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

TL;DR

This paper introduces Smoothing Quantile Regression Averaging, a novel probabilistic forecasting method for electricity prices that enhances trading strategies and profitability amid market disruptions like COVID-19 and geopolitical events.

Contribution

The paper presents Smoothing Quantile Regression Averaging, a new approach that improves probabilistic electricity price forecasts and trading outcomes compared to existing methods.

Findings

SQR Averaging improves forecast reliability and sharpness.

It increases trading profits by up to 3.5% over benchmark strategies.

The method remains effective during market disruptions like COVID-19 and geopolitical crises.

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 of up to 3.5\% on average 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.