Efficient Training of Learning-Based Thermal Power Flow for 4th
  Generation District Heating Grids
Andreas Bott Mario Beykirch Florian Steinke
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.