基于粒子群优化的LS-SVM短期风电功率预测研究

风电的波动性、间歇性和随机性导致风电功率预测时间较长、误差较大;为提高预测精度,缩短预测时间,采用粒子群算法(PSO)对最小二乘支持向量机(LS-SVM)算法进行参数寻优,进而建立优化预测模型进行仿真;结果表明:优化的模型比RBF和LS-SVM具有更高的预测精度.

Research on LS-SVM Short-term Wind Power Prediction Based on Particle Swarm Optimization

Volatility, intermittence and randomness of wind power cause longer time and bigger error in wind power prediction, therefore, in order to improve prediction accuracy and shorten prediction time, this paper uses particle swarm optimization to optimize the parameters for least square support vector machine (LS-SVM) algorithm, then sets up the optimization prediction model to conduct simulation, and the results show that the optimized model has higher prediction accuracy than RBF and LS-SVM.