电力系统运维和预测建模团队终于有了能评估安全约束的基准——PowerPhase 比现有基准大一个数量级，PowerForge 在安全与精度间取得最佳平衡，做电网概率预测的可以直接参考。
论文提出 PowerPhase，一个面向电力系统的大规模概率预测基准，包含 6 个传输电网，通道数从 2000 到 36964，远超现有基准。该基准引入约束感知指标（如 Safety_mBrier、NECV、CVaR-α），以评估预测在安全约束下的表现。研究发现，分布准确性与约束满足之间存在“安全-保真度”权衡，不同模型在这两个维度上排名不同。作者进一步提出 PowerForge，一种基于场景的分位数预测器，采用类型特定的解码头和变量组间的因果桥，在所有电网规模上取得最佳平均排名。
Navigating the Safety-Fidelity Trade-off: Massive-Variate Time Series Forecasting for Power Systems via Probabilistic Scenarios
Probabilistic forecasting models are increasingly deployed on multivariate systems with distinct channel physics and operational constraints, but existing benchmarks evaluate neither property at scale. Public canonical multivariate benchmarks cap out at 2,000 channels, while power-system benchmarks either lack temporal structure or probabilistic evaluation. We introduce PowerPhase, a probabilistic forecasting benchmark built on six transmission grids ranging from 2,000 to 36,964 jointly forecasted channels, more than an order of magnitude beyond popular canonical multivariate benchmarks. Each target trajectory is the output of an AC power-flow solve, and PowerPhase ships with constraint-aware metrics, including Safety_mBrier, NECV, and CVaR-alpha, that complement CRPS and Distortion. Across eight baselines and three seeds, distributional accuracy and constraint satisfaction rank models differently, a trade-off we term safety-fidelity. We further propose PowerForge, a scenario-based quantile forecaster with type-specific decoding heads and a causal bridge between variable groups, which achieves the best average rank on every grid.