8 citations · 17 across the 14 of their papers we have counts for
14 papers
Control-Oriented Scenario Tree Construction through Reinforcement Learning
Fabio Pavirani, Bert Claessens, Pierre Pinson +1
Multistage stochastic model predictive control (MPC) handles uncertainty by optimizing over a scenario tree, a finite branching approximation of future outcomes constructed from sa…
S3TS: Stochastic Scenario-Structured Tree Search for Advanced Planning Under Uncertainty
Fabio Pavirani, Bert Claessens, Pierre Pinson +1
Effective scheduling in the energy sector is essential to ensure the reliable operation of electrical grids and their connected assets by, for instance, optimizing the dispatch of…
Neural Network-Assisted Model Predictive Control for Implicit Balancing
Seyed Soroush Karimi Madahi, Kenneth Bruninx, Bert Claessens +1
In Europe, balance responsible parties can deliberately take out-of-balance positions to support transmission system operators (TSOs) in maintaining grid stability and earn profit,…
Model Predictive Control-Guided Reinforcement Learning for Implicit Balancing
Seyed Soroush Karimi Madahi, Kenneth Bruninx, Bert Claessens +1
In Europe, profit-seeking balance responsible parties can deviate in real time from their day-ahead nominations to assist transmission system operators in maintaining the supply-de…
Gaming Strategies in European Imbalance Settlement Mechanisms
Seyed Soroush Karimi Madahi, Kenneth Bruninx, Bert Claessens +1
Transmission System Operators (TSOs) rely on balancing energy provided by Balancing Service Providers (BSPs) to maintain the supply-demand balance in real time. Balance Responsible…
Predicting and Publishing Accurate Imbalance Prices Using Monte Carlo Tree Search
Fabio Pavirani, Jonas Van Gompel, Seyed Soroush Karimi Madahi +2
The growing reliance on renewable energy sources, particularly solar and wind, has introduced challenges due to their uncontrollable production. This complicates maintaining the el…
Control Policy Correction Framework for Reinforcement Learning-based Energy Arbitrage Strategies
Seyed Soroush Karimi Madahi, Gargya Gokhale, Marie-Sophie Verwee +2
A continuous rise in the penetration of renewable energy sources, along with the use of the single imbalance pricing, provides a new opportunity for balance responsible parties to…
Probabilistic forecasting of power system imbalance using neural network-based ensembles
Jonas Van Gompel, Bert Claessens, Chris Develder
Keeping the balance between electricity generation and consumption is becoming increasingly challenging and costly, mainly due to the rising share of renewables, electric vehicles…
Explainable Reinforcement Learning-based Home Energy Management Systems using Differentiable Decision Trees
Gargya Gokhale, Bert Claessens, Chris Develder
With the ongoing energy transition, demand-side flexibility has become an important aspect of the modern power grid for providing grid support and allowing further integration of s…
Distill2Explain: Differentiable decision trees for explainable reinforcement learning in energy application controllers
Gargya Gokhale, Seyed Soroush Karimi Madahi, Bert Claessens +1
Demand-side flexibility is gaining importance as a crucial element in the energy transition process. Accounting for about 25% of final energy consumption globally, the residential…
Distributional Reinforcement Learning-based Energy Arbitrage Strategies in Imbalance Settlement Mechanism
Seyed Soroush Karimi Madahi, Bert Claessens, Chris Develder
Growth in the penetration of renewable energy sources makes supply more uncertain and leads to an increase in the system imbalance. This trend, together with the single imbalance p…
Demand response for residential building heating: Effective Monte Carlo Tree Search control based on physics-informed neural networks
Fabio Pavirani, Gargya Gokhale, Bert Claessens +1
To reduce global carbon emissions and limit climate change, controlling energy consumption in buildings is an important piece of the puzzle. Here, we specifically focus on using a…
Transfer Learning in Transformer-Based Demand Forecasting For Home Energy Management System
Gargya Gokhale, Jonas Van Gompel, Bert Claessens +1
Increasingly, homeowners opt for photovoltaic (PV) systems and/or battery storage to minimize their energy bills and maximize renewable energy usage. This has spurred the developme…
Real-World Implementation of Reinforcement Learning Based Energy Coordination for a Cluster of Households
Gargya Gokhale, Niels Tiben, Marie-Sophie Verwee +3
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 mo…