电力系统自动化2017，Vol.41Issue(21)：40-45,6.DOI:10.7500/AEPS20170321005
基于神经网络平均影响值的超短期风电功率预测
Ultra-short-term Wind Power Prediction Based on Neural Network and Mean Impact Value
摘要
Abstract
To solve the problems of variable redundancy and model complexity in the prediction model based on the dynamic neural network,an ultra-short-term wind power prediction method is proposed by combining the neural network(NN)and the mean impact value(MIV).In this method,the external and internal contribution rates of the input variables to the output variables (wind power prediction value) are taken into account,and the input variable with the largest contribution to the output variables is selected.Then an optimized NN prediction model for ultra-short-term wind power prediction is developed. The experimental results show that the proposed model reduces the complexity of the prediction model,mitigates the influence of the measuring noise on the prediction accuracy,and obtains good wind power prediction results.关键词
风电功率/超短期预测/动态神经网络/平均影响值/变量筛选Key words
wind power/ultra-short-term prediction/dynamic neural network (DNN)/mean impact value (MIV)/variable selection引用本文复制引用
徐龙博,王伟,张滔,杨莉,汪少勇,李煜东..基于神经网络平均影响值的超短期风电功率预测[J].电力系统自动化,2017,41(21):40-45,6.基金项目
国家高技术研究发展计划(863计划)资助项目(2013AA050601).This work is supported by National High Technology Research and Development Program of China (863 Program) (No.2013AA050601). （863计划）
