This paper presents a novel hybrid approach for constricting probabilistic forecasts that combines both the Quantile Regression Averaging (QRA) method and the factor-based averaging scheme. The performance of the approach is evaluated on data sets from two European energy markets - the German EPEX SPOT and the Polish Power Exchange (TGE). The results show that the newly proposed method outperforms literature benchmarks in terms of statistical measures: the empirical coverage and the Christoffersen test for conditional coverage. Moreover, in line with recent literature trends, the economic value of forecasts is evaluated based on the trading strategy using probabilistic price predictions to optimize the operation of an energy storage system. The results suggest that apart from the use of statistical measures, there is a need for the economic evaluation of forecasts.

翻译：本文提出了一种新颖的混合方法，用于构建概率预测，该方法结合了分位数回归平均（QRA）方法与基于因子的平均方案。该方法的性能在两个欧洲能源市场数据集上进行了评估——德国EPEX SPOT市场和波兰电力交易所（TGE）。结果表明，新提出的方法在统计度量方面优于文献中的基准模型：经验覆盖率和条件覆盖率的Christoffersen检验。此外，遵循近期文献趋势，基于交易策略评估了预测的经济价值，该策略利用概率价格预测来优化储能系统的运行。结果表明，除了使用统计度量外，对预测进行经济评估也是必要的。