To address the challenge of forecasting power output from large-scale photovoltaic (PV) bases in desert regions during sand and dust storms, this paper proposes a hybrid data-physics driven prediction method. This approach utilizes satellite remote sensing to obtain regional irradiance data, transforming the traditional one-dimensional time-series forecasting into a two-dimensional spatiotemporal sequence prediction, thereby tracking the dynamic evolution of irradiance intensity under the influence of sand and dust. Firstly, a forecasting model based on a conditional variational autoencoder (CVAE) optimized with a recurrent state-space model (RSSM) is constructed to effectively capture both the deterministic trends and stochastic fluctuations in irradiance variation, providing a reliable input basis for power calculation. Secondly, at the physical modeling level, the model comprehensively considers the isotropic scattering characteristics and changes in sky clarity induced by sand and dust weather, establishing a physical mapping relationship from irradiance to PV output. This mitigates the constraint of scarce historical operational data in desert and sandy regions. This research provides a novel solution for regional-level PV power forecasting under extreme sand and dust weather, contributing to enhanced dispatchability and transmission stability of renewable energy bases during abrupt meteorological changes.