Probabilistic multivariate electricity price forecasting using implicit generative ensemble post-processing
This work addresses uncertainty estimation for risk-sensitive decision-making in electricity markets, representing an incremental improvement over existing ensemble methods.
The paper tackled the problem of multivariate probabilistic electricity price forecasting by proposing an implicit generative ensemble post-processing framework, which outperformed established benchmarks on German day-ahead market data.
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.