A Multivariate Wind Power Fitting Model Based on Cluster Wavelet Neural Network
A Multivariate Wind Power Fitting Model Based on Cluster Wavelet Neural Network
复制标题
基于簇小波神经网络的多元风电拟合模型
DOI：
10.1007/978-981-10-6364-0_10
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Binghong Li and Xiao-Yu Zhang
中科院分区：
文献类型：
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作者：
Ruiwen Zheng;Qing Fang;Zhiyuan Liu;Binghong Li and Xiao-Yu Zhang
本文采用层次聚类法对气象资料进行风能等级分类，然后应用0-1输出法对风能等级进行量化。其次，利用小波神经网络对多变量风电功率数据进行拟合，解决了风电功率数据的随机性、不确定性和波动性问题。最后，通过一个风电场的数值试验，得到了较为理想的拟合结果，误差精度为，验证了该模型的有效性。
In this paper, we select the hierarchical cluster method to classify the wind energy level with the meteorological data, and then apply the 0–1 output method to quantify the wind energy level. Next, we utilize wavelet neural network to fit multivariate wind power data, which solves the problem of randomness, intermittency and volatility of wind power data. Finally, a wind-power numerical experiment shows the ideal fitting results with an error precision ofand demonstrates the effectiveness of our model.