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

Por • 2 jun, 2020 • Sección: Ciencia y tecnología

Tim Janke, Florian Steinke

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:2005.13417v1 [stat.AP

Applications (stat.AP); Econometrics (econ.EM); Risk Management (q-fin.RM); Statistical Finance (q-fin.ST); Machine Learning (stat.ML)

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