  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 novel framework for multivariate
probabilistic electricity price forecasting. We use a likelihood-free implicit
generative model based on an ensemble of point forecasting models to generate
multivariate electricity price scenarios with a coherent dependency structure
as a representation of the joint predictive distribution. Our ensemble
post-processing method outperforms well-established model combination
benchmarks. This is demonstrated on a data set from the German day-ahead
market. As our method works on top of an ensemble of domain-specific expert
models, it can readily be deployed to other forecasting tasks.
