Multi-Step Model Predictive Control Based on Online Support Vector Regression Optimized by Multi-Agent Particle Swarm Optimization Algorithm

2018 ◽  
Vol 23 (5) ◽  
pp. 607-612 ◽  
Author(s):  
Xianlun Tang ◽  
Nianci Liu ◽  
Yali Wan ◽  
Fei Guo
Keyword(s):  
Particle Swarm Optimization ◽  
Predictive Control ◽  
Particle Swarm Optimization Algorithm ◽  
Support Vector ◽  
Online Support ◽  
Swarm Optimization ◽  
Step Model ◽  
Online Support Vector Regression ◽  
Multi Agent ◽  
Agent Particle

scholarly journals Power load forecasting algorithm based on nonlinear inertial factor change pattern particle swarm optimization algorithm

2018 ◽  
Vol 173 ◽  
pp. 02016
Author(s):  
Jin Liang ◽  
Wang Yongzhi ◽  
Bao Xiaodong
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Load Forecasting ◽  
Particle Swarm ◽  
Support Vector ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Power Load ◽  
Selection Of

The common method of power load forecasting is the least squares support vector machine, but this method is very dependent on the selection of parameters. Particle swarm optimization algorithm is an algorithm suitable for optimizing the selection of support vector parameters, but it is easy to fall into the local optimum. In this paper, we propose a new particle swarm optimization algorithm, it uses non-linear inertial factor change that is used to optimize the algorithm least squares support vector machine to avoid falling into the local optimum. It aims to make the prediction accuracy of the algorithm reach the highest. The experimental results show this method is correct and effective.

Sign in / Sign up

Export Citation Format

Share Document