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Found 8 papers, 4 papers with code
Efficient Training of Learning-Based Thermal Power Flow for 4th Generation District Heating Grids
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.
Deep Learning-enabled MCMC for Probabilistic State Estimation in District Heating Grids
Flexible district heating grids form an important part of future, low-carbon energy systems.
Generative machine learning methods for multivariate ensemble post-processing
Ensemble weather forecasts based on multiple runs of numerical weather prediction models typically show systematic errors and require post-processing to obtain reliable forecasts.
Bidding and Scheduling in Energy Markets: Which Probabilistic Forecast Do We Need?
For bidding curve optimization, pairwise or full joint distributions are necessary except for specific cases.
Implicit Generative Copulas
Copulas are a powerful tool for modeling multivariate distributions as they allow to separately estimate the univariate marginal distributions and the joint dependency structure.
Probabilistic multivariate electricity price forecasting using implicit generative ensemble post-processing
As our method works on top of an ensemble of domain-specific expert models, it can readily be deployed to other forecasting tasks.
Semi-supervised Regression using Hessian energy with an application to semi-supervised dimensionality reduction
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.
Non-parametric Regression Between Manifolds
This paper discusses non-parametric regression between Riemannian manifolds.
