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Found 5 papers, 4 papers with code
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.
