An optimized short-term wind power interval prediction method considering NWP accuracy

In recent years, the accuracy of the wind power prediction has been urgently studied and improved to sat- isfy the requirements of power system operation. In this paper, the relevance vector machine （RVM）-based models are established to predict the wind power and its interval for a given confidence...

Full description

Saved in:
Bibliographic Details
Published inChinese science bulletin Vol. 59; no. 11; pp. 1167 - 1175
Main Authors Liu, Yongqian, Yan, Jie, Han, Shuang, David, Infield, Tian, De, Gao, Linyue
Format Journal Article
LanguageEnglish
Published Heidelberg Science China Press 01.04.2014
Subjects
Accuracy
Algorithms
Chemistry/Food Science
Confidence intervals
Earth Sciences
Engineering
Genetic algorithms
Humanities and Social Sciences
Intervals
Life Sciences
Mathematical models
multidisciplinary
Physics
Science
Science (multidisciplinary)
Swarm intelligence
Wind power
区间预测
参数优化算法
支持向量机
电力系统运行
短期
预测模型
预测精度
风电场
China, People's Rep
NWP accuracy
Wind power interval prediction
Genetic algorithm
Particle swarm optimization
Relevance vector machine
Online AccessGet full text

Cover

More Information
Summary:In recent years, the accuracy of the wind power prediction has been urgently studied and improved to sat- isfy the requirements of power system operation. In this paper, the relevance vector machine （RVM）-based models are established to predict the wind power and its interval for a given confidence level. An NWP improvement module is presented considering the characteristic of NWP error. Moreover, two parameter optimization algorithms are applied to further improve the prediction model and to compare each performance. To take three wind farms in China as examples, the performance of two RVM-based models optimized, respectively, by genetic algorithm （GA） and particle swarm optimization （PSO） are compared with predictions based on a genetic algorithm-artificial neural network （GA-ANN） and support vector machine. Results show that the proposed models have better prediction accuracy with GA-RVM model and more efficient calcu- lation with PSO-RVM.
Bibliography:11-1785/N
Wind power interval prediction - NWPaccuracy ； Relevance vector machine ； Particleswarm optimization ； Genetic algorithm
In recent years, the accuracy of the wind power prediction has been urgently studied and improved to sat- isfy the requirements of power system operation. In this paper, the relevance vector machine （RVM）-based models are established to predict the wind power and its interval for a given confidence level. An NWP improvement module is presented considering the characteristic of NWP error. Moreover, two parameter optimization algorithms are applied to further improve the prediction model and to compare each performance. To take three wind farms in China as examples, the performance of two RVM-based models optimized, respectively, by genetic algorithm （GA） and particle swarm optimization （PSO） are compared with predictions based on a genetic algorithm-artificial neural network （GA-ANN） and support vector machine. Results show that the proposed models have better prediction accuracy with GA-RVM model and more efficient calcu- lation with PSO-RVM.
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1001-6538
1861-9541
DOI:10.1007/s11434-014-0119-7