Thermal power flow (TPF) is an important task for various control purposes in 4 Th generation district heating grids with multiple decentral heat sources and meshed grid structures. Computing the TPF, i.e., determining the grid state consisting of temperatures, pressures, and mass flows for given supply and demand values, is classically done by solving the nonlinear heat grid equations, but can be sped up by orders of magnitude using learned models such as neural networks. We propose a novel, efficient scheme to generate a sufficiently large training data set covering relevant supply and demand values. Instead of sampling supply and demand values, our approach generates training examples from a proxy distribution over generator and consumer mass flows, omitting the iterations needed for solving the heat grid equations. The exact, but slightly different, training examples can be weighted to represent the original training distribution. We show with simulations for typical grid structures that the new approach can reduce training set generation times by two orders of magnitude compared to sampling supply and demand values directly, without loss of relevance for the training samples. Moreover, learning TPF with a training data set is shown to outperform sample-free, physics-aware training approaches significantly.

通过生成一个足够大的训练数据集，我们提出了一种新颖且高效的方案，该数据集覆盖了相关的供应和需求值。我们的方法通过生成来自于发电机和消费者质量流的代理分布的训练样本，避免了解热网方程所需的迭代过程，并可以对准确性稍有不同的训练样本进行加权以代表原始训练分布。通过对典型网格结构的模拟，我们展示了这种新方法相比直接抽样供需值可以将训练集生成时间减少两个数量级，同时不会对训练样本的相关性产生损失。此外，使用训练数据集学习热功率流明显优于无样本、具有物理意识的训练方法。

第四代热力网基于学习的热力流高效训练