Spatio temporal fusion network with GHI injection for multi-horizon solar forecasting
As the integration of photovoltaic (PV) systems into modern electrical grids continues to grow, there is an increasing demand for accurate short-term forecasting of both global horizontal irradiance (GHI) and PV power output. However, most existing deep learning models weakly couple GHI and power predictions, degrade under cloudy and partially cloudy conditions, and offer limited interpretability. To address these challenges, this study proposes the spatio-temporal fusion network (STFNet). This explainable hybrid model integrates convolutional neural networks for local feature extraction, unidirectional Long Short-Term Memory networks for temporal modelling, and XGBoost for nonlinear prediction, enhanced by a novel cloud-conditioned bidirectional GHI injection mechanism. The core innovation is a bidirectional gated fusion that couples a dedicated GHI branch and a PV branch in both directions: a GHI→PV blend gate transfers irradiance physics into the power pathway. In contrast, a PV→GHI residual gate allows recent power dynamics to refine the irradiance estimate, with both gates modulated by cloud-type conditioning so that the coupling adapts to prevailing sky conditions. Extensive evaluations on the SHEERM dataset across 15, 30, and 60 min horizons, using mean absolute error, root mean square error (RMSE), and R2 metrics stratified by season and cloud type, demonstrate that STFNet achieves a better overall performance among the eleven models compared, with an R2 of 0.9413 for GHI (RMSE 55.06 W m−2) while maintaining competitive PV performance (R2 0.9069, RMSE 58.49 W m−2). A controlled ablation isolates the bidirectional coupling as the sole driver of the GHI improvement, which is large (∼17% RMSE reduction over the unidirectional gate) and highly reproducible across random seeds; the improvement is consistent across all seasons, horizons, and cloud buckets. LIME and SHAP analyses further reveal that lagged PV power, clear-sky GHI, precipitable water content, and sinusoidal time encodings are the most influential predictors, enhancing both accuracy and physical interpretability for reliable solar forecasting and grid integration.