构建超大规模电力系统概率预测基准，揭示安全与精度的权衡。
Navigating the Safety-Fidelity Trade-off: Massive-Variate Time Series Forecasting for Power Systems via Probabilistic Scenarios
- 提出基于六条输电网的多变量概率预测基准PowerPhase，通道数达2000至36964。
- 发现模型在分布精度和约束满足性上存在安全-精度权衡，评价指标含Safety_mBrier等。
- 设计PowerForge模型，通过分类型解码头和因果桥结构，综合表现最优。
概率预测模型正广泛应用于具有不同通道物理特性和运行约束的多变量系统，但现有基准未在大规模下评估这些特性。公共标准多变量基准最多仅支持2000个通道，而电力系统基准要么缺乏时间结构，要么无概率评估。我们引入PowerPhase，一个基于六条输电网的的概率预测基准，涵盖2000至36964个联合预测通道，远超主流基准一个数量级。每个目标轨迹由交流潮流计算生成，PowerPhase配备约束感知指标，包括Safety_mBrier、NECV和CVaR-alpha，补充CRPS与Distortion。在八个基线和三个随机种子下，分布准确性与约束满足性排名不一致，形成我们所称的安全-精度权衡。我们进一步提出PowerForge，一种基于情景的分位数预测器，采用类型特定解码头和变量组间的因果桥结构，在所有电网上均取得平均排名最佳。
原文摘要 · Abstract (English)
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.
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