Leveraging asynchronous cross-border market data for improved day-ahead electricity price forecasting in European markets

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. By leveraging price data from interconnected markets with earlier GCT (Germany-Luxembourg, Austria, and Switzerland), we reach impressive forecast accuracies, achieving Mean Absolute Errors (MAE) of 10.18 €/MWh and 11.79 €/MWh and relative MAEs of 0.36 and 0.42 for the Belgian (BE) and Swedish (SE3) bidding zones. This equals a 22 % and 9 % improvement when compared to state-of-the-art models not leveraging exogenous price data, and these improvements remain consistent across both the 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. • Effect of cross-border asynchronous market price publication is investigated. • Day-ahead electricity price forecasts are improved by 22 % in BE and 9 % in SE3. • Improvements are observed in both normal and extreme market conditions. • Frequent recalibration leads to optimal results but increases computational requirements. • Model interpretability methods reveal important insights and potential risks.