Wind Power Ultra-Short-Term Instantaneous Prediction Based on Spatiotemporal BP Neural Network Parameter Optimization and Error Correction Unit

Ultra-short-term wind power exhibits significant minute-level fluctuation characteristics, leading to substantial instantaneous prediction errors. To mitigate the impact of instantaneous wind power prediction errors, the following steps are taken: First, the correlation between instantaneous prediction errors and meteorological factors is determined, and strongly associated variables are selected as model inputs. Next, the particle swarm optimization algorithm is employed to optimize the initial weights and threshold parameters of the spatiotemporal backpropagation neural network prediction model to enhance its performance. Subsequently, based on the nonlinear relationship between wind speed/direction data and instantaneous prediction errors, a wind speed matrix gradient correction method and a deep learning correction method with physical constraints on prediction errors are constructed to address errors caused by declining model generalization under strong disturbances. To validate the effectiveness of the proposed prediction algorithm integrating parameter optimization and the error correction method, it is compared with typical convolutional neural networks, long short-term memory networks, and backpropagation neural algorithms. The results demonstrate that compared to other wind power prediction strategies, this method reduces the mean absolute percentage error, root mean square error, and mean absolute error by 48.49%, 45.51%, and 50.8%, respectively. These results confirm that combining error correction strategies with prediction model parameter optimization effectively enhances the ability to reduce instantaneous wind power prediction errors, providing a practical technical solution for optimizing ultra-short-term wind power prediction accuracy and offering valuable insights for ensuring the stability of wind power grid integration.