Electricity Theft Detection in AMI Using Customers’ Consumption Patterns

By Paria Jokar, Nasim Arianpoo, Victor C. M. Leung. IEEE Transactions on Smart Grid, 2016

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Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges (2018, methodology): “To train a theft detection classifier, the non-technical loss was first estimated in [26].”
Decision Tree and SVM-Based Data Analytics for Theft Detection in Smart Grid (2016, methodology): “[18] used SVM for detecting various types of anomalies in the electricity consumption.”
Wide and Deep Convolutional Neural Networks for Electricity-Theft Detection to Secure Smart Grids (2018, methodology): “However, it is challenging to analyze the periodicity of the electricity consumption data due to the following reasons: 1) the electricity consumption data is often erroneous and noisy; 2) it is difficult to analyze the periodicity of the electricity consumption data since it is 1-D time series data”
Energy Theft Detection Using Gradient Boosting Theft Detector With Feature Engineering-Based Preprocessing (2019, methodology): “Our approach was inspired by existing research [1] on AMI theft detection via user pattern recognition, which uses a consumption pattern-based electricity theft detector (CPBETD) as the main classiﬁer algorithm.”
A Survey on the Detection Algorithms for False Data Injection Attacks in Smart Grids (2020, background): “Both cases result in reduced electrical bill and increased unauthorized proﬁts [33], [34].”