Support vector machine forecasting method improved by chaotic particle swarm optimization and its application  

作　　者：李彦斌 张宁 李存斌 

机构地区：[1]School of Economics and Management,Beijing University of Aeronautics and Astronautics [2]School of Business Administration,North China Electric Power University

出　　处：《Journal of Central South University》2009年第3期478-481,共4页中南大学学报（英文版）

基　　金：Project(70572090) supported by the National Natural Science Foundation of China

摘　　要：By adopting the chaotic searching to improve the global searching performance of the particle swarm optimization (PSO), and using the improved PSO to optimize the key parameters of the support vector machine (SVM) forecasting model, an improved SVM model named CPSO-SVM model was proposed. The new model was applied to predicting the short term load, and the improved effect of the new model was proved. The simulation results of the South China Power Market’s actual data show that the new method can effectively improve the forecast accuracy by 2.23% and 3.87%, respectively, compared with the PSO-SVM and SVM methods. Compared with that of the PSO-SVM and SVM methods, the time cost of the new model is only increased by 3.15 and 4.61 s, respectively, which indicates that the CPSO-SVM model gains significant improved effects.By adopting the chaotic searching to improve the global searching performance of the particle swarm optimization （PSO）, and using the improved PSO to optimize the key parameters of the support vector machine （SVM） forecasting model, an improved SVM model named CPSO-SVM model was proposed. The new model was applied to predicting the short term load, and the improved effect of the new model was proved. The simulation results of the South China Power Market＇s actual data show that the new method can effectively improve the forecast accuracy by 2.23% and 3.87%, respectively, compared with the PSO-SVM and SVM methods. Compared with that of the PSO-SVM and SVM methods, the time cost of the new model is only increased by 3.15 and 4.61 s, respectively, which indicates that the CPSO-SVM model gains significant improved effects.

关 键 词：chaotic searching particle swarm optimization （PSO） support vector machine （SVM） short term load forecast 

分 类 号：O212[理学—概率论与数理统计]