STFNet: A Specialized Time-Frequency Domain Feature Extraction Neural Network for Long-Term Wind Power Forecasting

The rapid expansion of renewable energy has raised the demand for accurate, long-term wind power forecasting. However, wind power series are strongly affected by meteorological factors and exhibit pronounced volatility, making long-term prediction challenging. To model these characteristics more comprehensively, we propose STFNet, a dual-branch neural architecture that integrates time-domain and frequency-domain modeling. STFNet contains two key modules: (1) an MLFE module, which explicitly captures lag effects and non-stationary transitions through parallel multi-scale convolutions and a difference-convolution branch and further enhances multivariate dependency learning via cross-variable interaction modeling, and (2) an FGFE module, which applies DCT to capture long-cycle trends and uses a learnable low-pass filter for noise suppression. Experiments on two real-world wind farm datasets (LY and HG) show that STFNet consistently outperforms strong baselines, achieving average MSE reductions of 15.9–26.6% while maintaining a high computational efficiency. Ablation studies further confirm the effectiveness of each module, indicating the strong practical potential of STFNet for wind farm operation and management.