Efficient Training of Learning-Based Thermal Power Flow for 4th Generation District Heating Grids

• URL: http://arxiv.org/abs/2403.11877v1
• Date: Mon, 18 Mar 2024 15:31:09 GMT
• Title: Efficient Training of Learning-Based Thermal Power Flow for 4th Generation District Heating Grids
• Authors: Andreas Bott, Mario Beykirch, Florian Steinke,
• Abstract summary: 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. We show with simulations for typical grid structures that the new approach can reduce training set generation times by two orders of magnitude.
• Score: 1.0923877073891446
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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