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.

中文翻译：

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面向特定领域的替代模型以进行智能电网协同仿真

代理模型用于减少模拟复杂系统所需的计算量。可以将电网视为具有大量相互依赖的输入的复杂系统。借助人工神经网络和深度学习，可以构建高维近似模型。但是，培训过程也需要大数据集。本文提出了一种对输入数据进行采样并为低压电网创建深度学习替代模型的方法。讨论了挑战并在不同条件下评估了模型。结果表明，从机器学习的角度来看，该模型表现良好，但存在特定领域的弱点。