[1]孙新程,孔建寿,刘 钊.基于核主成分分析与改进神经网络的电力负荷中期预测模型[J].南京理工大学学报(自然科学版),2018,42(03):259.[doi:10.14177/j.cnki.32-1397n.2018.42.03.001]
 Sun Xincheng,Kong Jianshou,Liu Zhao.Middle-term power load forecasting model based on kernel principalcomponent analysis and improved neural network[J].Journal of Nanjing University of Science and Technology,2018,42(03):259.[doi:10.14177/j.cnki.32-1397n.2018.42.03.001]
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基于核主成分分析与改进神经网络的电力负荷中期预测模型()
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《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

卷:
42卷
期数:
2018年03期
页码:
259
栏目:
出版日期:
2018-06-30

文章信息/Info

Title:
Middle-term power load forecasting model based on kernel principalcomponent analysis and improved neural network
文章编号:
1005-9830(2018)03-0259-07
作者:
孙新程孔建寿刘 钊
南京理工大学 自动化学院,江苏 南京 210094
Author(s):
Sun XinchengKong JianshouLiu Zhao
School of Automation,Nanjing University of Science and Technology,Nanjing 210094,China
关键词:
核主成分分析 粒子群优化 反向传播神经网络 电力负荷 中期预测
Keywords:
kernel principal component analysis particle swarm optimization back propagation neural network power load middle-term forecasting
分类号:
TM715
DOI:
10.14177/j.cnki.32-1397n.2018.42.03.001
摘要:
为了提高电力负荷中期预测水平,提出了1种核主成分分析(KPCA)和粒子群优化反向传播神经网络(PSO-BPNN)相结合的电力负荷中期预测模型。引入KPCA对原始输入空间降维重构,将降维后的数据集输入PSO算法优化的BPNN模型中,提出了月平均最大预测负荷修正日预测负荷的方法,输出待预测日的最大预测负荷。采用欧洲智能技术网络提供的负荷数据进行验证,实验结果的平均绝对百分误差为1.39%。
Abstract:
A forecasting model is proposed by combing kernel principal component analysis(KPCA)with particle swarm optimization and back propagation neural network(PSO-BPNN)to improve the level of middle-term power load forecasting. Dimensionality reduction and reconstruction of the original input space are made with the KPCA. The data set after dimensionality reduction is input to a BPNN model optimized by PSO. The average daily peak load in each month is forecasted to revise the daily load,and the daily peak forecasting load is output in the end. This model is tested using the data provided by the European Network on Intelligent Technologies(EUNITE),and the mean absolute percent error(MAPE)of this model is 1.39%.

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备注/Memo

备注/Memo:
收稿日期:2017-03-06 修回日期:2017-07-18
基金项目:国家自然科学基金(51507086); 江苏省自然科学基金(BK20150839)
作者简介:孙新程(1993-),男,硕士生,主要研究方向:智能电网与控制,E-mail:sunxc24@163.com; 通讯作者:孔建寿(1962-),男,教授,主要研究方向:智能制造和智能电网信息工程等,E-mail:kongjs77@163.com。
引文格式:孙新程,孔建寿,刘钊. 基于核主成分分析与改进神经网络的电力负荷中期预测模型[J]. 南京理工大学学报,2018,42(3):259-265.
投稿网址:http://zrxuebao.njust.edu.cn
更新日期/Last Update: 2018-06-30