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pro vyhledávání: '"Eichelbeck, Michael"'
Building-specific knowledge such as building type and function information is important for numerous energy applications. However, comprehensive datasets containing this information for individual households are missing in many regions of Europe. For
Externí odkaz:
http://arxiv.org/abs/2409.09692
The growing complexity of power system management has led to an increased interest in reinforcement learning (RL). However, vanilla RL controllers cannot themselves ensure satisfaction of system constraints. Therefore, combining them with formally co
Externí odkaz:
http://arxiv.org/abs/2406.03231
Autor:
Stolz, Roland, Krasowski, Hanna, Thumm, Jakob, Eichelbeck, Michael, Gassert, Philipp, Althoff, Matthias
Continuous action spaces in reinforcement learning (RL) are commonly defined as interval sets. While intervals usually reflect the action boundaries for tasks well, they can be challenging for learning because the typically large global action space
Externí odkaz:
http://arxiv.org/abs/2406.03704
Graph neural networks are becoming increasingly popular in the field of machine learning due to their unique ability to process data structured in graphs. They have also been applied in safety-critical environments where perturbations inherently occu
Externí odkaz:
http://arxiv.org/abs/2404.15065
Publikováno v:
21st IEEE International Conference on Machine Learning and Applications (ICMLA), 2022, pp. 597-602
Future power systems will rely heavily on micro grids with a high share of decentralised renewable energy sources and energy storage systems. The high complexity and uncertainty in this context might make conventional power dispatch strategies infeas
Externí odkaz:
http://arxiv.org/abs/2205.06212