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.

arXiv commentTo be presented at the 16th International Conference on Probabilistic Methods Applied to Power Systems 2020 (PMAPS 2020)

References in corpus (2)

• Equivalence of distance-based and RKHS-based statistics in hypothesis testing
• A New Framework for the Assessment and Calibration of Medium Range Ensemble Temperature Forecasts

Cited by in corpus (2)

• Generative machine learning methods for multivariate ensemble post-processing
• Forecasting Electricity Prices