中文摘要：

由采用改进粒子群优化(PSO ) 的全球寻找性能的混乱寻找，并且用改进 PSO 到优化预报的支持向量机器(SVM ) 的参数建模的钥匙，一个改进 SVM 模型说出 CPSO-SVM 模型被建议。新模型被用于预言短术语负担，并且新模型的改进效果被证明。华南力量市场的实际数据的模拟结果证明分别地，新方法罐头有效地与 PSO-SVM 和 SVM 方法相比在 2.23% 和 3.87% 改进预报精确性。与 PSO-SVM 和 SVM 方法的相比，分别地，新模型的时间费用被 3.15 和 4.61 s 仅仅增加它显示 CPSO-SVM 模型获得重要改进效果。

英文摘要：

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.