Search Results for author: Bert Claessens

Found 11 papers, 1 papers with code

Explainable Reinforcement Learning-based Home Energy Management Systems using Differentiable Decision Trees

no code implementations18 Mar 2024 Gargya Gokhale, Bert Claessens, Chris Develder

We aim to address this challenging problem and introduce a reinforcement learning-based approach using differentiable decision trees.

energy management Management +1

Distributional Reinforcement Learning-based Energy Arbitrage Strategies in Imbalance Settlement Mechanism

no code implementations23 Dec 2023 Seyed Soroush Karimi Madahi, Bert Claessens, Chris Develder

Our proposed control framework takes a risk-sensitive perspective, allowing BRPs to adjust their risk preferences: we aim to optimize a weighted sum of the arbitrage profit and a risk measure while constraining the daily number of cycles for the battery.

Distributional Reinforcement Learning Q-Learning +1

Transfer Learning in Transformer-Based Demand Forecasting For Home Energy Management System

no code implementations29 Oct 2023 Gargya Gokhale, Jonas Van Gompel, Bert Claessens, Chris Develder

Specifically, we train an advanced forecasting model (a temporal fusion transformer) using data from multiple different households, and then finetune this global model on a new household with limited data (i. e. only a few days).

energy management Load Forecasting +3

Real-World Implementation of Reinforcement Learning Based Energy Coordination for a Cluster of Households

no code implementations29 Oct 2023 Gargya Gokhale, Niels Tiben, Marie-Sophie Verwee, Manu Lahariya, Bert Claessens, Chris Develder

Given its substantial contribution of 40\% to global power consumption, the built environment has received increasing attention to serve as a source of flexibility to assist the modern power grid.

energy management Reinforcement Learning (RL)

PhysQ: A Physics Informed Reinforcement Learning Framework for Building Control

no code implementations21 Nov 2022 Gargya Gokhale, Bert Claessens, Chris Develder

As a physics-informed reinforcement learning framework for building control, PhysQ forms a step in bridging the gap between conventional model-based control and data-intensive control based on reinforcement learning.

reinforcement-learning Reinforcement Learning (RL) +1

Physics Informed Neural Networks for Control Oriented Thermal Modeling of Buildings

1 code implementation23 Nov 2021 Gargya Gokhale, Bert Claessens, Chris Develder

To combine the interpretability of white/gray box physics models and the expressive power of neural networks, we propose a physics informed neural network approach for this modeling task.

Reinforcement Learning Applied to an Electric Water Heater: From Theory to Practice

no code implementations29 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.

Decision Making reinforcement-learning +1

Experimental analysis of data-driven control for a building heating system

no code implementations13 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.

Decision Making reinforcement-learning +1

Residential Demand Response Applications Using Batch Reinforcement Learning

no code implementations8 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.

reinforcement-learning Reinforcement Learning (RL)

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