用两种数据挖掘方法联合检测智能电表窃电，效果优于传统方法。
A Novel Combined Data-Driven Approach for Electricity Theft Detection
- 结合MIC与聚类算法，挖掘用电行为异常关联性。
- 在爱尔兰数据集上准确率超90%，可识别形状正常的窃电行为。
- 适合电力公司反窃电系统部署，无需标签数据或额外信息。
能源互联网中信息与能量双向流动是重要特征。数据挖掘技术在信息流中可用于解决实际问题。随着篡改智能电表的窃电行为日益增多，其异常模式愈发多样且难以识别。现有方法或依赖标注数据、或需难以获取的系统信息，或检测精度不足。本文提出一种新方法，融合最大信息系数（MIC）与密度峰值快速搜索聚类（CFSFDP）技术。MIC可发现非技术性损耗（NTL）与用户用电行为间的关联，精准识别外观正常的窃电；CFSFDP能从数千条负荷曲线中定位异常用户，适用于任意形态的窃电检测。通过构建两技术协同框架，在爱尔兰智能电表数据集上的实验表明该方法具有优异性能。
原文摘要 · Abstract (English)
The two-way flow of information and energy is an important feature of the Energy Internet. Data analytics is a powerful tool in the information flow that aims to solve practical problems using data mining techniques. As the problem of electricity thefts via tampering with smart meters continues to increase, the abnormal behaviors of thefts become more diversified and more difficult to detect. Thus, a data analytics method for detecting various types of electricity thefts is required. However, the existing methods either require a labeled dataset or additional system information which is difficult to obtain in reality or have poor detection accuracy. In this paper, we combine two novel data mining techniques to solve the problem. One technique is the Maximum Information Coefficient (MIC), which can find the correlations between the non-technical loss (NTL) and a certain electricity behavior of the consumer. MIC can be used to precisely detect thefts that appear normal in shapes. The other technique is the clustering technique by fast search and find of density peaks (CFSFDP). CFSFDP finds the abnormal users among thousands of load profiles, making it quite suitable for detecting electricity thefts with arbitrary shapes. Next, a framework for combining the advantages of the two techniques is proposed. Numerical experiments on the Irish smart meter dataset are conducted to show the good performance of the combined method.
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