Abstract
Energy theft refers to the intentional and illegal usage of electricity by various means. A number of studies have been conducted on energy theft detection in the advanced metering infrastructure using machine learning methods. However, applying machine learning for energy theft detection has a problem in that it is difficult to obtain enough electricity theft data to train a machine learning model. In this paper, we propose a method based on anomaly pattern detection to detect electricity theft in data streams generated from smart meters. The proposed method requires only normal energy consumption data to train the model. Previous usage records of customers being monitored are not needed for energy theft detection. This characteristic makes the proposed method applicable in real situations. Experiments were conducted using real smart meter data and artificial attack data, including the preprocessing of daily consumption vectors by standard normalization, the construction of an outlier detection model on normal electricity consumption data of randomly chosen customers, and the application of anomaly pattern detection on test data streams. Some promising results were obtained, notably, that attacks of types 4, 5, 6 were detected with an average F1 value of 0.93 and average delay of 19 days.
Highlights
As power consumption increases each year, the power generation and distribution industry grows in size, and the need for technologies to reduce power loss is increasing
The types of power losses are generally classified into technical losses (TLs) and non-technical losses (NTLs) [1]
TLs occur in the process of transmission, transformation and measurement of electricity, while NTLs are mainly caused by theft, fraud or billing problems [2]
Summary
As power consumption increases each year, the power generation and distribution industry grows in size, and the need for technologies to reduce power loss is increasing. The types of power losses are generally classified into technical losses (TLs) and non-technical losses (NTLs) [1]. TLs occur in the process of transmission, transformation and measurement of electricity, while NTLs are mainly caused by theft, fraud or billing problems [2]. The advanced metering infrastructure (AMI) is a central part of the smart grid system, and provides the communication and control functions needed to implement critical energy management services [3]. It creates the opportunity for a new type of energy theft. Electricity theft can be accomplished by physical tampering such as unjustified changes to the meter, or by electrically tampering with the metered data in the smart meter network [4,5]
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