12 citations · 18 across the 10 of their papers we have counts for
4 papers · 1 filter
Physics-Informed Inductive Biases for Voltage Prediction in Distribution Grids
Ehimare Okoyomon, Arbel Yaniv, Christoph Goebel
Voltage prediction in distribution grids is a critical yet difficult task for maintaining power system stability. Machine learning approaches, particularly Graph Neural Networks (G…
Adapting to Change: A Comparison of Continual and Transfer Learning for Modeling Building Thermal Dynamics under Concept Drifts
Fabian Raisch, Max Langtry, Felix Koch +3
Transfer Learning (TL) is currently the most effective approach for modeling building thermal dynamics when only limited data are available. TL uses a pretrained model that is fine…
Price Aware Power Split Control in Heterogeneous Battery Storage Systems
Sheng Yin, Vivek Teja Tanjavooru, Thomas Hamacher +2
This paper presents a unified framework for the optimal scheduling of battery dispatch and internal power allocation in Battery energy storage systems (BESS). This novel approach i…
GenTL: A General Transfer Learning Model for Building Thermal Dynamics
Fabian Raisch, Thomas Krug, Christoph Goebel +1
Transfer Learning (TL) is an emerging field in modeling building thermal dynamics. This method reduces the data required for a data-driven model of a target building by leveraging…