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:本文介绍了一种构建概率预测的新办法,这种方法结合了量回归率法和主要成分分析平均办法,在两个欧洲能源市场德国EPEXSPOT和波兰电力交易所(TGE)的数据集上评价了这种方法的绩效。结果显示,新提出的解决办法产生的结果比文献基准更准确。此外,实证证据表明,拟议的方法在实证覆盖面和克赖斯登测试方面优于竞争对手。此外,还根据金融指标对概率预测的经济价值进行评估。我们测试预测模型的绩效时标市场交易战略,利用概率价格预测和能源储存系统。结果显示,在使用新办法作出预测时,可获得高达每1兆瓦交易10欧元的利润。</s>