Research on short-term wind power forecasting models incorporating convolutional neural networks

Wind power forecasting plays a crucial role in grid dispatch and energy management. This paper designs a convolutional neural network-based wind power prediction model that employs a multibranch architecture to extract multiscale features and incorporates residual connections to optimize the network training process. The model effectively integrates meteorological data with historical power information to achieve high-precision predictions. The multi-branch architecture simultaneously captures short-term fluctuations and long-term generation patterns, while residual connections resolve gradient vanishing issues in deep network training, accelerating model convergence. Comparisons with traditional statistical methods and standard deep learning approaches demonstrate the proposed model's significant advantages, particularly in complex and variable meteorological conditions where it exhibits superior generalization and robustness. Compared to existing approaches, this model excels in forecasting extreme weather events, providing reliable technical support for wind farm management and grid dispatch. This advancement facilitates the seamless integration of wind energy resources into the energy mix.