no code implementations • 18 Mar 2024 • Andreas Bott, Mario Beykirch, Florian Steinke
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.
1 code implementation • 24 May 2023 • Andreas Bott, Tim Janke, Florian Steinke
Flexible district heating grids form an important part of future, low-carbon energy systems.
1 code implementation • 26 Sep 2022 • Jieyu Chen, Tim Janke, Florian Steinke, Sebastian Lerch
Ensemble weather forecasts based on multiple runs of numerical weather prediction models typically show systematic errors and require post-processing to obtain reliable forecasts.
no code implementations • 24 Mar 2022 • Mario Beykirch, Tim Janke, Florian Steinke
For bidding curve optimization, pairwise or full joint distributions are necessary except for specific cases.
1 code implementation • NeurIPS 2021 • Tim Janke, Mohamed Ghanmi, Florian Steinke
Copulas are a powerful tool for modeling multivariate distributions as they allow to separately estimate the univariate marginal distributions and the joint dependency structure.
1 code implementation • 27 May 2020 • Tim Janke, Florian Steinke
As our method works on top of an ensemble of domain-specific expert models, it can readily be deployed to other forecasting tasks.
no code implementations • NeurIPS 2009 • Kwang I. Kim, Florian Steinke, Matthias Hein
Semi-supervised regression based on the graph Laplacian suffers from the fact that the solution is biased towards a constant and the lack of extrapolating power.
no code implementations • NeurIPS 2008 • Florian Steinke, Matthias Hein
This paper discusses non-parametric regression between Riemannian manifolds.