@article{balduin2019,
Author = {Balduin, Stephan and Tröschel, Martin and Lehnhoff, Sebastian},
Title = {Towards domain-specific surrogate models for smart grid co-simulation},

Journal = {Energy Informatics},
Year = {2019},
Pages = {27},
Doi = {10.1186/s42162-019-0082-2},
type = {article},
Abstract = {Surrogate models are used to reduce the computational effort required to simulate complex systems. The power grid can be considered as such a complex system with a large number of interdependent inputs. With artificial neural networks and deep learning, it is possible to build high-dimensional approximation models. However, a large data set is also required for the training process. This paper presents an approach to sample input data and create a deep learning surrogate model for a low voltage grid. Challenges are discussed and the model is evaluated under different conditions. The results show that the model performs well from a machine learning point of view, but has domain-specific weaknesses.}
}
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