Journal of Shandong University(Engineering Science) ›› 2019, Vol. 49 ›› Issue (5): 29-36.doi: 10.6040/j.issn.1672-3961.0.2019.116

• Engineering—Special Topic on Artificial Intelligence Application • Previous Articles     Next Articles

None-consumption users filtering algorithm based on decision tree and data-driven methods

Bo ZHANG1(),Feng LU2,Hanyu DONG2,Qingtai CHEN3,Zhenzhi LIN1,4,*(),Hongtao WANG4   

  1. 1. School of Electrical Engineering, Zhejiang University, Hangzhou 310027, Zhejiang, China
    2. Huzhou Power Supply Company, State Gird Zhejiang Electric Power Company, Huzhou 313000, Zhejiang, China
    3. Zhejiang Huayun Information Technology Co., Ltd., Hangzhou 310012, Zhejiang, China
    4. School of Electrical Engineering, Shandong University, Jinan 250061, Shandong, China
  • Received:2019-03-25 Online:2019-10-20 Published:2019-10-18
  • Contact: Zhenzhi LIN E-mail:zhbzju@163.com;linzhenzhi@zju.edu.cn
  • Supported by:
    国家重点研发计划(2016YFB0901100);国家自然科学基金资助项目(51777185)

RichHTML

8

PDF (PC)

287

Abstract:

With the increasing of power consumers and diversification of power consumption in power systems, the number of none-consumption users (NCUs) was also increasing rapidly. Thus, lots of manpower and material resources of power supply companies were arranged to perform troubleshooting on NCUs. Given this background, a data-driven method based on the electricity information of NCUs collected by electricity information acquisition system (EIAS) was proposed to determine the filtering results of normal NCUs and abnormal NCUs. The decision tree was utilized to analyze the electricity data of NCUs, and determine the types of NCUs. The key factors suitable for NCUs filtering were determined based on the original data to filter the NCUs that could not be screened by the decision tree, and the evaluation system for NCUs filtering was constructed. On this basis, CRITIC and radar chart methods were adopted to determine the weights of the key factors and to determine the filtering results of NCUs, respectively. The NCUs power-supplied by an actual power supply station in Zhejiang Province were served for demonstrating the proposed algorithm of NCUs filtering, and the simulation and on-site inspection results showed that the proposed data-driven method was effective for screening out the abnormal NCUs.

Key words: none-consumption user, troubleshoot priority, decision tree, CRITIC method, radar chart method

CLC Number: 

  • TM933.4

Fig.1

Preliminary screening of NCU types based ondecision tree"

Fig.2

Radar chart of the kth NCU"

Table 1

Key factors and filtering results for part of NCUs"

户号 居民类型 合同容量/(kVA) 电压等级/V 信用等级 异常事件/件 环比电量/(kW·h) 同比电量/(kW·h) 离散系数/(p.u.) 台区线损变化率/(p.u.) 评分/(p.u.)
****050083 城镇居民 4 380 最高信用等级 4 0.00 2 808.39 0.92 0.02 57.61
****051024 城镇居民 45 380 最高信用等级 4 119.00 13 234.20 0.94 0.11 67.12
****019099 乡村居民 6 220 - 4 0.00 899.79 1.44 0.14 39.15
****020500 乡村居民 6 220 - 4 4.85 394.30 1.84 0.13 39.15
****021224 乡村居民 6 220 - 4 0.00 393.31 1.09 0.02 38.80
… … … … … … … … … … …
****063612 城镇居民 10 380 - 4 0.00 598.80 1.24 0.05 96.55
****064572 乡村居民 8 220 - 4 0.00 982.82 1.27 0.17 39.38
****064986 城镇居民 20 380 - 4 0.00 681.71 1.33 0.07 97.95
****064987 城镇居民 20 380 - 4 0.00 7 197.39 0.84 0.01 100.00

Fig.3

Weights of key factors of troubleshootingpriority for NCUs"

Fig.4

The radar chart of the highest and lowest rated NCUs"

Fig.5

The daily electricity consumption curve of the highest and lowest rated NCUs"

1 FANG Xi , MISRA Satyajayant , XUE Guoliang , et al. Smart grid—the new and improved power grid: a survey[J]. IEEE Communications Surveys & Tutorials, 2012, 14 (4): 944- 980.
2 宋亚奇, 周国亮, 朱永利. 智能电网大数据处理技术现状与挑战[J]. 电网技术, 2013, 37 (4): 927- 935.
SONG Yaqi , ZHOU Guoliang , ZHU Yongli . Present status and challenges of big data processing in smart grid[J]. Power System Technology, 2013, 37 (4): 927- 935.
3 YANG Hongming , ZHANG Jun , QIU Jing , et al. A practical pricing approach to smart grid demand response based on load classification[J]. IEEE Transactions on Smart Grid, 2018, 9 (1): 179- 190.
doi: 10.1109/TSG.2016.2547883
4 李海石, 徐向艺, 张磊. "一带一路"背景下全球能源互联网运行机制构建[J]. 山东大学学报(工学版), 2017, 47 (6): 134- 142.
LI Haishi , XU Xiangyi , ZHANG Lei . Operating mechanism establishment of the global energy interconnection under "the Belt and Road Initiative" situation[J]. Journal of Shandong University (Engineering Science), 2017, 47 (6): 134- 142.
5 LUAN Wenpeng, PENG Joshua, MARAS Mirjana, et al. Distribution network topology error correction using smart meter data analytics[C]//2013 IEEE Power & Energy Society General Meeting. Ottawa, Canada: IEEE, 2013: 1-5.
6 WU Jinsong , GUO Song , LI Jie , et al. Big data meet green challenges: big data toward green applications[J]. IEEE Systems Journal, 2016, 10 (3): 888- 900.
doi: 10.1109/JSYST.2016.2550530
7 丁杰, 奚后玮, 韩海韵, 等. 面向智能电网的数据密集型云存储策略[J]. 电力系统自动化, 2012, 36 (12): 66- 70.
DING Jie , XI Houwei , HAN Haiyun , et al. A smart grid-oriented data placement strategy for data-intensive cloud environment[J]. Automation of Electric Power Systems, 2012, 36 (12): 66- 70.
8 刘洋, 刘博, 王峰. 基于Parameter Server框架的大数据挖掘优化算法[J]. 山东大学学报(工学版), 2017, 47 (4): 1- 6.
LIU Yang , LIU Bo , WANG Feng . Optimization algorithm for big data mining based on parameter server framework[J]. Journal of Shandong University (Engineering Science), 2017, 47 (4): 1- 6.
9 王德文, 孙志伟. 电力用户侧大数据分析与并行负荷预测[J]. 中国电机工程学报, 2015, 35 (3): 527- 537.
WANG Dewen , SUN Zhiwei . Big data analysis and parallel load forecasting of electric power user side[J]. Proceedings of the CSEE, 2015, 35 (3): 527- 537.
10 黄小庆, 陈颉, 陈永新, 等. 大数据背景下的充电站负荷预测方法[J]. 电力系统自动化, 2016, 40 (12): 68- 74.
doi: 10.7500/AEPS20160323001
HUANG Xiaoqing , CHEN Jie , CHEN Yongxin , et al. Load forecasting method for electric vehicle charging station based on big data[J]. Automation of Electric Power Systems, 2016, 40 (12): 68- 74.
doi: 10.7500/AEPS20160323001
11 ZHANG Pei , WU Xiaoyu , WANG Xiaojun , et al. Short-term load forecasting based on big data technologies[J]. CSEE Journal of Power and Energy Systems, 2015, 1 (3): 59- 67.
doi: 10.17775/CSEEJPES.2015.00036
12 WANG Kun , LI Huining , FENG Yixiong , et al. Big data analytics for system stability evaluation strategy in the energy internet[J]. IEEE Transactions on Industrial Informatics, 2017, 13 (4): 1969- 1978.
doi: 10.1109/TII.2017.2692775
13 刘羽霄, 张宁, 康重庆. 数据驱动的电力网络分析与优化研究综述[J]. 电力系统自动化, 2018, 42 (6): 157- 167.
LIU Yuxiao , ZHANG Ning , KANG Chongqing . A review on data-driven analysis and optimization of power grid[J]. Automation of Electric Power Systems, 2018, 42 (6): 157- 167.
14 马纪, 刘希喆. 基于G2-熵权法的低压配网台区状态特性评估[J]. 电力自动化设备, 2017, 37 (1): 41- 46.
MA Ji , LIU Xizhe . Conditional characteristic evaluation based on G2-entropy weight method for low-voltage distribution network[J]. Electric Power Automation Equipment, 2017, 37 (1): 41- 46.
15 TSANG S , KAO B , YIP K Y , et al. Decision trees for uncertain data[J]. IEEE Transactions on Knowledge and Data Engineering, 2011, 23 (1): 64- 78.
doi: 10.1109/TKDE.2009.175
16 张小峰, 张志旺, 逄珊. 基于通信系统的决策树构造算法[J]. 山东大学学报(工学版), 2011, 41 (4): 79- 84.
ZHANG Xiaofeng , ZHANG Zhiwang , PANG Shan . Algorithm based on a communication system for constructing a decision tree[J]. Journal of Shandong University (Engineering Science), 2011, 41 (4): 79- 84.
17 MURPHY O J , MCCRAW R L . Designing storage efficient decision trees[J]. IEEE Transactions on Computers, 1991, 40 (3): 315- 320.
doi: 10.1109/12.76408
18 朱凌, 林冠强, 叶晓君, 等. 联合采用客观赋权和夹角度量法的电力系统关键线路辨识[J]. 电力建设, 2018, 39 (3): 49- 56.
doi: 10.3969/j.issn.1000-7229.2018.03.006
ZHU Ling , LIN Guanqiang , YE Xiaojun , et al. Critical line identification in power systems by combined use of objective weighting and inter-degree measure methods[J]. Electric Power Construction, 2018, 39 (3): 49- 56.
doi: 10.3969/j.issn.1000-7229.2018.03.006
19 LIN Zhenzhi , WEN Fushuan , WANG Huifang , et al. CRITIC-based node importance evaluation in skeleton-network reconfiguration of power grids[J]. IEEE Transactions on Circuits and Systems Ⅱ: Express Briefs, 2018, 65 (2): 206- 210.
doi: 10.1109/TCSII.2017.2703989
20 韩畅, 林振智, 杨莉, 等. 台风条件下区域电力系统的重要线路多维度辨识[J]. 电力系统自动化, 2018, 42 (15): 118- 125.
doi: 10.7500/AEPS20170715001
HAN Chang , LIN Zhenzhi , YANG Li , et al. Multi-dimensional critical line identification for regional power system under typhoon[J]. Automation of Electric Power Systems, 2018, 42 (15): 118- 125.
doi: 10.7500/AEPS20170715001
[1] SONG Dongmei, LIU Chunxiao, SHEN Chen, SHI Xuefa, ZANG Lin, FENG Wenqiang. Multiple objective and attribute decision making based on the subjective and objective weighting [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2015, 45(4): 1-9.
[2] PAN Pan1, WANG Xi-zhao2, ZHAI Jun-hai2. An improved induction algorithm based on ordinal decision tree [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2014, 44(1): 41-44.
[3] XU Chunyao1,2, CHEN Mingzhi3*, YU Lun1. A proactive recommendation model  adapted to users′ changing requirements [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2013, 43(3): 1-6.
[4] ZHANG Xiao-feng, ZHANG Zhi-wang, PANG Shan. Algorithm based on communication system for constructing decision tree [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2011, 41(4): 79-84.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
[1] SUN Dianzhu, ZHU Changzhi, LI Yanrui. [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2009, 39(1): 84 -86 .
[2] . [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2009, 39(1): 92 -95 .
[3] XIA Bin,ZHANG Lian-jun . Energy comparison-based TOA estimation algorithm for the DS-CDMA UWB system[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2007, 37(1): 70 -73 .
[4] XU Xiaodan, DUAN Zhengjie, CHEN Zhongyu. The sentiment mining method based on extended sentiment dictionary and integrated features[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2014, 44(6): 15 -18 .
[5] DENG Bin,WANG Jiang . Estimating parameters of a neuron model based on chaos synchronization and adaptive control[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2007, 37(5): 19 -23 .
[6] SHI Wen-Hua, LIU Wei-Dong, SUN Yong-Fu. Research of 1／3 dam breach simulation and personnel evacuation scenario based on digital elevation model DEM in a quake lake[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2009, 39(5): 144 -148 .
[7] CHEN Peng, HU Wen-Rong, FEI Hai-Yan. Screening of a denitrifying bacterium strain LZ-14 and its nitrogen removal characteristics[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2009, 39(5): 133 -138 .
[8] WANG Bai-wei,CAO Sheng-le . A mult-objective assessment method of the effects of industrial waste-water management[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2007, 37(3): 89 -92 .
[9] WANG Kai,SUN Feng-zhong,ZHAO Yuan-bin,GAO Ming,GAO Shan . Mathematical model and numerical simulation of the air inlet flowfield of a natural-draft cooling tower[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2008, 38(1): 13 -17 .
[10] ZHOU Xin-hong,PENG Yu-hua,LIU Yong,QU Huai-jing . Image retrieval based on multi-scale analysis and SVM relevance feedback[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2008, 38(2): 45 -50 .
Copyright 2018 © Editorial office of Journal of Shandong University (Engineering Science)
Supported by Beijing Magtech Co.Ltd