28 citations · 61 across the 8 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…