This paper introduces a novel probabilistic forecasting technique called Smoothing Quantile Regression Averaging (SQRA). It combines Quantile Regression Averaging - a well performing load and price forecasting approach - with kernel estimation to improve the reliability of the estimates. Three variants of SQRA are evaluated across datasets from four power markets and compared against well-established benchmarks. Empirical evidence indicates superior predictive performance of the method in terms of the Kupiec test, the pinball score, and the conditional predictive accuracy test. Moreover, considering a day-ahead market trading strategy that utilizes probabilistic price predictions and battery storage, the study shows that profits of up to 9 EUR per 1 MW traded can be achieved when forecasts are generated using SQRA.

翻译：本文提出了一种新颖的概率预测技术——平滑分位数回归平均（Smoothing Quantile Regression Averaging, SQRA）。该方法将分位数回归平均（一种性能优异的负荷与电价预测方法）与核估计相结合，以提升估计的可靠性。我们基于四个电力市场的数据集对SQRA的三种变体进行了评估，并与成熟的基准方法进行了对比。经验证据表明，该方法在Kupiec检验、弹球得分以及条件预测精度检验方面均展现出优越的预测性能。此外，通过考虑利用概率价格预测和电池储能的日前市场交易策略，研究表明，使用SQRA生成的预测结果可实现每1兆瓦交易量高达9欧元的收益。