28 citations · 61 across the 8 of their papers we have counts for
8 papers
Real-world validation of safe reinforcement learning, model predictive control and decision tree-based home energy management systems
Julian Ruddick, Glenn Ceusters, Gilles Van Kriekinge +4
Recent advancements in machine learning based energy management approaches, specifically reinforcement learning with a safety layer (OptLayerPolicy) and a metaheuristic algorithm g…
Balancing Forecast Accuracy and Switching Costs in Online Optimization of Energy Management Systems
Evgenii Genov, Julian Ruddick, Christoph Bergmeir +4
This study investigates the integration of forecasting and optimization in energy management systems, with a focus on the role of switching costs -- penalties incurred from frequen…
An adaptive safety layer with hard constraints for safe reinforcement learning in multi-energy management systems
Glenn Ceusters, Muhammad Andy Putratama, Rüdiger Franke +2
Safe reinforcement learning (RL) with hard constraint guarantees is a promising optimal control direction for multi-energy management systems. It only requires the environment-spec…
TreeC: a method to generate interpretable energy management systems using a metaheuristic algorithm
Julian Ruddick, Luis Ramirez Camargo, Muhammad Andy Putratama +2
Energy management systems (EMS) have traditionally been implemented using rule-based control (RBC) and model predictive control (MPC) methods. However, recent research has explored…
Safe reinforcement learning for multi-energy management systems with known constraint functions
Glenn Ceusters, Luis Ramirez Camargo, Rüdiger Franke +2
Reinforcement learning (RL) is a promising optimal control technique for multi-energy management systems. It does not require a model a priori - reducing the upfront and ongoing pr…
Renewable energy communities: do they have a business case in Flanders?
Alex Felice, Lucija Rakocevic, Leen Peters +3
Renewable energy communities (RECs) are prominent initiatives to provide end consumers an active role in the energy sector, raise awareness on the importance of renewable energy (R…
Evolutionary scheduling of university activities based on consumption forecasts to minimise electricity costs
Julian Ruddick, Evgenii Genov, Luis Ramirez Camargo +2
This paper presents a solution to a predict then optimise problem which goal is to reduce the electricity cost of a university campus. The proposed methodology combines a multi-dim…
Model-predictive control and reinforcement learning in multi-energy system case studies
Glenn Ceusters, Román Cantú Rodríguez, Alberte Bouso García +6
Model-predictive-control (MPC) offers an optimal control technique to establish and ensure that the total operation cost of multi-energy systems remains at a minimum while fulfilli…