Journal ArticleDOI
Electricity Theft Detection in AMI Using Customers’ Consumption Patterns
TLDR
A novel consumption pattern-based energy theft detector, which leverages the predictability property of customers' normal and malicious consumption patterns, and provides a high and adjustable performance with a low-sampling rate.Abstract:
As one of the key components of the smart grid, advanced metering infrastructure brings many potential advantages such as load management and demand response. However, computerizing the metering system also introduces numerous new vectors for energy theft. In this paper, we present a novel consumption pattern-based energy theft detector, which leverages the predictability property of customers’ normal and malicious consumption patterns. Using distribution transformer meters, areas with a high probability of energy theft are short listed, and by monitoring abnormalities in consumption patterns, suspicious customers are identified. Application of appropriate classification and clustering techniques, as well as concurrent use of transformer meters and anomaly detectors, make the algorithm robust against nonmalicious changes in usage pattern, and provide a high and adjustable performance with a low-sampling rate. Therefore, the proposed method does not invade customers’ privacy. Extensive experiments on a real dataset of 5000 customers show a high performance for the proposed method.read more
Citations
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Journal ArticleDOI
Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges
TL;DR: An application-oriented review of smart meter data analytics identifies the key application areas as load analysis, load forecasting, and load management and reviews the techniques and methodologies adopted or developed to address each application.
Journal ArticleDOI
Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges
TL;DR: In this paper, the authors conduct an application-oriented review of smart meter data analytics following the three stages of analytics, namely, descriptive, predictive and prescriptive analytics, identifying the key application areas as load analysis, load forecasting, and load management.
Journal ArticleDOI
Wide and Deep Convolutional Neural Networks for Electricity-Theft Detection to Secure Smart Grids
TL;DR: A novel electricity-theft detection method based on wide and deep convolutional neural networks (CNN) model that outperforms other existing methods in detection accuracy and captures the global features of 1-D electricity consumption data.
Journal ArticleDOI
A Survey on the Detection Algorithms for False Data Injection Attacks in Smart Grids
TL;DR: An intensive summary of several detection algorithms for false data injection attacks by categorizing them and elaborating on the pros and cons of each category is provided.
Journal ArticleDOI
Decision Tree and SVM-Based Data Analytics for Theft Detection in Smart Grid
TL;DR: This paper proposes a comprehensive top-down scheme capable enough to precisely detect and locate real-time electricity theft at every level in power transmission and distribution (T&D).
References
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