| 引用本文: | 陈 钢,李德英,陈希祥.基于改进XGBoost模型的低误报率窃电检测方法[J].电力系统保护与控制,2021,49(23):178-186.[点击复制] |
| CHEN Gang,LI Deying,CHEN Xixiang.Detection method of electricity theft with low false alarm rate based on an XGBoost model[J].Power System Protection and Control,2021,49(23):178-186[点击复制] |
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| 摘要: |
| 以降低窃电检测误报率为目标，提出一种基于贝叶斯优化和改进XGBoost模型的窃电检测方法。首先根据用电信息采集系统和营销系统数据构建了窃电检测指标，然后采用XGBoost模型作为分类器，添加Focal Loss和增加分类阈值的方式用于优化模型。在此基础上，以验证集的马修相关系数为目标函数，利用贝叶斯优化调参求出最优Focal Loss参数和分类阈值，进一步降低检测方法误判率。基于实际电力用户数据进行数值仿真，结果表明所提方法比Adaboost、BP神经网络、SVM具有更高的准确率。 |
| 关键词: 低误报率 XGBoost Focal Loss 窃电检测 |
| DOI：DOI: 10.19783/j.cnki.pspc.210094 |
| 投稿时间：2021-01-23修订日期：2021-06-16 |
| 基金项目:湖南省教育厅科学研究重点项目资助(19A349); 湖南省教育厅科学研究优秀青年项目资助(19B396) |
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| Detection method of electricity theft with low false alarm rate based on an XGBoost model |
| CHEN Gang,LI Deying,CHEN Xixiang |
| (School of Electronic Science and Engineering, Hunan University of Information Technology, Changsha 410151, China) |
| Abstract: |
| To help reduce the false alarm rate of electricity theft detection a method of detection based on Bayesian optimization and an improved XGBoost model is proposed. First, a detection index of electric theft is constructed based on the data of electric energy data acquisition and marketing systems, and then the XGBoost model is used as a classifier, and the Focal Loss and increased classification threshold are used to optimize the mode. The MCC of the validation set is used as the objective function, and the optimal Focal Loss parameters and classification threshold are found using Bayesian optimization to adjust the parameters, which further reduces the false alarm rate. The detection method misclassification rate is reduced further. Numerical simulation is carried out based on actual power user data,. The results show that the proposed method has higher accuracy than Adaboost, a BP neural network and SVM.
This work is supported by the Science Research Key Project of Hunan Education Department (No. 19A349) and the Science Research Outstanding Youth Project of Hunan Education Department (No. 19B396). |
| Key words: low false positive rate XGBoost Focal Loss electricity theft detection |
