引用本文:
【打印本页】   【HTML】   【下载PDF全文】   查看/发表评论  【EndNote】   【RefMan】   【BibTex】
←前一篇|后一篇→ 过刊浏览    高级检索
本文已被:浏览 2088次   下载 846 本文二维码信息
码上扫一扫!
基于小波变换和混合神经网络的短期负荷预测
尹成群,康丽峰,李丽,王红云
作者单位
摘要:
提出通过小波分解对各负荷子序列进行特性分析初选影响因素后,采用信息熵法从初选变量中自动筛选出对负荷较重要的因素,然后采用改进的主成分分析法消除重要影响因素间的相关性,采用动态聚类法对各分解序列的样本归类,通过灰色关联分析选择出与预测时刻负荷模式最相似的类作为神经网络训练的典型样本集,采用蚁群优化算法训练各子序列相应神经网络模型,采用小波重构得到最终负荷预测结果。并利用某地区1999年的实际负荷对所提方法进行验证,结果表明了该方法的合理性和有效性。
关键词:  负荷预测,小波变换,信息熵,主成分分析,动态聚类法,蚁群优化算法
DOI:
分类号:TM715
基金项目:
Short-term load forecast based on combination of wavelet transform and hybrid neural network
YIN Cheng-qun  KANG Li-feng  LI Li  WANG Hong-yun
Abstract:
A hybrid load forecast method is put forward.The character analysis is carried out with wavelet decomposition for each load subsequence and influencing factors are thus determined,from which main factors are selected using the information entropy method and their relativity is eliminated using the improved principal component analysis method.The dynamic clustering analysis is used to divide the historical load data into several categories and the grey relative analysis to pick out one as the typical sample set,which is most similar to the forecasting load mode.The ant colony optimization algorithm is then used to train the corresponding neural network model of each decomposed subsequence and the wavelet reconstruction is used to achieve final forecasts.Actual loads of a district in 1999 are taken for verification,which shows the proposed method is rational and effective.
Key words:  load forecast,wavelet transform,information entropy,principal component analysis,dynamic clustering algorithm,ant colony optimization algorithm

用微信扫一扫

用微信扫一扫