Electricity grid with data curve showing forecasting accuracy.

Decoding Electricity Price Forecasting: How Smoothing Quantile Regression is Changing the Game

Jordan Keane in Business & Economy December 2025 • 4 min read.

"Discover how a new approach to electricity price forecasting, Smoothing Quantile Regression Averaging, is enhancing market operations and trading strategies."

In today's dynamic energy landscape, marked by events like the COVID-19 pandemic and geopolitical tensions, the ability to accurately forecast electricity prices is more critical than ever. These fluctuations introduce significant instability, making precise short-term price predictions essential for navigating the complexities of the power market.

Accurate forecasting enables market participants to make informed decisions, optimize bidding strategies, and capitalize on profitable transactions. While traditional methods have largely focused on point forecasts, a new approach is gaining traction: probabilistic forecasting, which offers a comprehensive view of future price distribution.

This article explores Smoothing Quantile Regression (SQR) Averaging, an innovative method designed to enhance probabilistic forecasting schemes. By improving upon existing techniques and providing a more nuanced understanding of market dynamics, SQR Averaging is setting a new standard for accuracy and efficiency in electricity price prediction.

Smoothing Quantile Regression (SQR) Averaging: A New Era in Forecasting

Electricity grid with data curve showing forecasting accuracy.

Smoothing Quantile Regression (SQR) Averaging is introduced as an enhanced method that builds on Quantile Regression (QR) techniques to average point predictions. However, SQR Averaging uses smoothing quantile regression to refine these averages. This approach includes several variants, each designed to optimize forecast accuracy and reliability.

The technique is evaluated using data from major European electricity markets, specifically Germany and Spain, encompassing periods of significant market volatility. The performance of SQR Averaging is assessed using several benchmarks, focusing on statistical measures, reliability, and economic benefits derived from strategic trading simulations.

SQRA: A smoothed version of Quantile Regression Averaging, improving the stability and accuracy of forecasts.
SQRM: A smoothed equivalent of Quantile Regression Machine, which leverages machine learning techniques within the quantile regression framework.
SQRF: A smoothed version of Quantile Regression with probability (F) averaging, enhancing the probabilistic predictions.

These variants are designed to address the limitations of traditional quantile regression methods, particularly in managing the 'noise' inherent in electricity price data. By smoothing the regression process, SQR Averaging aims to provide more stable and reliable predictions, which in turn, can lead to better decision-making in energy trading and risk management.

The Future of Energy Trading with Enhanced Forecasting

The introduction of SQR Averaging marks a significant step forward in the quest for more accurate and reliable electricity price forecasts. By leveraging sophisticated statistical techniques and comprehensive market data, this method offers a promising avenue for enhancing trading strategies, optimizing resource allocation, and managing risk in the increasingly complex energy markets. As the energy sector continues to evolve, innovations like SQR Averaging will play a crucial role in shaping a more efficient, stable, and profitable future.

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This article is based on research published under:

DOI-LINK: https://doi.org/10.48550/arXiv.2302.00411,

Title: Smoothing Quantile Regression Averaging: A New Approach To Probabilistic Forecasting Of Electricity Prices

Subject: stat.ap q-fin.cp

Authors: Bartosz Uniejewski

Published: 01-02-2023

Everything You Need To Know

What is Smoothing Quantile Regression (SQR) Averaging and how does it improve electricity price forecasting?

Smoothing Quantile Regression (SQR) Averaging is a sophisticated method for forecasting electricity prices, designed to improve upon existing Quantile Regression (QR) techniques. Unlike traditional point forecasts, SQR Averaging focuses on probabilistic forecasting, providing a comprehensive view of future price distribution. This is achieved by smoothing the regression process, which helps manage the inherent 'noise' in electricity price data. There are several variants, including SQRA, SQRM, and SQRF, each designed to optimize forecast accuracy and reliability in volatile energy markets, enhancing market operations and trading strategies.

What are the key variants of Smoothing Quantile Regression (SQR) Averaging, and how do they differ?

The key variants of Smoothing Quantile Regression (SQR) Averaging include SQRA, SQRM, and SQRF. SQRA is a smoothed version of Quantile Regression Averaging, improving forecast stability and accuracy. SQRM, is a smoothed version of Quantile Regression Machine, using machine learning within the quantile regression framework. SQRF is a smoothed version of Quantile Regression with probability (F) averaging, enhancing probabilistic predictions. Each variant addresses the limitations of traditional quantile regression methods, particularly in handling the volatility in electricity price data, to offer more reliable predictions.

How does probabilistic forecasting, as facilitated by Smoothing Quantile Regression (SQR) Averaging, benefit energy traders?

Probabilistic forecasting, enhanced by Smoothing Quantile Regression (SQR) Averaging, provides energy traders with a more comprehensive view of future price distribution. This allows for more informed decision-making, as traders can assess the probability of various price outcomes rather than relying solely on a single point forecast. This enables market participants to make informed decisions, optimize bidding strategies, and capitalize on profitable transactions. By understanding the range of possible prices, traders can better manage risk and develop more effective trading strategies, particularly in volatile markets.

What role did events like the COVID-19 pandemic and geopolitical tensions play in highlighting the need for better electricity price forecasting?

Events like the COVID-19 pandemic and escalating geopolitical tensions significantly increased the volatility in energy markets. These events caused unpredictable shifts in supply and demand, leading to dramatic price fluctuations. This instability made it crucial to have accurate short-term price predictions to navigate the complexities of the power market. These circumstances emphasized the need for advanced forecasting techniques like Smoothing Quantile Regression (SQR) Averaging, which can handle the increased 'noise' and provide more reliable price forecasts, which are essential for managing risk and optimizing trading strategies during turbulent periods.

In which markets has Smoothing Quantile Regression (SQR) Averaging been evaluated, and what metrics were used to assess its performance?

Smoothing Quantile Regression (SQR) Averaging has been evaluated using data from major European electricity markets, specifically Germany and Spain, to assess its performance under real-world conditions. The performance of SQR Averaging was assessed using several benchmarks. These benchmarks focused on statistical measures to assess the accuracy of the forecasts, reliability to evaluate the consistency of the predictions, and economic benefits derived from strategic trading simulations to determine the practical value of the forecasts in trading scenarios. These evaluations help demonstrate the effectiveness and applicability of SQR Averaging in enhancing trading strategies and risk management within dynamic energy markets.