PVCast: Cloud Evolution for Joint Probabilistic Forecasting of Distributed Solar Power

Evolving clouds are an important source of PV power variability, and uncertainty in their evolution affects forecasts across locations and time. These effects shape both marginal power distributions and their spatiotemporal dependence. Prior work largely treats uncertainty in cloud evolution and dependence among PV outputs as separate modeling problems. We introduce PVCast, a joint probabilistic framework that couples marginal PV forecasts and spatiotemporal dependence through shared cloud evolution. PVCast models this evolution with learned transport-source-sink dynamics and represents its uncertainty with an ensemble of future cloud-field sequences. Within each sequence, local cloud conditions inform marginal power forecasts, while the common evolution induces dependence across systems and forecast steps. Across three real-world PV datasets, PVCast outperforms strong time-series, multimodal, and joint probabilistic baselines in marginal and aggregate forecasting, while better capturing the spatial and temporal dependence of joint power trajectories.