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This paper introduces a novel probabilistic forecasting technique calledSmoothing Quantile Regression Averaging (SQRA). It combines Quantile RegressionAveraging - a well performing load and price forecasting approach - with kernelestimation to improve the reliability of the estimates. Three variants of SQRAare evaluated across datasets from four power markets and compared againstwell-established benchmarks. Empirical evidence indicates superior predictiveperformance of the method in terms of the Kupiec test, the pinball score, andthe conditional predictive accuracy test. Moreover, considering a day-aheadmarket trading strategy that utilizes probabilistic price predictions andbattery storage, the study shows that profits of up to 9 EUR per 1 MW tradedcan be achieved when forecasts are generated using SQRA.

Smoothing Quantile Regression Averaging: A new approach to probabilistic forecasting of electricity prices