论文标题
概率的多元电力价格预测，使用隐式生成合奏后处理
Probabilistic multivariate electricity price forecasting using implicit generative ensemble post-processing
论文作者
论文摘要
对预测不确定性的可靠估计对于风险敏感的最佳决策至关重要。在本文中，我们提出了隐性生成集合后处理，这是多元概率电价预测的新型框架。我们使用基于点预测模型集合的无可能的隐式生成模型来生成具有连贯的依赖关系结构的多元电力价格场景，作为关节预测分布的表示。我们的合奏后处理方法优于建立的模型组合。这是在德国日间市场的数据集中证明的。由于我们的方法在特定领域的专家模型的集合之上起作用，因此可以轻松地将其部署到其他预测任务中。
The reliable estimation of forecast uncertainties is crucial for risk-sensitive optimal decision making. In this paper, we propose implicit generative ensemble post-processing, a novel framework for multivariate probabilistic electricity price forecasting. We use a likelihood-free implicit generative model based on an ensemble of point forecasting models to generate multivariate electricity price scenarios with a coherent dependency structure as a representation of the joint predictive distribution. Our ensemble post-processing method outperforms well-established model combination benchmarks. This is demonstrated on a data set from the German day-ahead market. As our method works on top of an ensemble of domain-specific expert models, it can readily be deployed to other forecasting tasks.
