R$^2$Energy: A Large-Scale Benchmark for Robust Renewable Energy Forecasting under Diverse and Extreme Conditions
arXiv:2602.15961v1 Announce Type: new 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 acce
arXiv:2602.15961v1 Announce Type: new 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 and reproducible comparison across state-of-the-art representative forecasting architectures. Beyond aggregate accuracy, we incorporate regime-wise evaluation with expert-aligned extreme weather annotations, uncovering a critical ``robustness gap'' typically obscured by average metrics. This gap reveals a stark robustness-complexity trade-off: under extreme conditions, a model's reliability is driven by its meteorological integration strategy rather than its architectural complexity. R$^2$Energy provides a principled foundation for evaluating and developing forecasting models for safety-critical power system applications.
Executive Summary
This article presents R$^2$Energy, a large-scale benchmark for robust renewable energy forecasting under diverse and extreme conditions. The benchmark comprises over 10.7 million high-fidelity hourly records from 902 wind and solar stations across four provinces in China, providing a wide range of meteorological conditions. The authors establish a standardized forecasting paradigm that enables fair and reproducible comparison across state-of-the-art forecasting architectures. The benchmark reveals a critical 'robustness gap' typically obscured by average metrics, highlighting a stark robustness-complexity trade-off. This work provides a principled foundation for evaluating and developing forecasting models for safety-critical power system applications.
Key Points
- ▸ R$^2$Energy: A large-scale benchmark for robust renewable energy forecasting under diverse and extreme conditions
- ▸ Comprises over 10.7 million high-fidelity hourly records from 902 wind and solar stations across four provinces in China
- ▸ Standardized forecasting paradigm enables fair and reproducible comparison across state-of-the-art forecasting architectures
Merits
Strength
The authors provide a comprehensive and well-structured dataset for evaluating and developing robust renewable energy forecasting models, addressing a significant gap in the field. The standardized forecasting paradigm ensures fair and reproducible comparison across different models, allowing for accurate evaluation of their performance.
Demerits
Limitation
The study is limited to wind and solar power forecasting, and its applicability to other renewable energy sources is unclear. Additionally, the authors' focus on robustness under extreme conditions may overlook the importance of average accuracy and other metrics in real-world applications.
Expert Commentary
The article presents a significant contribution to the field of renewable energy forecasting, addressing the critical need for robust models that can withstand diverse and extreme conditions. The R$^2$Energy benchmark provides a principled foundation for evaluating and developing such models, and its standardized forecasting paradigm ensures fair and reproducible comparison across different architectures. While the study has some limitations, its findings have significant implications for the development of safe and reliable renewable energy forecasting models, and it is an essential step towards ensuring a more sustainable and resilient energy future.
Recommendations
- ✓ Future studies should investigate the applicability of the R$^2$Energy benchmark to other renewable energy sources and explore the development of more robust forecasting models that can integrate multiple sources of renewable energy.
- ✓ Policymakers and industry stakeholders should prioritize the development and deployment of robust renewable energy forecasting models to support the integration of renewable energy sources into the grid and ensure grid stability and operational security.
