Short-Term Load Forecasting of LSSVM Based on Improved PSO Algorithm

摘要：

　　Based on the empirical,the precision of the forecasting will directly affect the reliability,economy and quality of power supply in power system.An improved particle swarm optimizer (IPSO) is proposed to be used on the least squares support vector machine (LSSVM) algorithm,which optimized the initialization parameters and improved the accuracy of short-term load forecasting.This thesis use the historical data of a certain grid to set up the short-term load forecasting model based on the optimization algorithm.While the data had comprehensive consideration the meteorology,weather,date,type and other factors which influencing the load.Compare with the LSSVM algorithm and the standard PSO-LSSVM,the empirical results show that IPSOLSSVM model is more applicable in terms of convergence effect,accurate prediction and fast speed.The IPSO not only improves the accuracy of load forecasting,but also prevents LSSVM from great reliance on empirical results and random selection.

关键词： load forecasting improved particle swarm optimization least square support vector machine parameter selection

作者: Qianhui Gong Wenjun Lu Wenlong Gong Xueting Wang

作者单位: College of Electrical and Information Engineering,Hunan University,Changsha 410082,China State Grid Chongqing Electric Power CO. Yongchuan Power Supply Company,Chongqing 400000,China

会议类型: 国际会议

会议名称: Chinese Conference on Pattern Recognition, CCPR(2014年全国模式识别学术会议)

会议地点: 长沙

会议语种:英文

页码: 63-71

在线出版日期: 2014-11-01（万方平台首次上网日期，不代表论文的发表时间）