Probabilistic multivariate electricity price forecasting using implicit generative ensemble post-processing

• URL: http://arxiv.org/abs/2005.13417v1
• Date: Wed, 27 May 2020 15:22:10 GMT
• Title: Probabilistic multivariate electricity price forecasting using implicit generative ensemble post-processing
• Authors: Tim Janke and Florian Steinke
• Abstract summary: We use a likelihood-free implicit generative model based on an ensemble of point forecasting models to generate multivariate electricity price scenarios. Our ensemble post-processing method outperforms well-established model combination benchmarks. As our method works on top of an ensemble of domain-specific expert models, it can readily be deployed to other forecasting tasks.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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