Wind Power Prediction Using Particle Swarm Optimized Support Vector Machine

This study presents a wind power prediction method using Particle Swarm Optimized Support Vector Machine (PSO-SVM), where the Particle Swarm Optimization algorithm enhances the Support Vector Machine algorithm. The approach significantly improves prediction model accuracy and stability. Particle Swarm Optimization mimics natural swarm behavior by simulating particle velocity and position updates to locate optimal solutions. In our implementation, PSO optimizes SVM's critical parameters (typically kernel parameters and penalty factors) through iterative fitness evaluations. The algorithm initializes a particle population where each particle represents a potential SVM parameter set, with velocity vectors guiding the search toward global optimum. This parameter optimization process enhances SVM's generalization capability, leading to higher prediction precision in wind power forecasting. The method provides more reliable predictions for the wind energy industry by implementing systematic hyperparameter tuning through swarm intelligence principles.