A Multi-Scale CNN-Transformer Network with Residual Correction for Ultra-Short-Term Photovoltaic Power Forecasting

Accurate photovoltaic (PV) power forecasting is essential for the reliable integration of renewable energy into electrical grids. This paper proposes a novel Multi-Scale CNN-Transformer network with Residual Correction (MSCT-RCM) for ultra-short-term PV power forecasting. The model integrates parallel multi-scale convolutional neural networks (CNNs) to extract local temporal features, a Transformer encoder to capture long-range dependencies, and a Residual Correction Module (RCM) that dynamically refines predictions using historical error patterns. A two-stage training strategy is employed to stabilize learning and enhance performance. Experimental evaluation on two years of operational data from a large-scale PV plant demonstrates that the proposed model achieves an R2 value of 0.9944 for 15-minute-ahead forecasts and reduces mean absolute error (MAE) and root mean square error (RMSE) by over 50% in one-hour-ahead predictions compared to benchmark models. The MSCT-RCM model therefore exhibits strong potential for deployment in scenarios requiring high-precision predictions, such as smart grid scheduling.