广东电力2017，Vol.30Issue(2)：29-33,5.DOI:10.3969/j.issn.1007-290X.2017.02.005
基于混合算法优化神经网络的风电预测模型
Wind Power Forecasting Model Based on Optimized Neural Network of Hybrid Algorithm
摘要
Abstract
In allusion to shortcomings of particle swarm optimization (PSO) algorithm being likely to run into partial optimization and generalization of Elman neural network being insufficient,this paper presents a kind of method for short-term wind power forecasting based on wavelet packet decomposition (WPD) and catalytic particle swarm optimization (CPSO) algorithm for optimizing Elman neural network (ENN).This method uses wavelet transform to decompose wind power samples into multi-level sequences,adopts CPSO-ENN optimized by CPSO algorithm for forecasting the sub-sequence of wind power obtained from reconstruction,and finally ovcrlays forecasting value of each sub-sequence to obtain actual forecasting results.Simulating verification for actual operational data of one wind power field indicates that this new model has higher forecasting precision.关键词
催化粒子群算法/神经网络/小波包分解/子序列/风电功率预测Key words
catalytic particle swarm optimization algorithm/neural network/wavelet packet decomposition/sub-sequence/wind power forecasting分类
信息技术与安全科学引用本文复制引用
董朕,殷豪,孟安波..基于混合算法优化神经网络的风电预测模型[J].广东电力,2017,30(2):29-33,5.基金项目
广东省科技计划项目(2016A010104016) （2016A010104016）
广东电网公司科技项目(GDKJQQ20152066) （GDKJQQ20152066）
