Application of support vector machine model in wind power prediction based on particle swarm optimization

Ning  Lu; Ying  Liu

doi:10.3934/dcdss.2015.8.1267

Article Contents

2015, Volume 8, Issue 6: 1267-1276. Doi: 10.3934/dcdss.2015.8.1267

This issue Previous Article Implementation of Mamdami fuzzy control on a multi-DOF two-wheel inverted pendulum robot Next Article Higher order uniformly close-to-convex functions

Application of support vector machine model in wind power prediction based on particle swarm optimization

Ning Lu^1, and
Ying Liu^2,

1.
School of Automation, Wuhan University of Technology, Wuhan
2.
Wuhan Electric Power Dispatching and Communication Center, Wuhan

Received: May 31, 2015

Revised: August 31, 2015

Published: November 2015

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Abstract

Wind energy is a kind of renewable and clean energy, and wind power is a non-hydropower renewable energy which has the best technical and economic conditions for large-scale development. It is characterized by fluctuation, intermittency, low energy density, etc., so wind power is also fluctuating. When a large-scale wind farm is connected to a power grid, great fluctuation in wind power will cause adverse effect to the power balance and frequency adjustment of the power grid. If the generation power of the wind farm can be prediction, the electricity dispatch department can arrange dispatch plans in advance according to the change in wind power and better protect the power balance and operation safety of the power grid. In this article, a SVM model is used to predict wind power and modified PSO is used to optimize SVM parameters, realizing the optimized selection of the SVM model parameters, which makes such prediction more close to actual law. Actual calculation examples shows that the prediction method used in the article has good convergence, high prediction precision and actual application value.

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Mathematics Subject Classification: Primary: 58F15, 58F17; Secondary: 53C35.

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