April 4, 2024, 4:41 a.m. | Alessandro Brusaferri, Andrea Ballarino, Luigi Grossi, Fabrizio Laurini

cs.LG updates on arXiv.org arxiv.org

arXiv:2404.02722v1 Announce Type: new
Abstract: Probabilistic electricity price forecasting (PEPF) is subject of increasing interest, following the demand for proper quantification of prediction uncertainty, to support the operation in complex power markets with increasing share of renewable generation. Distributional neural networks ensembles have been recently shown to outperform state of the art PEPF benchmarks. Still, they require critical reliability enhancements, as fail to pass the coverage tests at various steps on the prediction horizon. In this work, we propose a …

abstract arxiv cs.lg demand electricity forecasting line markets networks neural networks power prediction price quantification renewable support type uncertainty

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