Support vector machine based data classification for detection of electricity theft

Depuru, Soma Shekara Sreenadh Reddy; Wang, Lingfeng; Devabhaktuni, Vijay

doi:10.1109/psce.2011.5772466

Cited by 143 publications

(62 citation statements)

References 17 publications

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“…Our choice of models for comparison is based on the wide use of these classification techniques, SVM and MLP, and wide acceptance as two well-known approaches for classification and prediction by the research community [53][54][55][56][57][58][59][60][61][62][63][64].…”

Section: Evaluation and Resultsmentioning

confidence: 99%

Big Data Mining of Energy Time Series for Behavioral Analytics and Energy Consumption Forecasting

2018

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Responsible, efficient and environmentally aware energy consumption behavior is becoming a necessity for the reliable modern electricity grid. In this paper, we present an intelligent data mining model to analyze, forecast and visualize energy time series to uncover various temporal energy consumption patterns. These patterns define the appliance usage in terms of association with time such as hour of the day, period of the day, weekday, week, month and season of the year as well as appliance-appliance associations in a household, which are key factors to infer and analyze the impact of consumers' energy consumption behavior and energy forecasting trend. This is challenging since it is not trivial to determine the multiple relationships among different appliances usage from concurrent streams of data. Also, it is difficult to derive accurate relationships between interval-based events where multiple appliance usages persist for some duration. To overcome these challenges, we propose unsupervised data clustering and frequent pattern mining analysis on energy time series, and Bayesian network prediction for energy usage forecasting. We perform extensive experiments using real-world context-rich smart meter datasets. The accuracy results of identifying appliance usage patterns using the proposed model outperformed Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP) at each stage while attaining a combined accuracy of 81.82%, 85.90%, 89.58% for 25%, 50% and 75% of the training data size respectively. Moreover, we achieved energy consumption forecast accuracies of 81.89% for short-term (hourly) and 75.88%, 79.23%, 74.74%, and 72.81% for the long-term; i.e., day, week, month, and season respectively.

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Section: Evaluation and Resultsmentioning

confidence: 99%

Big Data Mining of Energy Time Series for Behavioral Analytics and Energy Consumption Forecasting

2018

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“…Here the focus was on the detection of abnormal electricity traces that are highly correlated with electricity theft. This work used a variety of machine learning techniques, including Support Vector Machines and Extreme Learning Machines to identify suspicious energy traces [14], [15], [16]. More recent work has emphasized the need to consider consumption data anomalies as part of a diagnostic system with the aim of enabling sensor fusion at the scale of a electricity distribution network and reduce false positives [17].…”

Section: Related Workmentioning

confidence: 99%

Game-Theoretic Models of Electricity Theft Detection in Smart Utility Networks: Providing New Capabilities with Advanced Metering Infrastructure

Amin

Schwartz²,

Cárdenas³

et al. 2015

IEEE Control Syst.

112

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The smart grid refers to the modernization of the power grid infrastructure with new technologies, enabling a more intelligently networked automated system with the goal of improving efficiency, reliability, and security, while providing more transparency and choices to electricity consumers. One of the key technologies being widely deployed on the consumption side of the grid is the Advanced Metering Infrastructure (AMI).AMI refers to the modernization of the electricity metering system by replacing old mechanical meters by smart meters. Smart meters are new embedded devices that provide two-way communications between the utility and the consumer. These devices have advanced communication and computational capabilities, with a potential to enable new functionalities such as improved service choices, transparencies, etc.Distribution utilities (or distributors) using AMIs for monitoring and billing of electricity consumption can avoid sending their employees to read the meters on-site. Importantly, AMIs provide several new capabilities: monitoring of network-wide and individual electricity consumption, faster remote diagnosis of outages (with analog meters, utilities 1

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“…To begin with, several contributions have gravitated on the use of machine learning models over supervised datasets, such as Support Vector Machines [13,14,15,16,17,18], Neural Networks [19,20,21], Extreme Learning Machines [22], Path Forests [23,24], Decision Trees [25,26,27], model ensembles [28], and statistical methods [29,30]. However, all such previous work builds upon the assumption that supervised datasets capture the entire casuistry of symptomatic anomalies of interest for fraud detection and/or electricity theft, which not only unrealistic in practice but also yields highly imbalanced datasets that subsequently jeopardize the model learning process.…”

Section: Introductionmentioning

confidence: 99%

Detection of non-technical losses in smart meter data based on load curve profiling and time series analysis

Villar-Rodríguez

Ser

Oregi

et al. 2017

Energy

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Support vector machine based data classification for detection of electricity theft

Cited by 143 publications

References 17 publications

Big Data Mining of Energy Time Series for Behavioral Analytics and Energy Consumption Forecasting

Big Data Mining of Energy Time Series for Behavioral Analytics and Energy Consumption Forecasting

Game-Theoretic Models of Electricity Theft Detection in Smart Utility Networks: Providing New Capabilities with Advanced Metering Infrastructure

Detection of non-technical losses in smart meter data based on load curve profiling and time series analysis

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