Electricity Consumption Data Analysis Using Various Outlier Detection Methods

Sidi Mohammed Kaddour, M. Lehsaini
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引用次数: 6

Abstract

Nowadays, detecting abnormal power consumption behavior of householders has become a big concern in the smart energy field; overcoming this limitation will help in identifying efficient solutions to reduce power consumption. This paper proposes a new methodology for detecting abnormal energy consumption in residential buildings based on hourly readings of energy consumption and peak energy consumption. The proposition is implemented using three unsupervised outlier detection methods (isolation forest, one-class SVM, and k-means). The authors propose this solution to help residents in reducing operating costs by detecting consumption failures that cannot be detected easily. On the other hand, energy providers will have the access to detailed data about anomalies, faulty appliances, and houses with poor power control strategy in general, which will help in pinpointing overconsumption problems, thus enhancing human awareness and reducing energy consumption.
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利用各种离群值检测方法分析用电量数据
当前,用户异常用电行为的检测已成为智能能源领域关注的热点;克服这一限制将有助于找到有效的解决方案来降低功耗。本文提出了一种基于每小时能耗和峰值能耗读数的住宅建筑能耗异常检测方法。该命题使用三种无监督离群值检测方法(隔离森林,一类支持向量机和k-means)实现。作者提出了这个解决方案,通过检测难以检测的消费故障,帮助居民降低运营成本。另一方面,能源供应商将获得有关异常、故障电器和一般电力控制策略较差的房屋的详细数据,这将有助于查明过度消费问题,从而提高人们的意识并减少能源消耗。
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