发表机构
Appsofa LLC（Appsofa 有限责任公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过受控合成实验证明，平流感知图神经网络仅在特定条件下有效，并引入自监督云运动估计器，以特征输入方式实现约60%的预测误差改善。
AI 中文摘要
在分布式光伏（PV）或辐照度传感器网络中，对云引起的功率爬坡进行短期预测是电网运营商公认的痛点。一个自然的想法是让图神经网络（GNN）具备平流感知能力：将每个站点连接到其上风方向的站点，并根据云运动矢量（CMV）设置边的时间滞后，从而使爬坡在其物理到达之前被前向传播。通过使用具有已知风场的受控合成测试平台，我们表明：（i）使用真实的互相关CMV估计时，显式平流图并不优于普通的静态或学习邻接时空GNN；（ii）从完美CMV可获得的收益中，约有一半仅仅来自于将准确的运动矢量作为输入特征提供，而非来自图结构；（iii）只有当平流位移（即预报时间范围内的v*H）能适配在传感器网络内部时，平流才有帮助。受（ii）启发，我们引入了一个小型自监督云运动估计器——一个仅基于多滞后光流重建目标（使用退火核）训练的位置感知编码器——它能够将真实风矢量恢复到2-4度的中位角度误差，在所有风况下比经典互相关方法好2-4倍。冻结此估计器并将其矢量输入预报器，在中度风况下可缩小约60%的oracle-CMV RMSE差距（相对于无平流，RMSE降低8-15%），且无需外部风数据。我们还报告了空间相干概率头的一个负面结果。所有结论均基于单一合成模拟器；我们讨论了为什么真实网络验证是必要的下一步，并概述了该验证。
英文摘要
Short-term forecasting of cloud-induced power ramps across a network of distributed photovoltaic (PV) or irradiance sensors is a recognised pain point for grid operators. A natural idea is to make the graph neural network (GNN) advection-aware: connect each site to the sites upwind of it, with edge time-lags set by the cloud-motion vector (CMV), so that a ramp is propagated forward before it physically arrives. Using a controlled synthetic testbed with a known wind field, we show that (i) with a realistic cross-correlation CMV estimate, an explicit advection graph does not beat a plain static or learned-adjacency spatiotemporal GNN; (ii) roughly half of the benefit available from a perfect CMV comes simply from providing an accurate motion vector as an input feature, not from graph structure; and (iii) advection helps only when the advective displacement over the forecast horizon, v*H, fits inside the sensor network. Motivated by (ii), we introduce a small self-supervised cloud-motion estimator -- a position-aware encoder trained only on a multi-lag optical-flow reconstruction objective with an annealed kernel -- that recovers the true wind vector to 2-4 degrees median angular error, 2-4x better than the classical cross-correlation method across every wind regime. Freezing this estimator and feeding its vector to the forecaster closes about 60% of the oracle-CMV RMSE gap at moderate wind (8-15% RMSE reduction over no advection), with no external wind data. We also report a negative result for a spatially-coherent probabilistic head. All claims are established on a single synthetic simulator; we discuss why real-network validation is the necessary next step and outline it.
