郑州大学学报（工学版）2012，Vol.33Issue(6)：32-35,4.DOI:10.3969/j.issn.1671-6833.2012.06.008
基于改进粒子群优化算法的短期风电功率预测
Short-term Wind Power Prediction Based on Modified Particle Swarm Optimization Algorithm
摘要
Abstract
In view of the parameter selection problems existing in the traditional support vector machine ( SVM ) model in wind power prediction, this paper puts forward a new forecasting model; with modified particle swarm optimization algorithm ( MPSO) for the optimal parameters of the SVM model, the classical PSO is a global optimization algorithm. Based on it, the modified PSO ( MPSO ) is proposed. Results show that the SVM model optimized by the MPSO is effective in short-term wind power prediction, and the prediction precision is improved.关键词
支持向量机/风电功率预测/改进粒子群优化算法/精度Key words
SVM/ wind power prediction/MPSO/precision分类
信息技术与安全科学引用本文复制引用
徐敏,袁建洲,刘四新,常俊甫..基于改进粒子群优化算法的短期风电功率预测[J].郑州大学学报（工学版）,2012,33(6):32-35,4.基金项目
江西省自然科学基金资助项目(20114BAB206036BAB2) （20114BAB206036BAB2）
