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[2602.15961] R$^2$Energy: A Large-Scale Benchmark for Robust Renewable Energy Forecasting under Diverse and Extreme Conditions
Summary
The paper presents R$^2$Energy, a benchmark for robust renewable energy forecasting, addressing challenges posed by extreme weather and grid stability.
Why It Matters
As renewable energy sources like wind and solar become more prevalent, accurate forecasting is essential for maintaining grid stability. This benchmark provides a standardized approach to evaluate forecasting models under extreme conditions, highlighting the importance of robustness in energy systems.
Key Takeaways
- R$^2$Energy includes over 10.7 million records from 902 renewable energy stations.
- The benchmark enables fair comparison of forecasting models using standardized NWP signals.
- A critical 'robustness gap' is identified, emphasizing the need for models to integrate meteorological data effectively.
- The study reveals that under extreme conditions, model reliability is more influenced by data integration than architectural complexity.
- R$^2$Energy serves as a foundational tool for developing forecasting models in safety-critical applications.
Computer Science > Machine Learning arXiv:2602.15961 (cs) [Submitted on 17 Feb 2026] Title:R$^2$Energy: A Large-Scale Benchmark for Robust Renewable Energy Forecasting under Diverse and Extreme Conditions Authors:Zhi Sheng, Yuan Yuan, Guozhen Zhang, Yong Li View a PDF of the paper titled R$^2$Energy: A Large-Scale Benchmark for Robust Renewable Energy Forecasting under Diverse and Extreme Conditions, by Zhi Sheng and 3 other authors View PDF HTML (experimental) Abstract:The rapid expansion of renewable energy, particularly wind and solar power, has made reliable forecasting critical for power system operations. While recent deep learning models have achieved strong average accuracy, the increasing frequency and intensity of climate-driven extreme weather events pose severe threats to grid stability and operational security. Consequently, developing robust forecasting models that can withstand volatile conditions has become a paramount challenge. In this paper, we present R$^2$Energy, a large-scale benchmark for NWP-assisted renewable energy forecasting. It comprises over 10.7 million high-fidelity hourly records from 902 wind and solar stations across four provinces in China, providing the diverse meteorological conditions necessary to capture the wide-ranging variability of renewable generation. We further establish a standardized, leakage-free forecasting paradigm that grants all models identical access to future Numerical Weather Prediction (NWP) signals, enabling fair ...