DSC-CBAM-BiLSTM: A Hybrid Deep Learning Framework for Robust Short-Term Photovoltaic Power Forecasting

To address the challenges of photovoltaic (PV) power prediction in highly dynamic environments. We propose an improved Long Short-Term Memory (ILSTM) model. The model uses Principal Component Analysis (PCA) and Particle Swarm Optimization (PSO) for feature selection, ensuring key information is preserved while reducing dimensionality. The Depthwise Separable Convolution (DSC) module extracts spatial features, while the Channel-Spatial Attention Mechanism (CBAM) focuses on important time-dependent patterns. Finally, Bidirectional Long Short-Term Memory (BiLSTM) captures nonlinear dynamics and long-term dependencies, boosting prediction performance. The model is called DSC-CBAM-BiLSTM. It selects important features adaptively. It captures key spatial-temporal patterns and improves forecasting performance based on RMSE, MAE, and $R^2$. Extensive experiments using real-world PV datasets under varied meteorological scenarios show the proposed model significantly outperforms traditional approaches. Specifically, RMSE and MAE are reduced by over 70%, and the coefficient of determination ($R^2$) is improved by 8.5%. These results confirm the framework’s effectiveness for real-time, short-term PV forecasting and its applicability in energy dispatching and smart grid operations.