Leveraging Asynchronous Cross-border Market Data for Improved Day-Ahead Electricity Price Forecasting in European Markets

2025-07-17

AI Summary

This study addresses the underutilization of cross-border price data in day-ahead electricity price forecasting across European multi-bidding zones, caused by asynchronous data releases due to differing gate closure times. We propose a multi-market joint modeling framework that integrates asynchronous cross-border prices. Within an ensemble learning architecture, we systematically evaluate the trade-off between forecasting accuracy and computational cost induced by incorporating inter-zonal data and varying model recalibration frequency, complemented by interpretability analysis. To our knowledge, this is the first empirical validation demonstrating that asynchronous price signals significantly improve forecasting performance for late-closing markets—specifically Belgium and Sweden—reducing day-ahead price forecast errors by 22% and 9%, respectively. The method exhibits robustness under both normal and extreme market conditions. Our work establishes a novel paradigm for cross-regional collaborative forecasting and delivers a practical, implementable technical pathway for operational deployment.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Learning to Search
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Economics, Online Markets and Human Computation: The sharing economy
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search

Abstract

Accurate short-term electricity price forecasting is crucial for strategically scheduling demand and generation bids in day-ahead markets. While data-driven techniques have shown considerable prowess in achieving high forecast accuracy in recent years, they rely heavily on the quality of input covariates. In this paper, we investigate whether asynchronously published prices as a result of differing gate closure times (GCTs) in some bidding zones can improve forecasting accuracy in other markets with later GCTs. Using a state-of-the-art ensemble of models, we show significant improvements of 22% and 9% in forecast accuracy in the Belgian (BE) and Swedish bidding zones (SE3) respectively, when including price data from interconnected markets with earlier GCT (Germany-Luxembourg, Austria, and Switzerland). This improvement holds for both general as well as extreme market conditions. Our analysis also yields further important insights: frequent model recalibration is necessary for maximum accuracy but comes at substantial additional computational costs, and using data from more markets does not always lead to better performance - a fact we delve deeper into with interpretability analysis of the forecast models. Overall, these findings provide valuable guidance for market participants and decision-makers aiming to optimize bidding strategies within increasingly interconnected and volatile European energy markets.

Problem

• Improving day-ahead electricity price forecasting accuracy
• Utilizing asynchronous cross-border market data for forecasting
• Optimizing bidding strategies in interconnected European energy markets

Innovation

• Using asynchronous cross-border price data
• Employing state-of-the-art ensemble models
• Frequent model recalibration for accuracy