城市负荷空间分布的聚类群簇分析
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摘要
城市电力负荷的空间分布提供负荷大小及其空间位置,是配电网现状评价和空间负荷预测的基础和前提条件。提出了一种城市负荷空间分布的聚类群簇分析方法,基于Python爬虫技术利用百度地图收集规划区域用户开源信息,并采用正则匹配识别用户所在的建筑体及其属性,依据建筑体单位面积用电功率估计电力负荷,构建具有时间、空间和负荷功率的负荷空间分布样本集合。采用样本局部密度和样本间距两个指标进行中低压用户负荷的聚类,依据群簇属性计算得到负荷群簇的负荷中心坐标、局部负荷密度大小以及分布半径以分析负荷空间分布特征。针对某城市供电网格算例,对比分析群簇属性与规划数据的一致性,开展变电站配置的合理性分析,说明所提方法的准确有效性。
The spatial distribution of urban power load provides accurate data of load size and its spatial location, which is the basis and precondition for the evaluation of current distribution network and spatial load forecasting. A clustering analysis method for urban load spatial distribution is proposed. Open source information of users in planning areas on Baidu map based on Python crawler technology is collected and then the building where the users are located and their attributes is identified by regular matching function. Power load is estimated according to the power consumption per unit area of building body to construct a sample set of spatial distribution of power load in which the samples have three attributes: time, space and load power. Further, clustering analysis of medium and low voltage user load is performed based on the two indexes of sample local density and sample spacing and the coordinates of load center, the local load density and the distribution radius of load clusters are calculated on the basis of the clusters' attributes. Finally, taking a power grid of a city as an example, the consistency of the clusters' attribute indicators is compared with the planning data. Rational analysis of substation configuration is carried out and the accuracy and effectiveness of the proposed method is illustrated.
The spatial distribution of urban power load provides accurate data of load size and its spatial location, which is the basis and precondition for the evaluation of current distribution network and spatial load forecasting. A clustering analysis method for urban load spatial distribution is proposed. Open source information of users in planning areas on Baidu map based on Python crawler technology is collected and then the building where the users are located and their attributes is identified by regular matching function. Power load is estimated according to the power consumption per unit area of building body to construct a sample set of spatial distribution of power load in which the samples have three attributes: time, space and load power. Further, clustering analysis of medium and low voltage user load is performed based on the two indexes of sample local density and sample spacing and the coordinates of load center, the local load density and the distribution radius of load clusters are calculated on the basis of the clusters' attributes. Finally, taking a power grid of a city as an example, the consistency of the clusters' attribute indicators is compared with the planning data. Rational analysis of substation configuration is carried out and the accuracy and effectiveness of the proposed method is illustrated.
引文
[1] 肖白,周潮,穆钢.空间电力负荷预测方法综述与展望[J].中国电机工程学报,2013,33(25):78-92.XIAO Bai, ZHOU Chao, MU Gang. Review and prospect of the spatial load forecasting methods[J]. Proceedings of the CSEE, 2013, 33(25): 78-92.
[2] 余贻鑫,张崇见,张弘鹏.空间电力负荷预测小区用地分析(一)——模糊推理新方法和小区用地分析原理[J].电力系统自动化,2001,25(6):23-26.YU Yixin, ZHANG Chongjian, ZHANG Hongpeng. Spatial electric load forecasting district land branch (Ⅰ) small area land-use analysis infuzzy spatial load forecasting[J]. Automation of Electric Power Systems, 2001, 25(6): 23-26.
[3] 杨丽徙,王金风,陈根永,等.基于元胞自动机理论的电力负荷空间分布预测[J].中国电机工程学报,2007,27(4):15-20.YANG Lixi, WANG Jinfeng, CHEN Genyong, et al. Load spatial distribution forecasting model on cellular theory[J]. Proceedings of the CSEE, 2007, 27(4): 15-20.
[4] 刘自发,庞铖铖,魏建炜,等.基于IAHP和TOPSIS方法的负荷密度指标计算[J].电力系统自动化,2012,36(13):56-60.LIU Zifa, PANG Chengcheng, WEI Jianwei, et al. Index calculation of load density based on IAHP and TOPSIS methods[J]. Automation of Electric Power Systems, 2012, 36(13): 56-60.
[5] 朱凤娟,王主丁,陆俭,等.考虑小区发展不均衡的空间负荷预测分类分区法[J].电力系统自动化,2012,36(12):41-48.ZHU Fengjuan, WANG Zhuding, LU Jian, et al. Disequilibrium development areas based classification and subarea method for spatial load forecasting[J]. Automation of Electric Power Systems, 2012, 36(12): 41-48.
[6] CHOW M Y, ZHU Jinxiang, TRAM H. Application of fuzzy multi-objective decision making in spatial load forecasting[J]. IEEE Transactions on Power Systems, 1998, 13(13): 1185-1190.
[7] 肖白,穆钢,黎平,等.空间负荷预测中的负荷时序消差方法[J].电力系统自动化,2010,34(16):50-54.XIAO Bai, MU Gang, LI Ping, et al. A time series mismatch corrective method for spatial load forecasting[J]. Automation of Electric Power Systems, 2010, 34(16): 50-54.
[8] 刘思,傅旭华,叶承晋,等.考虑地域差异的配电网空间负荷聚类及一体化预测方法[J].电力系统自动化,2017,41(3):70-75. DOI: 10.7500/AEPS20160507003.LIU Si, FU Xuhua, YE Chengjin, et al. Spatial load clustering and integrated forecasting method of distribution network considering regional difference[J]. Automation of Electric Power Systems, 2017, 41(3): 70-75. DOI: 10.7500/AEPS20160507003.
[9] 肖白,黎平.城网空间电力负荷预测中的负荷规律性分析[J].电网技术,2009,33(20):113-118.XIAO Bai, LI Ping. Load regularity analysis on spatial load forecasting of urban power system[J]. Power System Technology, 2009, 33(20): 113-118.
[10] 肖白,黎平.最佳电力负荷空间分辨率的获取方法[J].中国电机工程学报,2010,30(34):50-56.XIAO Bai, LI Ping. Method for acquiring optimum spatial resolution of electric load[J]. Proceedings of the CSEE, 2010,30(34): 50-56.
[11] KWAC J, FLORA J, RAJAGOPAL R. Household energy consumption segmentation using hourly data[J]. IEEE Transactions on Smart Grid, 2014, 5(1): 420-430.
[12] 王继业,季知祥,史梦洁,等.智能配用电大数据需求分析与应用研究[J].中国电机工程学报,2015,35(8):1829-1836.WANG Jiye, JI Zhixiang, SHI Mengjie, et al. Scenario analysis and application research on big data in smart power distribution and consumption systems[J]. Proceedings of the CSEE, 2015, 35(8): 1829-1836.
[13] 赵岩,李磊,刘俊勇,等.上海电网需求侧负荷模式的组合识别模型[J].电网技术,2010,34(1):145-151.ZHAO Yan, LI Lei, LIU Junyong, et al. Combinational recognition model for demand side load profile in Shanghai power grid[J]. Power System Technology, 2010, 34(1): 145-151.
[14] 马瑞,周谢,彭舟,等.考虑气温因素的负荷特性统计指标关联特征数据挖掘[J].中国电机工程学报,2015,35(1):43-51.MA Rui, ZHOU Xie, PENG Zhou, et al. Data mining on correlation feature of load characteristics statistical indexes considering temperature[J]. Proceedings of the CSEE, 2015, 35(1): 43-51.
[15] WANG Yi, CHEN Qixin, KANG Chongqing, et al. Clustering of electricity consumption behavior dynamics toward big data applications[J]. IEEE Transactions on Smart Grid, 2016, 7(5): 2437-2447.
[16] 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.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.
[17] 王星华,陈卓优,彭显刚.一种基于双层聚类分析的负荷形态组合识别方法[J].电网技术,2016,40(5):1495-1501.WANG Xinghua, CHEN Zhuoyou, PENG Xiangang. A new combinational electrical load analysis method based on bilayer clustering analysis[J]. Power System Technology, 2016, 40(5): 1495-1501.
[18] 周湶,李健,孙才新,等.基于粗糙集和元胞自动机的配电网空间负荷预测[J].中国电机工程学报,2008,28(25):68-73.ZHOU Quan, LI Jian, SUN Caixin, et al. Spatial load forecasting for distribution networks based on rough sets and cellular automata[J]. Proceedings of the CSEE, 2008, 28(25): 68-73.
[19] 朱敏捷,王崇斌,王天智,等.基于多源数据的自适应电力负荷预测方法[J].水电能源科学,2017(12):200-203.ZHU Minjie, WANG Chongbin, WANG Tianzhi, et al. Adaptive power load forecasting method based on multi-source data[J]. Water Resources and Power, 2017(12): 200-203.
[20] 刘科研,盛万兴,张东霞,等.智能配电网大数据应用需求和场景分析研究[J].中国电机工程学报,2015,35(2):287-293.LIU Keyan, SHENG Wanxing, ZHANG Dongxia, et al. Big data application requirements and scenario analysis in smart distribution network[J]. Proceedings of the CSEE, 2015, 35(2): 287-293.
[21] DORR D. Data analytics and applications newsletter(DMD and TMD demonstrations)[EB/OL].[2018-02-14]. http://smartgrid.epri.com.
[22] BOXALL B. UCLA interactive map shows Los Angeles electricity use[EB/OL].[2018-02-14]. http://articles.latimes.com/2013/mar/29/science/la-sci-sn-ucla-electricity-los-angeles-20130329.
[23] LAMONICA M. Los Angeles maps electricity use at the block level[EB/OL]. [2013-02-14]. https://www.technologyreview.com/s/512991/los-angeles-maps-electricity-use-at-the-block-level/.
[24] 池娇,焦利民,董婷,等.基于POI数据的城市功能区定量识别及其可视化[J].测绘地理信息,2016,41(2):68-73.CHI Jiao, JIAO Limin, DONG Ting, et al. Quantitative identification and visualization of urban functional area based on POI data[J]. Journal of Geomatics, 2016, 41(2): 68-73.
[25] 叶晟之.基于POI数据分析的商务产业集聚区的分布特征及关联因素研究——以南京为例[J].建筑与文化,2017(6):152-155.YE Shengzhi. Distribution characteristics and connected factors of the agglomeration area of business industry based on POI in Nanjing[J]. Architecture & Culture, 2017(6): 152-155.
[26] 孙宗耀,翟秀娟,孙希华,等.基于POI数据的生活设施空间分布及配套情况研究——以济南市内五区为例[J].地理信息世界,2017,24(1):65-70.SUN Zongyao, ZHAI Xiujuan, SUN Xihua, et al. Study on spatial distribution and matching situation of living facilities based on POI—taking five districts of Ji'nan as a case[J]. Geomatics World, 2017, 24(1): 65-70.
[27] 浩飞龙,王士君,冯章献,等.基于POI数据的长春市商业空间格局及行业分布[J].地理研究,2018,37(2):366-378.HAO Feilong, WANG Shijun, FENG Zhangxian, et al. Spatial pattern and its industrial distribution of commercial space in Changchun based on POI data[J]. Geographical Research, 2018, 37(2): 366-378.
[28] SILVA M C, HORTA I M, LEAL V, et al. A spatially-explicit methodological framework based on neural networks to assess the effect of urban form on energy demand[J]. Applied Energy, 2017, 202:386-398.
[29] 苗春葆.点与多边形关系的射线法[J].电脑编程技巧与维护,2008(3):56-58.MIAO Chunbao. The ray method of judging the relationship between points and polygons[J]. Computer Programming Skills & Maintenance, 2008(3): 56-58.
[30] 王宇.城市商业综合体负荷密度指标分析[J].现代建筑电气,2014,5(5):31-37.WANG Yu. Analysis of power load density index in urban commercial complex[J]. Moder Architecture Electric, 2014, 5(5): 31-37.
[31] 李长海.普通住宅建筑负荷计算研究[J].电气应用,2013(2):77-85.LI Changhai. Research on load calculation of ordinary residential buildings[J]. Electrotechnical Application, 2013(2): 77-85.
[32] RODRIGUEZ A, LAIO A. Clustering by fast search and find of density peaks[J]. Science, 2014, 344(6191): 1492-1496.
[2] 余贻鑫,张崇见,张弘鹏.空间电力负荷预测小区用地分析(一)——模糊推理新方法和小区用地分析原理[J].电力系统自动化,2001,25(6):23-26.YU Yixin, ZHANG Chongjian, ZHANG Hongpeng. Spatial electric load forecasting district land branch (Ⅰ) small area land-use analysis infuzzy spatial load forecasting[J]. Automation of Electric Power Systems, 2001, 25(6): 23-26.
[3] 杨丽徙,王金风,陈根永,等.基于元胞自动机理论的电力负荷空间分布预测[J].中国电机工程学报,2007,27(4):15-20.YANG Lixi, WANG Jinfeng, CHEN Genyong, et al. Load spatial distribution forecasting model on cellular theory[J]. Proceedings of the CSEE, 2007, 27(4): 15-20.
[4] 刘自发,庞铖铖,魏建炜,等.基于IAHP和TOPSIS方法的负荷密度指标计算[J].电力系统自动化,2012,36(13):56-60.LIU Zifa, PANG Chengcheng, WEI Jianwei, et al. Index calculation of load density based on IAHP and TOPSIS methods[J]. Automation of Electric Power Systems, 2012, 36(13): 56-60.
[5] 朱凤娟,王主丁,陆俭,等.考虑小区发展不均衡的空间负荷预测分类分区法[J].电力系统自动化,2012,36(12):41-48.ZHU Fengjuan, WANG Zhuding, LU Jian, et al. Disequilibrium development areas based classification and subarea method for spatial load forecasting[J]. Automation of Electric Power Systems, 2012, 36(12): 41-48.
[6] CHOW M Y, ZHU Jinxiang, TRAM H. Application of fuzzy multi-objective decision making in spatial load forecasting[J]. IEEE Transactions on Power Systems, 1998, 13(13): 1185-1190.
[7] 肖白,穆钢,黎平,等.空间负荷预测中的负荷时序消差方法[J].电力系统自动化,2010,34(16):50-54.XIAO Bai, MU Gang, LI Ping, et al. A time series mismatch corrective method for spatial load forecasting[J]. Automation of Electric Power Systems, 2010, 34(16): 50-54.
[8] 刘思,傅旭华,叶承晋,等.考虑地域差异的配电网空间负荷聚类及一体化预测方法[J].电力系统自动化,2017,41(3):70-75. DOI: 10.7500/AEPS20160507003.LIU Si, FU Xuhua, YE Chengjin, et al. Spatial load clustering and integrated forecasting method of distribution network considering regional difference[J]. Automation of Electric Power Systems, 2017, 41(3): 70-75. DOI: 10.7500/AEPS20160507003.
[9] 肖白,黎平.城网空间电力负荷预测中的负荷规律性分析[J].电网技术,2009,33(20):113-118.XIAO Bai, LI Ping. Load regularity analysis on spatial load forecasting of urban power system[J]. Power System Technology, 2009, 33(20): 113-118.
[10] 肖白,黎平.最佳电力负荷空间分辨率的获取方法[J].中国电机工程学报,2010,30(34):50-56.XIAO Bai, LI Ping. Method for acquiring optimum spatial resolution of electric load[J]. Proceedings of the CSEE, 2010,30(34): 50-56.
[11] KWAC J, FLORA J, RAJAGOPAL R. Household energy consumption segmentation using hourly data[J]. IEEE Transactions on Smart Grid, 2014, 5(1): 420-430.
[12] 王继业,季知祥,史梦洁,等.智能配用电大数据需求分析与应用研究[J].中国电机工程学报,2015,35(8):1829-1836.WANG Jiye, JI Zhixiang, SHI Mengjie, et al. Scenario analysis and application research on big data in smart power distribution and consumption systems[J]. Proceedings of the CSEE, 2015, 35(8): 1829-1836.
[13] 赵岩,李磊,刘俊勇,等.上海电网需求侧负荷模式的组合识别模型[J].电网技术,2010,34(1):145-151.ZHAO Yan, LI Lei, LIU Junyong, et al. Combinational recognition model for demand side load profile in Shanghai power grid[J]. Power System Technology, 2010, 34(1): 145-151.
[14] 马瑞,周谢,彭舟,等.考虑气温因素的负荷特性统计指标关联特征数据挖掘[J].中国电机工程学报,2015,35(1):43-51.MA Rui, ZHOU Xie, PENG Zhou, et al. Data mining on correlation feature of load characteristics statistical indexes considering temperature[J]. Proceedings of the CSEE, 2015, 35(1): 43-51.
[15] WANG Yi, CHEN Qixin, KANG Chongqing, et al. Clustering of electricity consumption behavior dynamics toward big data applications[J]. IEEE Transactions on Smart Grid, 2016, 7(5): 2437-2447.
[16] 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.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.
[17] 王星华,陈卓优,彭显刚.一种基于双层聚类分析的负荷形态组合识别方法[J].电网技术,2016,40(5):1495-1501.WANG Xinghua, CHEN Zhuoyou, PENG Xiangang. A new combinational electrical load analysis method based on bilayer clustering analysis[J]. Power System Technology, 2016, 40(5): 1495-1501.
[18] 周湶,李健,孙才新,等.基于粗糙集和元胞自动机的配电网空间负荷预测[J].中国电机工程学报,2008,28(25):68-73.ZHOU Quan, LI Jian, SUN Caixin, et al. Spatial load forecasting for distribution networks based on rough sets and cellular automata[J]. Proceedings of the CSEE, 2008, 28(25): 68-73.
[19] 朱敏捷,王崇斌,王天智,等.基于多源数据的自适应电力负荷预测方法[J].水电能源科学,2017(12):200-203.ZHU Minjie, WANG Chongbin, WANG Tianzhi, et al. Adaptive power load forecasting method based on multi-source data[J]. Water Resources and Power, 2017(12): 200-203.
[20] 刘科研,盛万兴,张东霞,等.智能配电网大数据应用需求和场景分析研究[J].中国电机工程学报,2015,35(2):287-293.LIU Keyan, SHENG Wanxing, ZHANG Dongxia, et al. Big data application requirements and scenario analysis in smart distribution network[J]. Proceedings of the CSEE, 2015, 35(2): 287-293.
[21] DORR D. Data analytics and applications newsletter(DMD and TMD demonstrations)[EB/OL].[2018-02-14]. http://smartgrid.epri.com.
[22] BOXALL B. UCLA interactive map shows Los Angeles electricity use[EB/OL].[2018-02-14]. http://articles.latimes.com/2013/mar/29/science/la-sci-sn-ucla-electricity-los-angeles-20130329.
[23] LAMONICA M. Los Angeles maps electricity use at the block level[EB/OL]. [2013-02-14]. https://www.technologyreview.com/s/512991/los-angeles-maps-electricity-use-at-the-block-level/.
[24] 池娇,焦利民,董婷,等.基于POI数据的城市功能区定量识别及其可视化[J].测绘地理信息,2016,41(2):68-73.CHI Jiao, JIAO Limin, DONG Ting, et al. Quantitative identification and visualization of urban functional area based on POI data[J]. Journal of Geomatics, 2016, 41(2): 68-73.
[25] 叶晟之.基于POI数据分析的商务产业集聚区的分布特征及关联因素研究——以南京为例[J].建筑与文化,2017(6):152-155.YE Shengzhi. Distribution characteristics and connected factors of the agglomeration area of business industry based on POI in Nanjing[J]. Architecture & Culture, 2017(6): 152-155.
[26] 孙宗耀,翟秀娟,孙希华,等.基于POI数据的生活设施空间分布及配套情况研究——以济南市内五区为例[J].地理信息世界,2017,24(1):65-70.SUN Zongyao, ZHAI Xiujuan, SUN Xihua, et al. Study on spatial distribution and matching situation of living facilities based on POI—taking five districts of Ji'nan as a case[J]. Geomatics World, 2017, 24(1): 65-70.
[27] 浩飞龙,王士君,冯章献,等.基于POI数据的长春市商业空间格局及行业分布[J].地理研究,2018,37(2):366-378.HAO Feilong, WANG Shijun, FENG Zhangxian, et al. Spatial pattern and its industrial distribution of commercial space in Changchun based on POI data[J]. Geographical Research, 2018, 37(2): 366-378.
[28] SILVA M C, HORTA I M, LEAL V, et al. A spatially-explicit methodological framework based on neural networks to assess the effect of urban form on energy demand[J]. Applied Energy, 2017, 202:386-398.
[29] 苗春葆.点与多边形关系的射线法[J].电脑编程技巧与维护,2008(3):56-58.MIAO Chunbao. The ray method of judging the relationship between points and polygons[J]. Computer Programming Skills & Maintenance, 2008(3): 56-58.
[30] 王宇.城市商业综合体负荷密度指标分析[J].现代建筑电气,2014,5(5):31-37.WANG Yu. Analysis of power load density index in urban commercial complex[J]. Moder Architecture Electric, 2014, 5(5): 31-37.
[31] 李长海.普通住宅建筑负荷计算研究[J].电气应用,2013(2):77-85.LI Changhai. Research on load calculation of ordinary residential buildings[J]. Electrotechnical Application, 2013(2): 77-85.
[32] RODRIGUEZ A, LAIO A. Clustering by fast search and find of density peaks[J]. Science, 2014, 344(6191): 1492-1496.