构建首个覆盖30国10年电力数据的基准，揭示现有预测模型在复杂相关性下的局限性。
Real-E: A Foundation Benchmark for Advancing Robust and Generalizable Electricity Forecasting
- 构建跨30+国家、74个电站的10年电力数据集，含丰富元信息
- 20种基线模型在该数据集上表现显著下降，因相关性动态更复杂非平稳
- 提出新指标量化相关性结构变化，适合评估模型鲁棒性
电力预测对电网可靠性和运行效率至关重要。尽管时间序列预测近年取得进展，现有基准在空间和时间范围上仍有限，缺乏多能源特征，影响其在真实场景中的可靠性与适用性。为此，我们提出Real-E数据集，涵盖30多个欧洲国家超过74个发电站的10年数据，附带丰富元信息。基于Real-E，我们对20种不同类型的基线模型进行了广泛的分析与基准测试。引入新指标以量化相关性结构的变化，发现现有方法在该数据集上表现不佳，因数据展现出更复杂的非平稳相关性动态。研究结果凸显当前方法的关键局限，并为构建更鲁棒的预测模型提供了坚实实证基础。
原文摘要 · Abstract (English)
Energy forecasting is vital for grid reliability and operational efficiency. Although recent advances in time series forecasting have led to progress, existing benchmarks remain limited in spatial and temporal scope and lack multi-energy features. This raises concerns about their reliability and applicability in real-world deployment. To address this, we present the Real-E dataset, covering over 74 power stations across 30+ European countries over a 10-year span with rich metadata. Using Real- E, we conduct an extensive data analysis and benchmark over 20 baselines across various model types. We introduce a new metric to quantify shifts in correlation structures and show that existing methods struggle on our dataset, which exhibits more complex and non-stationary correlation dynamics. Our findings highlight key limitations of current methods and offer a strong empirical basis for building more robust forecasting models
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