A new support vector machine optimized by improved particle swarm optimization and its application

来源期刊：中南大学学报(英文版)2006年第5期

论文作者：李翔 杨尚东 乞建勋

文章页码：568 - 572

Key words：support vector machine; particle swarm optimization algorithm; short-term load forecasting; simulated annealing

Abstract: 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 improved particle swarm optimization algorithm was used to optimize the parameters of SVM (c,σandε). 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.

基金信息：the National Natural Science Foundation of China