期刊论文

Short-term wind speed prediction based on NARX and chaos-support vector machine

Li, Yingqiu(liyq-2001@163.com)

中文

电力系统保护与控制

1674-3415

2019

47

23

65-73

10.19783/j.cnki.pspc.181533

This work is supported by National Natural Science Foundation of China (No. 11731012 and No. 11571052) and Natural Science Foundation of Hunan Province (No. 2018JJ2417). Key words: short-term wind speed prediction; chaotic characteristics; time series; EGARCH; NARX; support vector machine

本校为第一且通讯机构

风速预测精度的提高,对降低风力发电成本、合理安排风场选址等方面有着积极作用。使用DBSCAN聚类对所有数据进行去噪处理,选择最合适的风速数据序列进行实证研究。首先,针对风速数据序列具有混沌性而对预测结果产生影响的问题,采用C-C法确定相空间重构中所需参数。与此同时,结合混沌理论建立混沌支持向量机模型,用以预测未来24 h的风速值。之后,将该模型与EGARCH模型以及具有外生输入的非线性自回归网络(NARX)模型的预测结果进行对比。最后,根据各预测模型的RMSE和MAPE精度对模型预测效果进行评估。结果表明:基于混沌时间序列的支持向量机模型对NWTC m~2气象站所在地风速具有最佳预测效果。

The improvement of wind speed prediction accuracy plays a positive role in reducing the cost of wind power generation and arranging the location of wind farms. The DBSCAN clustering method is used to denoise all data and select the most appropriate sequences for empirical research. Firstly, since the chaotic wind speed data sequences would affect the prediction results, C-C method is made to determine the needed parameters in phase space reconstruction. Meanwhile, a model of chaos support vector machine combined chaos theory is established to p...