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Learning Probability Distributions of Day-Ahead Electricity Prices

Jozef Baruník and Lubos Hanus

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Abstract: We propose a novel machine learning approach to probabilistic forecasting of hourly day-ahead electricity prices. In contrast to recent advances in data-rich probabilistic forecasting that approximate the distributions with some features such as moments, our method is non-parametric and selects the best distribution from all possible empirical distributions learned from the data. The model we propose is a multiple output neural network with a monotonicity adjusting penalty. Such a distributional neural network can learn complex patterns in electricity prices from data-rich environments and it outperforms state-of-the-art benchmarks.

Date: 2023-10, Revised 2023-10
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ecm, nep-ene and nep-for
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