Probabilistic multivariate electricity price forecasting using implicit generative ensemble post-processing
Abstract
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.
Cite
@article{arxiv.2005.13417,
title = {Probabilistic multivariate electricity price forecasting using implicit generative ensemble post-processing},
author = {Tim Janke and Florian Steinke},
journal= {arXiv preprint arXiv:2005.13417},
year = {2020}
}
Comments
To be presented at the 16th International Conference on Probabilistic Methods Applied to Power Systems 2020 (PMAPS 2020)