no code implementations • 18 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.
no code implementations • 18 Mar 2024 • Gargya Gokhale, Seyed Soroush Karimi Madahi, Bert Claessens, Chris Develder
Accounting for about 25% of final energy consumption globally, the residential sector is an important (potential) source of energy flexibility.
no code implementations • 23 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.
no code implementations • 6 Dec 2023 • Fabio Pavirani, Gargya Gokhale, Bert Claessens, Chris Develder
Thus, we study MCTS specifically for building demand response.
no code implementations • 29 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).
no code implementations • 29 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.
no code implementations • 21 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.
1 code implementation • 23 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.
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.