东北大学学报（自然科学版）

东北大学学报(自然科学版) ›› 2010, Vol. 31 ›› Issue (4): 482-485.DOI: -

• 论著 • 上一篇    下一篇

基于DPCA-BP神经网络的中长期电力负荷预测方法

张石;张瑞友;汪定伟;   

  1. 东北大学信息科学与工程学院;
  • 收稿日期:2013-06-20 修回日期:2013-06-20 出版日期:2010-04-15 发布日期:2013-06-20
  • 通讯作者: -
  • 作者简介:-
  • 基金资助:
    国家自然科学基金资助项目(70931001,70771021);;

Medium/long-term load forecasting based on DPCA-BP neural network

Zhang, Shi (1); Zhang, Rui-You (1); Wang, Ding-Wei (1)   

  1. (1) School of Information Science and Engineering, Northeastern University, Shenyang 110004, China
  • Received:2013-06-20 Revised:2013-06-20 Online:2010-04-15 Published:2013-06-20
  • Contact: Zhang, S.
  • About author:-
  • Supported by:
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摘要： 针对PCA-神经网络预测方法解决预测问题时,忽视数据自相关性而造成的预测结果难以满足实际工程要求精度的研究现状,建立了预测数据的增广矩阵.通过计算前l时刻数据确定增广矩阵的维数,并把得到增广后的预测数据作为BP神经网络的输入变量,建立了基于DPCA-BP神经网络的预测模型,给出了模型结构.该模型能有效地去除自变量系统中与因变量无关的数据信息,增加自变量系统中数据的自相关性.算例比较分析表明,所建立模型的模型成分解释性增强,预测精度提高,预测效果优于PCA-BP神经网络方法.

关键词: 动态主元分析, 数据拟合, BP神经网络, 负荷预测, 电力系统

Abstract: The result of the load forecasting based on PCA neural network cannot meet the accuracy required with the autocorrelation between data being ignored. An augmented matrix is therefore given, of which the number of dimensions is determined by adding the observed data for l time past together, and the forecast data after augmentation are taken as the input variables in BP neural network. Then, a forecasting model is developed on the basis of DPCA-BP neural network, with its architecture described. The model can exclude efficiently the data irrelevant to dependent variables from the independent variable system so as to intensity the autocorrelatability between data of the independent variable system. Compared the two methods of load forecasting with each other, the exemplification results show that the load forecasting model we developed is better than that based on PCA-BP neural network, since it provides higher interpretability and forecasting accuracy.

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