Applications Using Batch Reinforcement Learning
Frederik Ruelens, Bert Claessens, Stijn Vandael +3
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 model-based ap…