Short-Term Photovoltaic Power Forecasting Based on CNN–LSTM–Attention Mechanism

Accurate short-term photovoltaic (PV) power forecasting is crucial for maintaining grid stability. However, existing hybrid deep learning models suffer from inherent limitations owing to static feature-weighting mechanisms, often displaying significant phase lag and peak clipping under severe meteorological fluctuations. To address this, in this study, we adopt a hybrid CNN–LSTM–Attention forecasting framework incorporating an SE-based attention strategy. Field validation at a 150 kW PV power plant in Ningxia, China, demonstrated that the adopted model achieved a Root Mean Square Error (RMSE) convergence of 2.157 kW. Notably, this represented a 41.92% reduction in error compared to the standard LSTM benchmark and a further 16.46% improvement over the suboptimal CNN-LSTM baseline, explicitly confirming the specific contribution of the SE-based attention mechanism. Moreover, multi-weather evaluations and ablation studies confirm the framework’s robustness. Dynamic Time Warping (DTW) and Diebold–Mariano (DM) tests establish its statistical superiority and the reduction in phase lag against baselines. Residual analysis reveals a leptokurtic distribution with white noise properties, confirming the reduction in systematic bias. Consequently, this high-fidelity tracking allows precise minute-level ramping detection and decreases spinning reserve demands in practical power dispatch.