Short-Term Wind Power Forecasting Based on ISFOA-SVM

Short-term wind power prediction is critical for stable power system operation, but the non-stationarity and randomness of wind power output hinder prediction accuracy. To address this, this study proposes an improved superb fairy-wren optimization algorithm (ISFOA), which dynamically adjusts search step size via an adaptive learning factor to enhance global exploration and integrates a differential evolution strategy to optimize local search, improving convergence speed and optimization accuracy. Convergence analysis based on the Markov chain model verifies ISFOA’s stability. The ISFOA is combined with a Support Vector Machine (SVM) to construct the ISFOA-SVM model for short-term wind power prediction and is validated on real datasets from a southern China wind farm. Performance comparisons with four state-of-the-art models (SFOA-SVM, PSO-SVM, MFO-SVM, and GWO-SVM) show ISFOA-SVM achieves the best results across all metrics: MAE (0.3158), MBE (0.0126), RMSE (0.3304), and $R^2$ (0.9982). Compared to SFOA-SVM, it reduces RMSE by 67.08%, MBE by 54.68%, MAE by 1.10%, and increases $R^2$ by 0.34%. It outperforms PSO-SVM and MFO-SVM, which show intermediate results, and GWO-SVM, which exhibits the worst MAE, RMSE, and $R^2$ despite better MBE. These results confirm ISFOA-SVM’s effectiveness in improving short-term wind power prediction accuracy.