论文标题
电力价格预测的分销神经网络
Distributional neural networks for electricity price forecasting
论文作者
论文摘要
我们提出了一种利用分布神经网络的概率电价预测的新型方法。模型结构基于一个包含所谓概率层的深神经网络。网络的输出是一个参数分布，具有2个（正常）或4（约翰逊的SU）参数。在一项涉及德国市场日间电力价格的预测研究中，我们的方法极大地超过了最先进的基准测试，包括宽松的回归和深层神经网络，结合了平均分数回归。获得的结果不仅强调了对挥发性电价进行建模时更高时刻的重要性，而且考虑到概率预测是风险管理的本质，也为管理电力部门的投资组合提供了重要的影响。
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.
