Modeling price risks is crucial for economic decision making in energy markets. Besides the risk associated with a single price, the dependence structure of multiple prices is often relevant. We therefore propose a generic and easy-to-implement method for generating multivariate probabilistic forecasts based on univariate point forecasts of day-ahead electricity prices. While each univariate point forecast refers to one of the day’s 24 hours, the multivariate forecast distribution models dependencies across hours. The proposed method is based on simple copula techniques and an optional time series component. We illustrate the method for five benchmark data sets. Furthermore, we demonstrate an example for constructing realistic prediction intervals for the weighted sum of consecutive electricity prices as needed for pricing individual load profiles.

对价格风险进行建模对于能源市场的经济决策至关重要。除了与单一价格相关的风险外，多个价格的依赖结构通常也是相关的。因此，我们提出了一种通用且易于实施的方法，用于根据日前电价的单变量点预测生成多变量概率预测。虽然每个单变量点预测是指一天中的 24 小时之一，但多变量预测分布模型依赖于跨小时。所提出的方法基于简单的 copula 技术和可选的时间序列组件。我们说明了五个基准数据集的方法。此外，我们还展示了一个示例，用于根据为单个负载配置文件定价所需的连续电价加权和构建实际预测区间。