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pro vyhledávání: '"Ceusters, Glenn"'
Autor:
Ruddick, Julian, Ceusters, Glenn, Van Kriekinge, Gilles, Genov, Evgenii, Coosemans, Thierry, Messagie, Maarten
Recent advancements in machine learning based energy management approaches, specifically reinforcement learning with a safety layer (OptLayerPolicy) and a metaheuristic algorithm generating a decision tree control policy (TreeC), have shown promise.
Externí odkaz:
http://arxiv.org/abs/2408.07435
Safe reinforcement learning (RL) with hard constraint guarantees is a promising optimal control direction for multi-energy management systems. It only requires the environment-specific constraint functions itself a priori and not a complete model. Th
Externí odkaz:
http://arxiv.org/abs/2304.08897
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 project-specific engineering effort and is capable of learning better re
Externí odkaz:
http://arxiv.org/abs/2207.03830
Autor:
Ceusters, Glenn, Rodríguez, Román Cantú, García, Alberte Bouso, Franke, Rüdiger, Deconinck, Geert, Helsen, Lieve, Nowé, Ann, Messagie, Maarten, Camargo, Luis Ramirez
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 fulfilling all system constraints. However, this method presumes an adequate m
Externí odkaz:
http://arxiv.org/abs/2104.09785
Publikováno v:
In Energy and AI April 2023 12
Publikováno v:
Frontiers in Control Engineering; 8/17/2023, p1-14, 14p
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