摘要
随着能源互联网的持续推进,电力系统的信息化程度不断提高,用户侧电量数据迅速增长,为开展基于大数据分析技术的用户用能特征检测提供了数据基础。针对传统的用户异常用电模式检测模型存在投入高、效率低的问题,提出了包含数据清洗特征筛选模型训练的用户异常用电全周期检测模型。为了综合考虑用户异常用电模式的影响因素,建立了包含负荷曲线斜率指标、线损指标和告警类指标的评估指标体系;并对初始数据进行数据清洗及缺失值处理以提高用户异常用电模式检测的精确度,然后使用极端梯度提升树(extreme gradient boosting,XGBoost)进行异常检测。最后,通过算例验证了检测模型的有效性,并通过与决策树、随机森林及Adaboost的对比分析,得出了XGBoost在用户异常用电模式检测中以较短的训练时间获得了较高的检测精度的结论。
With the continuous advancement of the energy Internet,the degree of informatization of the power system has been constantly improved and the amount of electricity data on the end-user side has been growing rapidly.The change provides a data foundation for the detection of user energy consumption based on the big data analysis technology.To deal with the high-cost and low-efficiency problem of the traditional detection model for abnormal electricity consumption patterns,a full cycle detection model of abnormal electricity consumption is proposed,which includes data cleaning,feature screening and model training.Besides,for comprehensively considering the factors affecting the abnormal electricity consumption patterns,the evaluation index system including the power consumption slope index,the line-loss index,and the warning information index is built.The data cleaning and missed value preprocess are conducted on the initial data to improve the accuracy of abnormal electricity pattern detection,and XGBoost is used for abnormal detection.Finally,a numerical case is used to verify the availability of the proposed detection method.In terms of the detection accuracy and training time,the detection performance of XGBoost algorithm is the best by comparing it with decision tree,random forest and Adaboost.
作者
户艳琴
李海明
刘念
傅皆恺
黄天翔
李承霖
李珂舟
胡志强
范志夫
邬小可
HU Yanqin;LI Haiming;LIU Nian;FU Jiekai;HUANG Tianxiang;LI Chenglin;LI Kezhou;HU Zhiqiang;FAN Zhifu;WU Xiaoke(State Grid Jiangxi Integrated Energy Services Co.,Ltd.,Nanchang 330096,China;State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources(North China Electric Power University),Beijing 102206,China;State Grid Jiangxi Electric Power Co.,Ltd.,Nanchang 330077,China;State Grid Jiangxi Electric Power Co.,Ltd.Yingtan Power Supply Branch,Yingtan 335000,Jiangxi Province,China)
出处
《电力建设》
CSCD
北大核心
2021年第10期19-27,共9页
Electric Power Construction
基金
国网江西省电力有限公司科技项目“基于配用电大数据能效提升关键技术研究”(521855200004)。
关键词
异常用电模式
XGBoost
评价指标体系
检测模型
能源互联网
abnormal electricity consumption patterns
XGBoost
evaluation index system
detection model
energy Internet
作者简介
户艳琴(1984),女,高级工程师,主要研究方向为反窃电、综合能源;李海明(1994),男,硕士研究生,主要研究方向为综合能源系统、负荷预测;通信作者:刘念(1981),男,博士,教授,博士生导师,主要研究方向为智能配用电、综合能源系统、信息物理系统等,E-mail:nianliu@ncepu.edu.cn;傅皆恺(1991),男,工程师,主要研究方向为综合能源、配电网、反窃电;黄天翔(1991),男,硕士,主要研究方向为电力系统分析、综合能源业务;李承霖(1991),男,工程师,主要研究方向为反窃电、综合能源;李珂舟(1982),男,高级工程师,主要研究方向为电气工程、电力通讯工程;胡志强(1982),男,高级工程师,主要研究方向为电能计量、线损治理、反窃电等;范志夫(1984),男,硕士,主要研究方向为电能计量、用电信息采集等;邬小可(1980),男,高级工程师,主要研究方向为电力营销智能用电。
