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文章摘要
基于KPCA和BP神经网络的短期负荷预测
Short-Term Power Load Forecasting Based on Kernel Principal Component Analysis and BP Neural Network
Received:February 02, 2015  Revised:April 27, 2015
DOI:
中文关键词: 电力系统  负荷预测  核主成分分析  神经网络
英文关键词: power system, load forecasting, kernel principal component analysis(KPCA), neural network
基金项目:多源互补源网协调优化调度理论及方法的研究(5227201350PM)
Author NameAffiliationE-mail
Liu Chang* School of Electrical Engineering and Information,Sichuan University changliu0128@163.com 
Liu Tianqi School of Electrical Engineering and Information,Sichuan University  
Chen Zhenhuan Gansu Electric Power Corporation  
Wang Fujun Gansu Electric Power Corporation  
He Chuan School of Electrical Engineering and Information,Sichuan University  
Guan Tieying Gansu Electric Power Corporation  
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中文摘要:
      本文提出了一种基于核主成分分析(KPCA)和BP神经网络的短期负荷预测方法。首先考虑负荷影响因素得到初始神经网络输入变量;然后运用核主成分分析的方法对初始输入变量进行降维,得到神经网络的输入变量;最后在每一个时刻点上建立改进了训练算法的神经网络进行预测。采用本文提出的方法对甘肃某地区2014年的负荷进行了预测,并与已有的两种方法进行比较,结果表明本文方法可提高负荷预测的精度。
英文摘要:
      A method for short-term load forecasting based on kernel principal component analysis and BP neural network is proposed .Firstly, original input variables of neural network were got fully considering the load factors. Then, the dimension of original input variables was reduced by Kernel Principal Component Analysis method to get input variables of neural network. Finally, improved training algorithm of neural network was established to predict the load for every moment. Load of a region in Gansu Province in 2014 was forecasted by using the proposed method in the paper. Comparison with other two methods shows that the method in the paper can improve the accuracy of load forecasting.
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