摘要
针对粒子群算法(Particle Swarm Optimization,PSO)优化最小二乘支持向量机(Least Square Support Vector Machine,LSSVM)时存在非最优解问题,提出了一种基于混沌理论(Chaos theory)的粒子群优化LSSVM参数的短期负荷预测的方法.该预测模型首先引入混沌理论,对粒子群算法加以改进;然后让结合混沌理论的PSO对LSSVM回归估计进行参数优化,得到CPSO-LSSVM;最后将该方法应用于短期负荷预测,通过Matlab仿真训练得到预测结果.仿真实验结果表明该方法不仅可以降低算法陷入局部极值的可能性,还提高了学习能力,从而提高了预测的精准度.
There is a non-optimal solution problem when optimizing the Least Square Support Vector Machine(LSSVM) for Particle Swarm Optimization(PSO). A short-term load prediction method based on Chaos theory for optimizing LSSVM parameters is proposed. The prediction model first introduces chaos theory,improves the particle swarm algorithm. Then it allows the PSO combined with chaos theory to optimize the LSSVM regression estimate to obtain CPSO-LSSVM. Finally, the method is applied to short-term load prediction,and the prediction results are obtained by Matlab simulation training. The simulation results show that this method can not only reduce the probability of the algorithm falling into local extremum, but also improve the learning ability, thus improving the accuracy of prediction.
作者
唐菁敏
马含
TANG Jing-min;MA Han(Faculty of Information Engineering and Automation,Kunming University of Science and Technology,Kunming 650500,China)
出处
《云南大学学报（自然科学版）》
CAS
CSCD
北大核心
2019年第6期1123-1129,共7页
Journal of Yunnan University(Natural Sciences Edition)
基金
国家自然科学基金(61761025)
关键词
混沌理论
粒子群算法
最小二乘向量机
短期负荷预测
chaos theory
Particle Swarm Optimization algorithm
Least Square Vector Machine
short-term load forecasting
作者简介
通信作者:唐菁敏(1979-),男,湖南人,博士,副教授,研究方向为无线认知网络,物联网等.E-mail:tang_min213@163.com..
