A novel spatiotemporal relation fused network for solar photovoltaic power forecasting

Accurate prediction of solar photovoltaic power is crucial for renewable integration. However, existing methods struggles to simultaneously capture its complex spatial correlation and nonlinear temporal dependencies. Here, we propose a novel spatiotemporal relationship fusion network (STRFN) for short-term prediction of photovoltaic power generation. STRFN uses convolutional neural networks to extract spatial features, long short-term memory networks to capture time dependence, and an attention mechanism to enhance its expressiveness. Additionally, the optimal network hyperparameters of STRFN are identified through Bayesian optimization. Moreover, it employs advanced data preprocessing techniques to improve input data quality. These techniques include feature recognition, principal component analysis, location coding, and sliding window segmentation. Our STRFN is applied to two typical PV systems for demonstration and compared with traditional deep learning models. The results show that our model’s accuracy and stability significantly outperform traditional deep learning models, with RMSE of 2.46 and 0.036, and MAPE of 1.51 % and 1.94 %. Furthermore, in predictions for typical days across four seasons, our STRFN still maintained consistent superior performance—evidenced by its normalized RMSE (NRMSE) of 0.90 % and 0.61 % for the two PV systems. Finally, we integrate data processing, model training, and results visualization together into a one-stop platform and make it user-friendly and easily improved for solar power prediction. Our proposed method along with its forecasting platform can offer valuable insights and guidelines for researchers and PV operators.