 Smoothing quantile regression averaging: A new approach to probabilistic forecasting of electricity pricesBartosz Uniejewski Journal of Commodity Markets, 2025, vol. 39, issue C Abstract: Accurate short-term price forecasting is essential for daily operations in electricity markets. This article introduces a new method, called Smoothing Quantile Regression (SQR) Averaging, that improves upon well-performing probabilistic forecasting schemes. To demonstrate its utility, a comprehensive study is conducted on two electricity markets, including recent data covering the COVID-19 pandemic and the Russian invasion of Ukraine. The performance of SQR Averaging is evaluated both in terms of reliability and sharpness measures, and economic benefits from a trading strategy. The latter utilizes battery storage and sets limit orders using selected quantiles of the predictive distribution. SQR Averaging leads to profit increases compared to the benchmark strategy based solely on point forecasts. This is strong evidence for the practical value of using probabilistic forecasts in day-ahead power trading, even in the face of the COVID-19 pandemic and geopolitical disruptions. Keywords: Electricity price probabilistic forecasting; Economic evaluation; Trading strategy; Energy storage system; Smoothing quantile regression averaging (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:jocoma:v:39:y:2025:i:c:s2405851325000455 DOI: 10.1016/j.jcomm.2025.100501 Access Statistics for this article Journal of Commodity Markets is currently edited by Marcel Prokopczuk, Betty Simkins and Sjur Westgaard More articles in Journal of Commodity Markets from Elsevier |
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