电测与仪表2016，Vol.53Issue(17)：49-54,6.
基于小波包－神经网络的太阳逐时辐射预测
Study on prediction of solar radiation intensity based on wavelet decomposition and BP neural network
摘要
Abstract
The amount of solar radiation is affected by the season , atmospheric conditions , cloud conditions , tempera-ture , humidity and even dust and other weather factors with a strong time variability and randomness .For the predic-tion method of nonlinear radiation , many methods are put forward currently .However , the following problems are still presented , such as the intelligent algorithm selecting is unreasonable , network structure generalization ability is poor and prediction accuracy is not ideal .In view of the deficiency of that a photovoltaic power station solar radiation inten-sity of the original hourly data is not obvious and the normal BP neural network can not be completely mapped its fea -tures,this paper puts forward a prediction model based on Wavelet Packet Neural Network ( WPNN) .It used wavelet packet to transform the radiation intensity sequence multi-scale decomposition and established several BP neural net-work models to forecast each frequency components , and obtaining the complete prediction value with the wavelet packet reconstruction finally .The results show that the prediction accuracy is significantly improved to meet the expec-ted results , which demonstrates the effectiveness and practical value of the model .关键词
太阳辐射/预测/小波包变换/神经网络Key words
solar radiation/prediction/wavelet transform/neural network分类
信息技术与安全科学引用本文复制引用
陈杰,张新燕,吕光建..基于小波包－神经网络的太阳逐时辐射预测[J].电测与仪表,2016,53(17):49-54,6.基金项目
国家自然科学基金资助项目（51367015）；新疆维吾尔自治区科技支疆项目（201491112）；国网新疆电网项目 （）
