When Does Advection-Aware Graph Nowcasting Help? A Controlled Study of Distributed Solar Ramp Forecasting with a Self-Supervised Cloud-Motion Estimator

A controlled synthetic study of distributed solar ramp forecasting found that an explicit advection-aware graph does not beat a plain static or learned-adjacency spatiotemporal GNN when the cloud-motion vector (CMV) is estimated by classical cross-correlation, and that roughly half the benefit of a perfect CMV comes from supplying an accurate motion vector as an input feature rather than from graph structure. The authors introduce a self-supervised cloud-motion estimator, a position-aware encoder trained 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 cross-correlation across every wind regime, and feeding its frozen 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. Advection helps only when the advective displacement over the forecast horizon, v*H, fits inside the sensor network, and the authors report a negative result for a spatially-coherent probabilistic head, noting all claims rest on a single synthetic simulator and that real-network validation is the necessary next step.

arXiv:2609.30286v1 Announce Type: new Abstract: 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.