基于Mean Shift数据采掘知识发现过程的住宅用户高效电力盗窃检测
Efficient Power Theft Detection for Residential Consumers Using Mean Shift Data Mining Knowledge Discovery Process
摘要：
能源盗窃对电力运营商来说是一个非常重要的问题。由于检查方法不足，试图发现和减少非技术性损失是一项具有挑战性的任务。随着智能电网中先进计量基础设施（AMI）的发展，出现了更复杂的能源盗窃现状，许多新技术正在被采用来解决这个问题。为了识别非法居民用户，提出了一种基于数据采掘技术分析和识别居民用户用电模式的计算方法。结合主成分分析（PCA）和均值漂移算法的不同的电力盗窃的情况下，我们现在可以应付的电力盗窃检测问题。总体研究在住宅消费者电力盗窃检测方面取得了令人鼓舞的结果，这将帮助公用事业公司提高电网的可靠性、安全性和运营。
英文摘要：
Energy theft constitutes an issue of great importance for electricity operators. The attempt to detect and reduce non-technical losses is a challenging task due to insufficient inspection methods. With the evolution of advanced metering infrastructure (AMI) in smart grids, a more complicated status quo in energy theft has emerged and many new technologies are being adopted to solve the problem. In order to identify illegal residential consumers, a computational method of analyzing and identifying electricity consumption patterns of consumers based on data mining techniques has been presented. Combining principal component analysis (PCA) with mean shift algorithm for different power theft scenarios, we can now cope with the power theft detection problem sufficiently. The overall research has shown encouraging results in residential consumers power theft detection that will help utilities to improve the reliability, security and operation of power network.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
