Electricity Consumption Data Analysis Using Various Outlier Detection Methods

Electricity Consumption Data Analysis Using Various Outlier Detection Methods

Sidi Mohammed Kaddour, Mohamed Lehsaini
Copyright: © 2021 |Pages: 16
DOI: 10.4018/IJSSCI.2021070102
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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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Anomaly detection (AKA: outlier detection), is the process of detecting infrequent patterns in a given dataset that do not conform to the expected behavior (Prasad, et al., 2009). This technique is deployed in many fields of applications such as health care, fraud detection, fault detection and many others.

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