Learning Probability Distributions of Day-Ahead Electricity Prices
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.
- Publication:
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arXiv e-prints
- Pub Date:
- October 2023
- DOI:
- 10.48550/arXiv.2310.02867
- arXiv:
- arXiv:2310.02867
- Bibcode:
- 2023arXiv231002867B
- Keywords:
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- Economics - General Economics