Short-Term Load Forecasting of Microgrid Based on Chaotic Particle Swarm Optimization

被引：12

作者：

Ma, Han ^{[1
]}

Tang, Jing Min ^{[1
]}

机构：

[1] Kunming Univ Technol, Sch Informat Engn & Automat, Kunming, Yunnan, Peoples R China

来源：

PROCEEDINGS OF THE 3RD INTERNATIONAL CONFERENCE ON MECHATRONICS AND INTELLIGENT ROBOTICS (ICMIR-2019) | 2020年 / 166卷

关键词：

Chaos Theory; Particle Swarm Optimization; Least Square Vector Machine; Short-term Load Forecasting;

D O I：

10.1016/j.procs.2020.02.026

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In order to solve the problem of non-optimal when Particle Swarm Optimization (PSO) optimizes least square support vector machine (LSSVM), a short-term load forecasting method based on Chaos theory is proposed.Firstly, chaos theory is introduced into the prediction model to improve the particle swarm algorithm, and then PSO combined with chaos theory is used to optimize the parameters of LSSVM. Finally, the method is applied to short-term load forecasting, and the forecasting results are obtained through Matlab simulation training The experimental simulation shows that the method can not only reduce the possibility of the algorithm falling into local extremum, but also improve the learning ability, thus improving the accuracy of prediction. (C) 2020 The Authors. Published by Elsevier B.V.

引用

页码：546 / 550

页数：5

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