A Novel Network for Short-Term Wind Speed Prediction: Mitigating Distribution Shift and Feature Loss

Abstract

Accurate wind speed forecasting is essential for mitigating the challenges of wind power grid integration. However, existing wind speed prediction models overlook the distributional shift problem within wind speed series, and this time-varying distribution can significantly impact wind prediction accuracy. In this paper, we propose the Distribution Shift and Feature Decoupling Network (DSFD-Net), which addresses the issue of distributional shifts occurring both within the input series and between the input and predicted series through a distribution matching model and distribution mapping module, respectively. Additionally, we introduce a feature decoupling module to mitigate the feature loss encountered in our work. We conduct extensive experiments on two datasets, and comprehensive experimental results demonstrate that DSFD-Net achieves at least a 4.1% reduction in error metrics compared to other wind speed forecasting models, indicating superior performance.