AI brain forecasting energy prices.

Decoding the Energy Market: Can AI Predict Tomorrow's Electricity Prices?

Samir D’Costa in Tech & Innovation August 2025 • 4 min read.

"A new forecasting model uses machine learning to anticipate energy costs, promising smarter trading and a greener grid."

The energy market is a complex beast, influenced by everything from weather patterns to geopolitical events. Predicting electricity prices, even a day ahead, is a high-stakes game for energy producers, consumers, and traders. Accurate forecasts can lead to better resource allocation, reduced costs, and a more stable energy grid. However, the traditional methods often fall short, struggling to capture the volatile nature of this critical market.

Enter the world of artificial intelligence. Researchers are increasingly turning to machine learning models to tackle the challenges of energy forecasting. These models can analyze vast amounts of data, identify subtle patterns, and make predictions with unprecedented accuracy. One such model, a hybrid 'Factor-QRA' approach, is making waves in the field.

This new model combines quantile regression averaging (QRA) with a factor-based averaging scheme, creating a powerful tool for probabilistic forecasting. In essence, it not only predicts what the price of electricity will be, but also provides a range of possible outcomes and their probabilities. This level of detail is invaluable for making informed decisions in the energy market.

What is Factor-QRA and Why is it a Game Changer?

The Factor-QRA model is a sophisticated forecasting technique that leverages the strengths of two distinct approaches. Quantile Regression Averaging (QRA) is a statistical method that predicts a range of possible values, not just a single point estimate. This is particularly useful in volatile markets like energy, where prices can fluctuate dramatically.

The 'factor-based averaging scheme' component addresses the challenge of using multiple forecasts, each with its own strengths and weaknesses. Instead of relying on a single forecast, this scheme combines numerous predictions by identifying underlying factors that drive their accuracy. This creates a more robust and reliable overall forecast.

Improved Accuracy: Outperforms traditional forecasting methods in statistical benchmarks.
Economic Value: Enhances trading strategies and optimizes energy storage system operations.
Probabilistic Insights: Provides a range of potential outcomes, enabling better risk management.
Adaptability: Evaluated across diverse European energy markets and fluctuating conditions.

By integrating these two approaches, the Factor-QRA model offers a comprehensive and nuanced understanding of the electricity market. It moves beyond simple price predictions, providing stakeholders with the tools they need to navigate uncertainty and make strategic decisions.

The Future of Energy Forecasting

The Factor-QRA model represents a significant step forward in energy forecasting. By combining statistical rigor with the power of machine learning, it offers a more accurate, reliable, and nuanced understanding of the electricity market. As AI continues to evolve, we can expect even more sophisticated forecasting tools to emerge, further optimizing energy use, reducing costs, and paving the way for a more sustainable energy future.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: 10.1016/j.epsr.2024.110541,

Title: Probabilistic Forecasting With A Hybrid Factor-Qra Approach: Application To Electricity Trading

Subject: stat.ap q-fin.rm q-fin.st

Authors: Katarzyna Maciejowska, Tomasz Serafin, Bartosz Uniejewski

Published: 15-03-2023

Everything You Need To Know

What is Factor-QRA and how does it improve electricity price forecasting?

Factor-QRA is a hybrid forecasting model that enhances the prediction of electricity prices by combining two key elements: Quantile Regression Averaging (QRA) and a factor-based averaging scheme. QRA provides a range of possible price outcomes and their probabilities, which is crucial for managing risk in the volatile energy market. The factor-based averaging scheme improves accuracy by combining multiple predictions and identifying underlying factors that drive their accuracy. This integrated approach offers a more accurate, reliable, and nuanced understanding of the electricity market than traditional forecasting methods.

How does the Factor-QRA model benefit energy traders and consumers?

The Factor-QRA model offers several advantages for both energy traders and consumers. For traders, it enhances trading strategies by providing more accurate price forecasts, allowing them to make better decisions about buying and selling electricity. This can lead to increased profits and more efficient resource allocation. For consumers, the model contributes to cost savings and a more stable energy grid. By optimizing energy use and storage, it helps reduce overall energy costs. The model's probabilistic insights also enable better risk management for both groups by offering a range of potential price outcomes.

What is Quantile Regression Averaging (QRA) and why is it important in energy forecasting?

Quantile Regression Averaging (QRA) is a statistical method used within the Factor-QRA model to predict a range of possible electricity prices, rather than a single point estimate. This is particularly important in the energy market because electricity prices are highly volatile and can fluctuate significantly. By providing a range of potential outcomes and their probabilities, QRA allows stakeholders to better manage risk and make more informed decisions. This nuanced approach moves beyond simple predictions, offering a more comprehensive understanding of market dynamics.

How does the factor-based averaging scheme work in the Factor-QRA model?

The factor-based averaging scheme in the Factor-QRA model addresses the challenge of combining multiple forecasts. Instead of relying on a single prediction, this scheme analyzes and combines numerous forecasts by identifying underlying factors that contribute to their accuracy. This allows the model to leverage the strengths of different forecasting methods, creating a more robust and reliable overall forecast. It essentially creates a meta-forecast by intelligently weighting and combining individual predictions based on these factors.

What are the key advantages of the Factor-QRA model compared to traditional forecasting methods?

The Factor-QRA model provides several key advantages over traditional forecasting methods. It offers improved accuracy, outperforming traditional methods in statistical benchmarks. It also provides economic value by enhancing trading strategies and optimizing energy storage system operations. Another critical advantage is its ability to provide probabilistic insights, offering a range of potential outcomes, which enables better risk management. Finally, the model is adaptable, having been evaluated across diverse European energy markets and under fluctuating conditions, demonstrating its broad applicability and robustness.