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中文摘要：
改进的粒子群优化(PSO ) 优化的一台新支持向量机器(SVM ) 与退火模仿结合了算法(SA ) 被建议。由与模仿的退火的方法合并，粒子群优化(SAPSO ) 的全球寻找能力在碰巧，并且粒子群优化的寻找的能力被学习。然后，改进粒子群优化算法被用来优化 SVM 的参数(c，σ和ε) 。基于一个地区性的力量格子在北方中国提供的运作的数据，方法在预报的实际短术语负担被使用。与 PSO-SVM 和传统的 SVM 相比，在试验性的过程的建议方法的平均时间由 11.6 s 和 31.1 s 减少，并且建议方法的精确分别地增加 1.24% 和 3.18% 的结果表演。那么，改进方法比 PSO-SVM 和传统的 SVM 好。
英文摘要：
A new support vector machine （SVM） optimized by an improved particle swarm optimization （PSO） combined with simulated annealing algorithm （SA） was proposed. By incorporating with the simulated annealing method, the global searching capacity of the particle swarm optimization（SAPSO） was enchanced, and the searching capacity of the particle swarm optimization was studied. Then, the improyed particle swarm optimization algorithm was used to optimize the parameters of SVM （c,σ and ε）. Based on the operational data provided by a regional power grid in north China, the method was used in the actual short term load forecasting. The results show that compared to the PSO-SVM and the traditional SVM, the average time of the proposed method in the experimental process reduces by 11.6 s and 31.1 s, and the precision of the proposed method increases by 1.24% and 3.18%, respectively. So, the improved method is better than the PSO-SVM and the traditional SVM.
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