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.

翻译：我们提出了一个利用分布式神经网络进行概率电价预测的新办法。模型结构基于一个包含所谓概率层的深层神经网络。网络的输出是具有2(正常)或4(Johnson's SU)参数的参数分布。在一项涉及德国市场日头电价的预测研究中,我们的方法大大优于最新基准,包括LASSO估计的回归和与量反反差动相结合的深神经网络。获得的结果不仅强调了建模波动电价时较高时刻的重要性,而且 -- -- 鉴于概率预测是风险管理的精髓 -- -- 也为管理电力部门的组合提供了重要影响。