智能用电用户行为分析的聚类优选策略
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摘要
针对大数据背景下用户智能用电行为最佳聚类数目的选择问题,提出一种用户用电行为分析的聚类优选策略。在前期智能用电用户行为分析的特征优选策略研究的基础上,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聚类分析;然后提出聚类数优选策略,通过综合考虑准确度评价指标和有效度评价指标确定最佳聚类数目。以国内外的用电数据为数据源,仿真验证了所述策略可以选择合理的聚类数目,有效提高用电行为分析的数据聚类效果。
Aiming at the problem of choosing the optimal cluster number for electricity consumption behavior in smart grid under the background of big data,a clustering optimization strategy of electricity consumption behavior analysis is put forward.Based on the research of the feature optimization selection strategy of the electricity consumption behavior analysis in the early stage,the feature optimization selection strategy is used to extract the optimal feature set of the load curve to cluster the users' electricity data.Then the optimal strategy of clustering number is proposed,and the optimal number of clusters is determined by comprehensively considering the accuracy evaluation index and the effectiveness evaluation index.Based on the data of domestic and foreign electricity data,the experimental simulation verifies that the proposed strategy can select the reasonable number of clustering and effectively improve the clustering effect of the data of electricity consumption.
Aiming at the problem of choosing the optimal cluster number for electricity consumption behavior in smart grid under the background of big data,a clustering optimization strategy of electricity consumption behavior analysis is put forward.Based on the research of the feature optimization selection strategy of the electricity consumption behavior analysis in the early stage,the feature optimization selection strategy is used to extract the optimal feature set of the load curve to cluster the users' electricity data.Then the optimal strategy of clustering number is proposed,and the optimal number of clusters is determined by comprehensively considering the accuracy evaluation index and the effectiveness evaluation index.Based on the data of domestic and foreign electricity data,the experimental simulation verifies that the proposed strategy can select the reasonable number of clustering and effectively improve the clustering effect of the data of electricity consumption.
引文
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[2]龚钢军,熊琛,许刚.基于层次分析判断矩阵的配用电通信业务模型的研究[J].电力系统保护与控制,2011,41(21):19-23.GONG Gangjun,XIONG Chen,XU Gang.Research of communication business model of power distribution and utilization based on the analytic hierarchy judgment matrix[J].Power System Protection and Control,2011,41(21):19-23.
[3]鲁文,杜红卫,丁恰,等.智能配电网优化调度设计及关键技术[J].电力系统自动化,2017,41(3):1-6.DOI:10.7500/AEPS20160405009.LU Wen,DU Hongwei,DING Qia,et al.Design and key technologies of optimal dispatch for smart distribution network[J].Automation of Electric Power Systems,2017,41(3):1-6.DOI:10.7500/AEPS20160405009.
[4]龚钢军.智能配电通信网关键技术研究[D].北京:华北电力大学,2014.
[5]张根周.大数据在智能电网领域的应用[J].电网与清洁能源,2016,32(6):114-117.ZHANG Genzhou.Applications of big data in the field of smart grid[J].Power Grid and Clean Energy,2016,32(6):114-117.
[6]占彤平.基于数据挖掘的客户用电行为分析研究与实践[J].电网技术,2014,38(S2):149-152.ZHAN Tongping.Analysis and practice of customer power consumption based on data mining[J].Power System Technology,2014,38(S2):149-152.
[7]BERTOLDI P,ATANASIU B.An in-depth analysis of the electricity end-use consumption and energy efficiency trends in the tertiary sector of the European Union[J].International Journal of Green Energy,2011,8(3):306-331.
[8]中国电机工程学会信息化专委会.中国电力大数据发展白皮书[R].2013.
[9]闫华光,陈宋宋,钟鸣,等.电力需求侧能效管理与需求响应系统的研究与设计[J].电网技术,2015,39(1):42-47.YAN Huaguang,CHEN Songsong,ZHONG Ming,et al.Research and design of demand side energy efficiency management and demand response system[J].Power System Technology,2015,39(1):42-47.
[10]崔强,王秀丽,王维洲.考虑风电消纳能力的高载能用户错峰峰谷电价研究[J].电网技术,2015,39(4):946-952.CUI Qiang,WANG Xiuli,WANG Weizhou.Stagger peak electricity price for heavy energy-consuming enterprises considering improvement of wind power accommodation[J].Power System Technology,2015,39(4):946-952.
[11]于娜,于乐征,李国庆.智能电网环境下基于多代理的商业用户可控负荷管理策略[J].电力系统自动化,2015,39(17):89-95.DOI:10.7500/AEPS20150331031.YU Na,YU Yuezheng,LI Guoqing.Controllable load management strategy for commercial users based on multiagent in smart grid environment[J].Automation of Electric Power Systems,2015,39(17):89-95.DOI:10.7500/AEPS20150331031.
[12]王守相,孙智卿,刘喆.面向智能用电的家庭能量协同调度策略[J].电力系统自动化,2015,40(17):108-113.DOI:10.7500/AEPS20140625016.WANG Shouxiang,SUN Zhiqing,LIU Zhe.Co-scheduling strategy of home energy for smart power utilization[J].Automation of Electric Power Systems,2015,40(17):108-113.DOI:10.7500/AEPS20140625016.
[13]李东东,崔龙龙,林顺富,等.家庭智能用电系统研究及智能控制器开发[J].电力系统保护与控制,2013,41(4):123-129.LI Dongdong,CUI Longlong,LIN Shunfu,et al.Study of smart power utilization system and development of smart controller for homes[J].Power System Protection and Control,2013,41(4):123-129.
[14]王璨,冯勤超.基于价值评价的电力用户聚类研究[J].价值工程,2009,28(5):64-67.WANG Can,FENG Qinchao.The research of power customers classification based on value assessment[J].Journal of Value Engineering,2009,28(5):64-67.
[15]李欣然,姜学皎,钱军,等.基于用户日负荷曲线的用电行业聚类与综合方法[J].电力系统自动化,2010,34(10):56-61.LI Xinran,JIANG Xuejiao,QIAN Jun,et al.A classifying and synthesizing method of power consumer industry based on the daily load profile[J].Automation of Electric Power Systems,2010,34(10):56-61.
[16]黄宇腾,侯芳,周勤,等.一种面向需求侧管理的用户负荷形态组合分析方法[J].电力系统保护与控制,2013,41(13):20-25.HUANG Yuteng,HOU Fang,ZHOU Qin,et al.A new combinational electrical load analysis method for demand side management[J].Power System Protection and Control,2013,41(13):20-25.
[17]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.
[18]张敞,王园园,赵裕啸,等.一种基于信息熵的聚类结果评价方法[J].合肥工业大学学报:自然科学版,2011,34(8):1251-1256.ZHANG Chang,WANG Yuanyuan,ZHAO Yuxiao,et al.A clustering results evaluation method based on information entropy[J].Journal of Hefei University of Technology:Natural Science Edition,2011,34(8):1251-1256.
[19]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.
[20]武亚昆,段富,尹雪梅.分类器准确率评估的研究[J].电脑开发与应用,2011,24(4):10-12.WU Yakun,DUAN Fu,YIN Xuemei.Research on accuracy evaluation of classifier[J].Computer Development and Application,2011,24(4):10-12.
[21]张惟皎,刘春煌,李芳玉.聚类质量的评价方法[J].计算机工程,2005,31(20):10-12.ZHANG Weijiao,LIU Chunhuang,LI Fangyu.Method of quality evaluation for clustering[J].Computer Engineering,2005,31(20):10-12.
[22]胡勇.聚类分析结果评价方法研究[D].包头:内蒙古科技大学,2014.