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.

翻译：概率预测模型日益部署在具有不同通道物理特性和运行约束的多变量系统中，但现有基准测试未能大规模评估这两种特性。公开的经典多变量基准测试通道上限为2,000个，而电力系统基准测试要么缺乏时间结构，要么缺乏概率评估。我们提出PowerPhase——一个基于六个输电网格（包含2,000至36,964个联合预测通道）的概率预测基准，其规模超出经典多变量基准测试一个数量级以上。每个目标轨迹均为交流潮流求解的输出结果，PowerPhase配备了包含约束感知指标（如Safety_mBrier、NECV和CVaR-alpha）的系统，与CRPS和Distortion形成互补。在八个基线模型和三个随机种子上，分布精度和约束满足度对模型进行了不同排序，我们将这种权衡称为“安全-保真”。我们进一步提出PowerForge——一种基于场景的分位数预测器，具有类型特定的解码头及变量组间的因果桥接，在所有网格上均取得了最佳平均排名。