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| Hauptseite > Publikationsdatenbank > A Benchmark Arena for Real-Time Energy Forecasting |
| Abstract | FZJ-2026-03200 |
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2026
Abstract: Forecasting electricity prices, load, and renewable generation has become essential for powersystem operators, energy traders, and analysts as renewable penetration and market volatility riseacross Europe, a trend reflected in the sharp growth of related research, including more than 4,000papers published in 2024 alone1. Despite a vast body of literature and notable open-access effortssuch as Lago et al.2 (2021), the field lacks a widely accepted, continuously updated real-timebenchmark that captures the evolving conditions of modern power markets. As a result, it remainsdifficult to reliably assess state-of-the-art forecasting performance, hindering consistent andmeasurable progress, both in research and in commercial practice.Existing studies rely on static historical benchmark datasets2–4, which are useful for comparingrelative model performance. However, in a rapidly changing energy system, their results quicklybecome outdated and offer limited insight into real-time forecasting performance under currentsystem conditions. Furthermore, static benchmarks allow to rely on exogenous regressors that werenot available at the time the forecast would have been issued, thereby overlooking key real-timeforecasting constraintsi. At the same time, commercial forecasting solutions remain predominantlyclosed-source, preventing transparent quality assessment and inhibiting trust in their performanceclaims (see, e.g., Semmelmann7 (2025) or Stupperich8 (2025)).This paper introduces a benchmark arena for real-time energy forecasting. The platform providesthe first open, API-driven environment where researchers and practitioners can submit day-aheadforecasts for electricity prices, load, and renewable generation. All submissions are evaluated onceactual values are published, ensuring assessment under true real-time conditions. Standardizedmetrics are computed automatically, and a public leaderboard ranks models based on rolling-horizon performance (e.g., the best results over the last 30, 90, or 365 days). This enablessystematic comparison of forecasting solutions, quantifying real performance differences andproviding an evidence-based view of methodological progress. By continuously reflectingcontemporary market dynamics, the benchmark arena establishes a transparent, reproducible, anddynamically evolving reference for forecasting quality.
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