CRAformer: A cross-residual attention transformer for solar irradiation multistep forecasting

In recent years, solar energy has gained widespread adoption in smart grids due to its safety, environmental friendliness, abundance, and other advantages, driving the application of photovoltaic (PV) power generation technology. Accurately predicting solar irradiance is essential for ensuring the operational stability of PV power systems, making it a critical challenge for maintaining grid security and stability. Although Transformer models in deep learning have achieved significant advancements in solar irradiance forecasting, existing studies often treat cross-batch time-series data (TSD) as independent. By overlooking the complex coupling relationships between different data batches, they fail to fully capture the underlying patterns in TSD under varying conditions. Moreover, handling the long-term dependencies and short-term weather-induced fluctuations inherent in TSD remains difficult. To address these issues, this paper proposes an efficient Transformer model (CRAformer) based on Cross-Residual Attention (CRA) for multi-step solar irradiance forecasting. CRAformer effectively captures the deep coupling relationships within TSD through a residual scoring mechanism, which can dynamically adjust feature weights and balance long-term dependencies with short-term variations. Furthermore, by incorporating a dual-output mode and dual-attention strategy, the model can deconstruct complex data structures and guide the prediction process with greater accuracy. Additionally, the newly designed Convolutional Weighted Fusion Module (CWFM) enhances the model's capability to recognize diverse patterns and characteristics in TSD. By dynamically regulating the information transfer process, the CWFM improves the model's generalization, fitting accuracy, and robustness. To evaluate CRAformer's performance, four prediction tasks with varying time steps (24 h, 48 h, 72 h, 96 h) were designed using irradiance datasets from different locations: Denver, Clark, and Folsom. The experimental results demonstrate that, compared to the second-best model, iTransformer, CRAformer reduces the RMSE by an average of 5.6 %, 3.9 %, and 5.6 % across the four prediction steps for the datasets from Denver, Clark, and Folsom, respectively. These results indicate that CRAformer offers significant advantages in multi-step solar irradiance forecasting, providing a valuable reference for future model optimization.