中南大学学报（自然科学版）2011，Vol.42Issue(11)：3408-3414,7.
基于改进蚁群算法优化参数的LSSVM短期负荷预测
Parameters selection for LSSVM based on modified ant colony optimization in short-term load forecasting
摘要
Abstract
An optimization method based on the modified ant colony optimization (MACO) algorithm was used to select the two parameters of least square support vector machine (LSSVM) model. In this method, the parameters of LSSVM model were considered the position vector of ants. Target individuals which lead the ant colony to do global rapid search were determined by dynamic and stochastic extraction, and the optimal ant of this generation searched in small step nearly. The optimal parameter value was obtained by MACO and modified ant colony optimization-least square support vector machine (MACO-LSSVM) forecasting model was obtained. The proposed model is applied to the short-term electrical power load forecasting problem. Every hour's load from 2009-08-01 to 2009-08-30 of area in Hunan province was taken as the sample data to be analyzed. The results indicate that the root-mean-square relative error of the proposed method is only 1.71%, which is less than those of BP and SVM model by 1.61% and 1.05%, respectively.关键词
最小二乘支持向量机/蚁群优化算法/参数优化/短期负荷预测Key words
least square support vector machine (LSSVM)/ ant colony optimization (ACO) algorithm/ parameter optimization/ short-term load forecasting分类
信息技术与安全科学引用本文复制引用
龙文,梁昔明,龙祖强,李朝辉..基于改进蚁群算法优化参数的LSSVM短期负荷预测[J].中南大学学报（自然科学版）,2011,42(11):3408-3414,7.基金项目
国家自然科学基金资助项目(60874070,61074069) （60874070,61074069）
贵州财经学院博士科研启动基金资助项目 （）
