@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.}}@COMMENT{Bibtex file generated on }