基于小波与最小资源分配网络的超短期风电功率预测研究

针对风电场实际风速和风电功率序列的波动性、间歇性等特点以及RBF神经网络结构一旦确定隐节点个数就不可变等缺陷,提出了基于小波分析和最小资源分配网络的超短期风电功率预测方法。首先将历史风速和风电功率序列进行小波去噪及多频分解,得到多组高频信号和一组低频信号。然后对各频信号分别建立神经网络预测模型对未来4 h风电功率进行超短期预测。最后将各预测结果通过小波重构得到最终的超短期预测功率。实验结果证明,该方法能有效提高预测精度。

Ultra-short-term wind power prediction based on wavelet and minimum resource allocation network

Because the actual wind speed and wind power sequences are fluctuating, intermittent and the hidden node number of RBF neural network is unchangeable after the structure of RBF neural network is confirmed, a method of ultra-short-term wind power prediction based on wavelet and minimum resource allocation network is proposed. Firstly the historical wind speed and wind power sequences are denoised and multi frequency decomposed by wavelet transform, several high frequency signals and a low frequency signals are obtained. Then neural network prediction models of different frequency signals are built respectively to predict the wind power in the next 4 hours. Finally, the final ultra-short-term wind power prediction result is obtained from wavelet reconstruction of different components. The experimental results show that this method can effectively improve the prediction accuracy. This work is supported by Sino-Dan International S & T Cooperation Program (No. 2014DFG62530), National Natural Science Foundation of China (No. 51507053), and Fundamental Research Funds for the Central Universities (No. 2017B42314).