Leveraging Asynchronous Cross-border Market Data for Improved Day-Ahead Electricity Price Forecasting in European Markets
Maria Margarida Mascarenhas, Jilles De Blauwe, Mikael Amelin, Hussain Kazmi

TL;DR
This study demonstrates that incorporating asynchronously published cross-border market data significantly enhances day-ahead electricity price forecasts in European markets, with implications for market participants' bidding strategies.
Contribution
It introduces a novel approach of using asynchronously published prices from interconnected markets to improve forecast accuracy in European electricity markets.
Findings
Forecast accuracy improved by 22% in Belgium and 9% in Sweden when using cross-border data.
Frequent model recalibration is necessary but increases computational costs.
More data from additional markets does not always improve forecast performance.
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…
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Taxonomy
TopicsElectric Power System Optimization · Energy Load and Power Forecasting
