Wind Power Prediction Using Support Vector Machine Optimized by Particle Swarm Optimization

Wind power forecasting constitutes a critical research area in renewable energy, with accurate predictions being essential for grid dispatch and energy management. The integration of Particle Swarm Optimization (PSO) with Support Vector Machine (SVM) methodologies significantly enhances prediction accuracy by automating parameter optimization. Support Vector Machine (SVM) is a machine learning algorithm particularly effective for small-sample, high-dimensional datasets, widely applied in regression and classification tasks. However, in wind power forecasting, SVM's performance is highly dependent on kernel function parameters and penalty factors. Traditional parameter selection methods like grid search incur high computational costs and exhibit limited efficiency. Particle Swarm Optimization (PSO) is a swarm intelligence-based optimization algorithm that efficiently searches for optimal solutions by simulating bird flock foraging behavior. By employing PSO to optimize SVM's critical parameters—including kernel function parameters (e.g., RBF kernel's gamma) and penalty factor C—the algorithm automatically identifies optimal parameter combinations, overcoming limitations of manual adjustment. Implementation typically involves initializing particle positions and velocities, then iteratively updating them based on personal and global best solutions using velocity and position update equations. In practical applications, this approach first constructs feature sets using historical wind power data, wind speed, temperature, and other meteorological factors. The PSO algorithm then iteratively optimizes SVM model parameters through fitness evaluation (often using cross-validation error as the objective function). The optimized model ultimately delivers high-precision predictions. Compared to conventional methods, PSO-SVM not only improves prediction accuracy but also reduces computational time, making it suitable for large-scale wind farm power forecasting tasks. Key implementation steps include data normalization, PSO parameter initialization (swarm size, inertia weight), and SVM model retraining with optimized parameters. The method's advantage lies in combining PSO's global search capability with SVM's strong generalization performance, providing an intelligent and efficient solution for wind power forecasting. Code implementation typically involves libraries like scikit-learn for SVM coupled with custom PSO optimization loops to dynamically adjust hyperparameters during training.