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

• Published in: Chinese science bulletin 2014-04, Vol.59 (11), p.1167-1175
• Main Authors: Liu, Yongqian, Yan, Jie, Han, Shuang, David, Infield, Tian, De, Gao, Linyue
• Format: Article
• Language: English
• 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
• ISSN: 1001-6538; 1861-9541
• DOI: 10.1007/s11434-014-0119-7

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.