scholarly journals Energy Theft Detection in Advanced Metering Infrastructure Based on Anomaly Pattern Detection

Energies ◽  
2020 ◽  
Vol 13 (15) ◽  
pp. 3832 ◽  
Author(s):  
Cheong Hee Park ◽  
Taegong Kim
Keyword(s):  
Machine Learning ◽  
Data Streams ◽  
Pattern Detection ◽  
Advanced Metering Infrastructure ◽  
Daily Consumption ◽  
Electricity Theft ◽  
Consumption Data ◽  
Anomaly Pattern ◽  
Advanced Metering ◽  
Energy Theft

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.

scholarly journals Towards Big Data Electricity Theft Detection Based on Improved RUSBoost Classifiers in Smart Grid

Energies ◽  
2021 ◽  
Vol 14 (23) ◽  
pp. 8029
Author(s):  
Rehan Akram ◽  
Nasir Ayub ◽  
Imran Khan ◽  
Fahad R. Albogamy ◽  
Gul Rukh ◽  
...  
Keyword(s):  
Electric Power ◽  
Machine Learning Algorithms ◽  
Primary Sources ◽  
Space Technology ◽  
Electricity Theft ◽  
Bird Swarm Algorithm ◽  
Grave Problem ◽  
Advanced Metering ◽  
Energy Theft ◽  
Different Sources

The advent of the new millennium, with the promises of the digital age and space technology, favors humankind in every perspective. The technology provides us with electric power and has infinite use in multiple electronic accessories. The electric power produced by different sources is distributed to consumers by the transmission line and grid stations. During the electric transmission from primary sources, there are various methods by which to commit energy theft. Energy theft is a universal electric problem in many countries, with a possible loss of billions of dollars for electric companies. This energy contention is deep rooted, having so many root causes and rugged solutions of a technical nature. Advanced Metering Infrastructure (AMI) is introduced with no adequate results to control and minimize electric theft. Until now, so many techniques have been applied to overcome this grave problem of electric power theft. Many researchers nowadays use machine learning algorithms, trying to combat this problem, giving better results than previous approaches. Random Forest (RF) classifier gave overwhelmingly good results with high accuracy. In our proposed solution, we use a novel Convolution Neural Network (CNN) with RUSBoost Manta Ray Foraging Optimization (rus-MRFO) and RUSBoost Bird Swarm Algorithm (rus-BSA) models, which proves to be very innovative. The accuracy of our proposed approaches, rus-MRFO and rus-BSA, are 91.5% and a 93.5%, respectively. The proposed techniques have shown promising results and have strong potential to be applied in future.

scholarly journals Anomaly Pattern Detection in Streaming Data Based on the Transformation to Multiple Binary-Valued Data Streams

2021 ◽  
Vol 12 (1) ◽  
pp. 19-27
Author(s):  
Taegong Kim ◽  
Cheong Hee Park
Keyword(s):  
Outlier Detection ◽  
Data Streams ◽  
Data Stream ◽  
Detection Method ◽  
Binary Classification ◽  
Streaming Data ◽  
Pattern Detection ◽  
Detection Methods ◽  
Anomaly Pattern ◽  
Isolation Forest

Abstract Anomaly pattern detection in a data stream aims to detect a time point where outliers begin to occur abnormally. Recently, a method for anomaly pattern detection has been proposed based on binary classification for outliers and statistical tests in the data stream of binary labels of normal or an outlier. It showed that an anomaly pattern can be detected accurately even when outlier detection performance is relatively low. However, since the anomaly pattern detection method is based on the binary classification for outliers, most well-known outlier detection methods, with the output of real-valued outlier scores, can not be used directly. In this paper, we propose an anomaly pattern detection method in a data stream using the transformation to multiple binary-valued data streams from real-valued outlier scores. By using three outlier detection methods, Isolation Forest(IF), Autoencoder-based outlier detection, and Local outlier factor(LOF), the proposed anomaly pattern detection method is tested using artificial and real data sets. The experimental results show that anomaly pattern detection using Isolation Forest gives the best performance.

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