Distributional neural networks for electricity price forecasting

Grzegorz Marcjasz, Micha{\l} Narajewski, Rafał Weron and Florian Ziel

Papers from arXiv.org

Abstract: We present a novel approach to probabilistic electricity price forecasting which utilizes distributional neural networks. The model structure is based on a deep neural network that contains a so-called probability layer. The network's output is a parametric distribution with 2 (normal) or 4 (Johnson's SU) parameters. In a forecasting study involving day-ahead electricity prices in the German market, our approach significantly outperforms state-of-the-art benchmarks, including LASSO-estimated regressions and deep neural networks combined with Quantile Regression Averaging. The obtained results not only emphasize the importance of higher moments when modeling volatile electricity prices, but also -- given that probabilistic forecasting is the essence of risk management -- provide important implications for managing portfolios in the power sector.

Date: 2022-07, Revised 2022-12
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ene and nep-for
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Citations: View citations in EconPapers (4)

Published in Enrgy Economics, 125 (2023) 106843

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Journal Article: Distributional neural networks for electricity price forecasting (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2207.02832

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