基于极限学习机的居民用电行为分类分析方法
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摘要
针对大数据背景下智能用户用电行为分类问题,提出一种基于极限学习机(ELM)算法的用户用电行为分类方法。首先,在前期用户行为的特征优选策略的基础上,采用特征优选策略提取负荷曲线的最佳特征集对用户用电数据进行分类分析。然后,将特征优选集作为输入,通过比较不同隐含层激活函数和隐含层节点个数下训练集和测试集的正确率,优选出适用于用户用电行为分析的ELM算法的输入参数。最后,以国内和国外用户用电数据为数据源,进行算例仿真实验,通过与反向传播(BP)神经网络的对比分析表明,所提出的基于ELM算法的用户用电行为分析方法提高了检测的正确率并且降低了算法运行时间,能够更好地掌握用户用电负荷状态,实现配电网的削峰填谷。
Aiming at the classification problem of electricity consumption analysis of smart users under the background of big data,a classification method based on extreme learning machine(ELM)algorithm is proposed for electricity consumption behavior analysis.Firstly,based on the previous research of feature preference for electricity consumption behavior of smart users,the feature preference strategy is adopted to extract the best feature sets of the load curve,which helps to classify and analyze the data of electricity consumption for users.Then,the best feature sets are used as the input of ELM network.By comparing the accuracy of the training set and the test set with different hidden layer excitation functions and hidden layer node numbers,input parameters of ELM algorithm are selected,which are suitable for user's electricity consumption behavior analysis.Finally,the domestic and foreign electricity consumption data is taken as the data source to carry out the simulation experiment.Through the comparison and analysis with back propagation(BP)neural network,the results show that the analysis of electricity consumption behavior based on ELM algorithm improves the detection accuracy and reduces the algorithm operation time,which can better grasp the user load status and realize load balance of distribution network.
Aiming at the classification problem of electricity consumption analysis of smart users under the background of big data,a classification method based on extreme learning machine(ELM)algorithm is proposed for electricity consumption behavior analysis.Firstly,based on the previous research of feature preference for electricity consumption behavior of smart users,the feature preference strategy is adopted to extract the best feature sets of the load curve,which helps to classify and analyze the data of electricity consumption for users.Then,the best feature sets are used as the input of ELM network.By comparing the accuracy of the training set and the test set with different hidden layer excitation functions and hidden layer node numbers,input parameters of ELM algorithm are selected,which are suitable for user's electricity consumption behavior analysis.Finally,the domestic and foreign electricity consumption data is taken as the data source to carry out the simulation experiment.Through the comparison and analysis with back propagation(BP)neural network,the results show that the analysis of electricity consumption behavior based on ELM algorithm improves the detection accuracy and reduces the algorithm operation time,which can better grasp the user load status and realize load balance of distribution network.
引文
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[11]ZHAO Z,LEE W,SHIN Y,et al.An optimal power scheduling method for demand response in home energy management system[J].IEEE Transactions on Smart Grid,2013,4(3):1391-1400.
[12]VIVEKANANTHAN C,MISHRA Y,LEDWICH G.Demand response for residential appliances via customer reward scheme[J].IEEE Transactions on Smart Grid,2014,5(2):809-820.
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[15]张少敏,赵硕,王保义.基于云计算和量子粒子群算法的电力负荷曲线聚类算法研究[J].电力系统保护与控制,2014,42(21):93-98.ZHANG Shaomin,ZHAO Shuo,WANG Baoyi.Research of power load curve clustering algorithm based on cloud computing and quantum particle swarm optimization[J].Power System Protection and Control,2014,42(21):93-98.
[16]郭晓利,于阳.基于云计算的家庭智能用电策略[J].电力系统自动化,2015,39(17):114-119.DOI:10.7500/AEPS20150310017.GUO Xiaoli,YU Yang.A residential smart power utilization strategy based on cloud computing[J].Automation of Electric Power Systems,2015,39(17):114-119.DOI:10.7500/AEPS20150310017.
[17]肖乃慎,李博,孔德诗.大数据背景下的电网客户用电行为分析系统设计[J/OL].电子设计工程,2016[2018-02-11].http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=dzsjgc201617019.XIAO Naishen,LI Bo,KONG Deshi.Design of power grid customer’s behavior analysis system under the background of large data[J/OL].Electronic Design Engineering,2016[2018-02-11].http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=dzsjgc201617019.
[18]陆俊,朱炎平,彭文昊,等.智能用电用户行为分析特征优选策略[J].电力系统自动化,2017,41(5):58-63.DOI:10.7500/AEPS20160607002.LU Jun,ZHU Yanping,PENG Wenhao,et al.Feature selection strategy for electricity consumption behavior analysis in smart grid[J].Automation of Electric Power Systems,2017,41(5):58-63.DOI:10.7500/AEPS20160607002.
[19]HUANG G.Learning capability and storage capacity of twohidden-layer feedforward networks[J].IEEE Transactions on Neural Networks and Learning Systems,2003,14(2):274-81.
[20]HUANG G,CHEN L,SIEW C.Universal approximation using incremental constructive feedforward networks with random hidden nodes[J].IEEE Transactions on Neural Networks and Learning Systems,2006,17(4):879-892.
[21]甘露.极限学习机的研究与应用[D].西安:西安电子科技大学,2014.GAN Lu.Research and application of extreme learning machine[D].Xi’an:Xidian University,2014.
[22]HUANG G,ZHU Q,SIEW C.Extreme learning machine:theory and applications[J].Neurocomputing,2006,70(1):489-501.
[23]RAO C,MITRA S.Further contributions to the theory of generalized inverse of matrices and its applications[J].The Indian Journal of Statistics:Series A,1971,33(3):289-300.
[24]朱文俊,王毅,罗敏,等.面向海量用户用电特性感知的分布式聚类算法[J].电力系统自动化,2016,40(12):21-27.DOI:10.7500/AEPS20160316007.ZHU Wenjun,WANG Yi,LUO Min,et al.Distributed clustering algorithm for awareness of electricity consumption characteristics of massive consumers[J].Automation of Electric Power systems,2016,40(12):21-27.DOI:10.7500/AEPS20160316007.
[2]韩笑,狄方春,刘广一,等.应用智能电网统一数据模型的大数据应用架构及其实践[J].电网技术,2016,40(10):3206-3212.HAN Xiao,DI Fangchun,LIU Guangyi,et al.Application of big data application framework based on unified data model of smart grid and its practice[J].Power System Technology,2016,40(10):3206-3212.
[3]杨永标,颜庆国,王冬,等.居民用户智能用电建模及优化仿真分析[J].电力系统自动化,2016,40(3):46-51.DOI:10.7500/AEPS20150924010.YANG Yongbiao,YAN Qingguo,WANG Dong,et al.Intelligent electricity consumption modeling and optimal simulations for residential users[J].Automation of Electric Power Systems,2016,40(3):46-51.DOI:10.7500/AEPS20150924010.
[4]赵明宇,徐石明,高辉,等.基于模糊K-means算法的电动汽车应急供电策略[J].电力系统自动化,2016,40(5):91-95.DOI:10.7500/AEPS20150304001.ZHAO Mingyu,XU Shiming,GAO Hui,et al.Strategy of electric vehicle emergency power supply based on fuzzy K-means algorithm[J].Automation of Electric Power Systems,2016,40(5):91-95.DOI:10.7500/AEPS20150304001.
[5]李扬,王蓓蓓,李方兴.灵活互动的智能用电展望与思考[J].电力系统自动化,2015,39(17):2-9.DOI:10.7500/AEPS20150730004.LI Yang,WANG Beibei,LI Fangxing.Outlook and thinking of flexible and interactive utilization of intelligent power[J].Automation of Electric Power Systems,2015,39(17):2-9.DOI:10.7500/AEPS20150730004.
[6]史俊祎,文福拴,崔鹏程,等.参与需求响应的工业用户智能用电管理[J].电力系统自动化,2017,41(14):45-53.DOI:10.7500/AEPS20170203002.SHI Junyi,WEN Fushuan,CUI Pengcheng,et al.Intelligent energy management of industrial loads considering participation in demand response program[J].Automation of Electric Power Systems,2017,41(14):45-53.DOI:10.7500/AEPS20170203002.
[7]荆朝霞,胡荣兴,袁灼新,等.含风/光/抽水蓄能并计及负荷响应的海岛微网优化配置[J].电力系统自动化,2017,41(1):65-72.DOI:10.7500/AEPS20160106008.JING Zhaoxia,HU Rongxing,YUAN Zhuoxin,et al.Capacity configuration optimization for island microgrid with wind/solar/pumped storage considering demand response[J].Automation of Electric Power Systems,2017,41(1):65-72.DOI:10.7500/AEPS20160106008.
[8]李秋硕,王岩,孙宇军,等.BP神经网络在用电用户分类中的应用[J].现代电子技术,2017,40(9):156-158.LI Qiushuo,WANG Yan,SUN Yujun,et al.Application of BPneural network in electricity users classification[J].Electronic Technology,2017,40(9):156-158.
[9]孙毅,崔灿,陆俊,等.基于差量特征提取与模糊聚类的非侵入式负荷监测方法[J].电力系统自动化,2017,41(4):86-91.DOI:10.7500/AEPS20160504016.SUN Yi,CUI Can,LU Jun,et al.Non-intrusive load monitoring method based on delta feature extraction and fuzzy clustering[J].Automation of Electric Power Systems,2017,41(4):86-91.DOI:10.7500/AEPS20160504016.
[10]龚钢军,陈志敏,陆俊,等.智能用电用户行为分析的聚类优选策略[J].电力系统自动化,2018,42(2):58-63.DOI:10.7500/AEPS20170726009.GONG Gangjun,CHEN Zhimin,LU Jun,et al.Clustering optimization strategy for electricity consumption behavior analysis in smart grid[J].Automation of Electric Power Systems,2018,42(2):58-63.DOI:10.7500/AEPS20170726009.
[11]ZHAO Z,LEE W,SHIN Y,et al.An optimal power scheduling method for demand response in home energy management system[J].IEEE Transactions on Smart Grid,2013,4(3):1391-1400.
[12]VIVEKANANTHAN C,MISHRA Y,LEDWICH G.Demand response for residential appliances via customer reward scheme[J].IEEE Transactions on Smart Grid,2014,5(2):809-820.
[13]李培强,李欣然,陈辉华,等.基于模糊聚类的电力负荷特性的分类与综合[J].中国电机工程学报,2005,25(24):73-78.LI Peiqiang,LI Xinran,CHEN Huihua,et al.The characteristics classification and synthesis load based on fuzzy clustering[J].Proceedings of the CSEE,2005,25(24):73-78.
[14]刘莉,王刚,翟登辉.k-means聚类算法在负荷曲线分类中的应用[J].电力系统保护与控制,2011,39(23):65-68.LIU Li,WANG Gang,ZHAI Denghui.Application of k-means clustering algorithm in load curve classification[J].Power System Protection and Control,2011,39(23):65-68.
[15]张少敏,赵硕,王保义.基于云计算和量子粒子群算法的电力负荷曲线聚类算法研究[J].电力系统保护与控制,2014,42(21):93-98.ZHANG Shaomin,ZHAO Shuo,WANG Baoyi.Research of power load curve clustering algorithm based on cloud computing and quantum particle swarm optimization[J].Power System Protection and Control,2014,42(21):93-98.
[16]郭晓利,于阳.基于云计算的家庭智能用电策略[J].电力系统自动化,2015,39(17):114-119.DOI:10.7500/AEPS20150310017.GUO Xiaoli,YU Yang.A residential smart power utilization strategy based on cloud computing[J].Automation of Electric Power Systems,2015,39(17):114-119.DOI:10.7500/AEPS20150310017.
[17]肖乃慎,李博,孔德诗.大数据背景下的电网客户用电行为分析系统设计[J/OL].电子设计工程,2016[2018-02-11].http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=dzsjgc201617019.XIAO Naishen,LI Bo,KONG Deshi.Design of power grid customer’s behavior analysis system under the background of large data[J/OL].Electronic Design Engineering,2016[2018-02-11].http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=dzsjgc201617019.
[18]陆俊,朱炎平,彭文昊,等.智能用电用户行为分析特征优选策略[J].电力系统自动化,2017,41(5):58-63.DOI:10.7500/AEPS20160607002.LU Jun,ZHU Yanping,PENG Wenhao,et al.Feature selection strategy for electricity consumption behavior analysis in smart grid[J].Automation of Electric Power Systems,2017,41(5):58-63.DOI:10.7500/AEPS20160607002.
[19]HUANG G.Learning capability and storage capacity of twohidden-layer feedforward networks[J].IEEE Transactions on Neural Networks and Learning Systems,2003,14(2):274-81.
[20]HUANG G,CHEN L,SIEW C.Universal approximation using incremental constructive feedforward networks with random hidden nodes[J].IEEE Transactions on Neural Networks and Learning Systems,2006,17(4):879-892.
[21]甘露.极限学习机的研究与应用[D].西安:西安电子科技大学,2014.GAN Lu.Research and application of extreme learning machine[D].Xi’an:Xidian University,2014.
[22]HUANG G,ZHU Q,SIEW C.Extreme learning machine:theory and applications[J].Neurocomputing,2006,70(1):489-501.
[23]RAO C,MITRA S.Further contributions to the theory of generalized inverse of matrices and its applications[J].The Indian Journal of Statistics:Series A,1971,33(3):289-300.
[24]朱文俊,王毅,罗敏,等.面向海量用户用电特性感知的分布式聚类算法[J].电力系统自动化,2016,40(12):21-27.DOI:10.7500/AEPS20160316007.ZHU Wenjun,WANG Yi,LUO Min,et al.Distributed clustering algorithm for awareness of electricity consumption characteristics of massive consumers[J].Automation of Electric Power systems,2016,40(12):21-27.DOI:10.7500/AEPS20160316007.