26 citations · 55 across the 6 of their papers we have counts for
6 papers
Deep Learning-enabled MCMC for Probabilistic State Estimation in District Heating Grids
Andreas Bott, Tim Janke, Florian Steinke
Flexible district heating grids form an important part of future, low-carbon energy systems. We examine probabilistic state estimation in such grids, i.e., we aim to estimate the p…
Generative machine learning methods for multivariate ensemble post-processing
Jieyu Chen, Tim Janke, Florian Steinke +1
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?
Mario Beykirch, Tim Janke, Florian Steinke
Probabilistic forecasting in combination with stochastic programming is a key tool for handling the growing uncertainties in future energy systems. Derived from a general stochasti…
Implicit Generative Copulas
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. H…
Probabilistic Forecast Combination for Anomaly Detection in Building Heat Load Time Series
Mario Beykirch, Tim Janke, Imed Tayeche +1
We consider the problem of automated anomaly detection for building level heat load time series. An anomaly detection model must be applicable to a diverse group of buildings and p…
Probabilistic multivariate electricity price forecasting using implicit generative ensemble post-processing
Tim Janke, Florian Steinke
The reliable estimation of forecast uncertainties is crucial for risk-sensitive optimal decision making. In this paper, we propose implicit generative ensemble post-processing, a n…