电力系统保护与控制2011，Vol.39Issue(15)：43-46,51,5.
基于自适应粒子群优化的SVM模型在负荷预测中的应用
Application of support vector machine model in load forecasting based on adaptive particle swarm optimization
陆宁 1武本令 2刘颖3
作者信息
- 1. 武汉理工大学自动化学院,湖北,武汉,4300702
- 2. 许昌市高级技术学校,河南,许昌,461000
- 3. 武汉供电公司,湖北,武汉,430015
- 折叠
摘要
Abstract
In order to improve the precision of short-term load forecasting, and aiming at the parameter selection of traditional SVM in load forecast this paper proposes a new load forecasting model, i.e., using the improved adaptive particle swarm optimization (PSO) for seeking the optimal parameters of support vector machine(SVM) model. The classical PSO is a global optimization algorithm. Based on it, the improved PSO (FAPSO) is proposed and its convergence tests are conducted, and then the SVM model based on the FAPSO optimization is applied to the short-term power load forecasting. The simulation results show that the adaptive particle swarm optimization-based SVM load forecasting model is more accurate than the traditional SVM model and has certain practical value.关键词
自适应/粒子群优化/支持向量机/全局优化/负荷预测Key words
adaptive/ particle swarm optimization/ support vector machine/ global optimization/ load forecasting分类
信息技术与安全科学引用本文复制引用
陆宁,武本令,刘颖..基于自适应粒子群优化的SVM模型在负荷预测中的应用[J].电力系统保护与控制,2011,39(15):43-46,51,5.
