标题： 日前电力市场中的概率预测：模拟高峰和低谷电价 显示英文标题

标题： Probabilistic Forecasting in Day-Ahead Electricity Markets: Simulating Peak and Off-Peak Prices

摘要： 在本文中，我们包括了电力价格预测和预测评估的依赖结构。 我们使用德国-奥地利日前价格的非高峰和高峰时间序列，因此我们分析了双变量数据。 我们首先估计两个时间序列的均值，然后在第二步中估计残差。 均值方程通过普通最小二乘法和弹性网络进行估计，而残差则通过最大似然进行估计。 我们的贡献是在残差中包含一个双变量跳跃成分的均值回归跳跃扩散模型。 模型的预测结果使用四种不同的标准进行评估，包括能量得分，以衡量时间序列之间的相关结构是否被正确包含。 在结果中观察到，具有双变量跳跃的模型在能量得分上提供了更好的结果，这意味着为了正确预测相关时间序列，考虑这种结构是重要的。 显示英文摘要

摘要： In this paper we include dependency structures for electricity price forecasting and forecasting evaluation. We work with off-peak and peak time series from the German-Austrian day-ahead price, hence we analyze bivariate data. We first estimate the mean of the two time series, and then in a second step we estimate the residuals. The mean equation is estimated by OLS and elastic net and the residuals are estimated by maximum likelihood. Our contribution is to include a bivariate jump component on a mean reverting jump diffusion model in the residuals. The models' forecasts are evaluated using four different criteria, including the energy score to measure whether the correlation structure between the time series is properly included or not. In the results it is observed that the models with bivariate jumps provide better results with the energy score, which means that it is important to consider this structure in order to properly forecast correlated time series.