This paper presents a novel approach for constructing probabilistic forecasts, which combines both the Quantile Regression Averaging (QRA) method and the Principal Component Analysis (PCA) averaging scheme. The performance of the approach is evaluated on datasets from two European energy markets - the German EPEX SPOT and the Polish Power Exchange (TGE). The results indicate that newly proposed solutions yield results, which are more accurate than the literature benchmarks. Additionally, empirical evidence indicates that the proposed method outperforms its competitors in terms of the empirical coverage and the Christoffersen test. In addition, the economic value of the probabilistic forecast is evaluated on the basis of financial metrics. We test the performance of forecasting models taking into account a day-ahead market trading strategy that utilizes probabilistic price predictions and an energy storage system. The results indicate that profits of up to 10 EUR per 1 MWh transaction can be obtained when predictions are generated using the novel approach.

翻译：本文提出了一种构建概率预测的新方法，该方法结合了分位数回归平均（QRA）方法与主成分分析（PCA）平均方案。该方法在两个欧洲能源市场——德国EPEX SPOT交易所和波兰电力交易所（TGE）——的数据集上进行了性能评估。结果表明，新提出的方案比文献中的基准模型更为准确。此外，实证证据显示，该提出方法在经验覆盖率和Christoffersen检验方面均优于竞争对手。同时，基于财务指标评估了概率预测的经济价值。我们考虑了利用概率价格预测和储能系统的日前市场交易策略，评估了预测模型的性能。结果表明，使用这种新方法生成预测时，每1兆瓦时交易量可获得高达10欧元的利润。