电力系统保护与控制2018，Vol.46Issue(9)：55-61,7.DOI:10.7667/PSPC171217
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
Ultra-short-term wind power prediction based on wavelet and minimum resource allocation network
摘要
Abstract
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.关键词
风电场/神经网络/小波分析/最小资源分配网络/超短期风电功率预测Key words
wind farm/neural network/wavelet analysis/minimum resource allocation network/ultra-short-term wind power prediction引用本文复制引用
杨杰,霍志红,何永生,郭苏,邱良,许昌..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[J].电力系统保护与控制,2018,46(9):55-61,7.基金项目
中丹国际科技合作专项项目资助(2014DFG62530) （2014DFG62530）
国家自然科学基金项目资助(51507053) （51507053）
中央高校基本科研业务费项目-科技发展前瞻性研究专项资助(2017B42314)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). （2017B42314）
