3D-PV: Enhancing PV power prediction by modeling spatial uncertainty under dynamic shading conditions

The Earth's revolution and geographic variability introduce spatial uncertainty in photovoltaic (PV) systems. Subtle spatial variations give rise to dynamic shading conditions (DSC), which disrupt power prediction over time. Existing models often neglect to capture the effects of spatial uncertainty, and consequently struggle to address the DSC in PV systems. This paper presents a 3D-PV framework, which introduces a deblurring 3D reconstruction technique to produce spatial representations, preserving details of PV panels and their surrounding environment. Further, shadow variation matrices are constructed by the proposed ComputeShader-based shadow calculation algorithm, serving as a spatio-temporal representation to bridge the obtained spatial representations and dynamic shading variations. Building on the spatio-temporal representations, 3D-PV performs semantic fusion of shadow dynamics and irradiance signals, enabling temporally consistent power prediction under DSC. Experimental results, including ablation studies, demonstrate that precise spatial modeling effectively captures and simulates accurate shadow patterns over time. In particular, 3D-PV outperforms state-of-the-art prediction methods, achieving a 23.95 % reduction in mean squared error (MSE) for prediction accuracy. These results highlight the benefits of explicitly modeling spatial uncertainty and dynamically fusing spatio-temporal representations with irradiance signals under DSC, enabling accurate prediction of PV power.