Fraud Detection in Electric Power Distribution: An Approach That Maximizes the Economic Return

Massaferro, Pablo; Martino, J. Matías Di; Fernández, Alicia

doi:10.1109/tpwrs.2019.2928276

Cited by 52 publications

(20 citation statements)

References 24 publications

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“…AUC AUC is used in the classification analysis to evaluate which models classify effectively. [27,43,63,80] Average precision Combines the result of recall and precision. [43] Recognition Rate Rec.Rate = 1-0.5[(FP/N) + (FN/P)] [12,38] BDR [P(I).…”

Section: Performance Metrics Used For Ntl Detectionmentioning

confidence: 99%

“…The Bayesian classifiers are the probabilistic classifiers in which the responsibility of the class is to predict the values of the features. The Bayesian classifier needs the previous information of NTL probability that can be obtained from the general statistics [27,33,62,64,103]. The working of this classifier is based on the fact that if the class of the sample is known, it can be utilized to calculate the values of the different features.…”

Section: Bayesian Classifiersmentioning

confidence: 99%

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Detection of Non-Technical Losses in Power Utilities—A Comprehensive Systematic Review

et al. 2020

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Electricity theft and fraud in energy consumption are two of the major issues for power distribution companies (PDCs) for many years. PDCs around the world are trying different methodologies for detecting electricity theft. The traditional methods for non-technical losses (NTLs) detection such as onsite inspection and reward and penalty policy have lost their place in the modern era because of their ineffective and time-consuming mechanism. With the advancement in the field of Artificial Intelligence (AI), newer and efficient NTL detection methods have been proposed by different researchers working in the field of data mining and AI. The AI-based NTL detection methods are superior to the conventional methods in terms of accuracy, efficiency, time-consumption, precision, and labor required. The importance of such AI-based NTL detection methods can be judged by looking at the growing trend toward the increasing number of research articles on this important development. However, the authors felt the lack of a comprehensive study that can provide a one-stop source of information on these AI-based NTL methods and hence became the motivation for carrying out this comprehensive review on this significant field of science. This article systematically reviews and classifies the methods explored for NTL detection in recent literature, along with their benefits and limitations. For accomplishing the mentioned objective, the opted research articles for the review are classified based on algorithms used, features extracted, and metrics used for evaluation. Furthermore, a summary of different types of algorithms used for NTL detection is provided along with their applications in the studied field of research. Lastly, a comparison among the major NTL categories, i.e., data-based, network-based, and hybrid methods, is provided on the basis of their performance, expenses, and response time. It is expected that this comprehensive study will provide a one-stop source of information for all the new researchers and the experts working in the mentioned area of research.

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Section: Performance Metrics Used For Ntl Detectionmentioning

confidence: 99%

Section: Bayesian Classifiersmentioning

confidence: 99%

Detection of Non-Technical Losses in Power Utilities—A Comprehensive Systematic Review

et al. 2020

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“…To analyze the classification results, accuracy, precision, recall, area under the ROC curve (AUC) [35], and F1-score [36] are calculated to determine the performance of ML models. Besides, to obtain more meaningful performance measures, certain performance criteria, such as the numbers of true positives (TPs), true negatives (TNs), false positives (FPs), and false negatives (FNs), are commonly used in previous studies [29,[37][38][39][40][41][42][43].…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Analysis of Supervised Learning Techniques for Electricity Theft Detection

Bohani¹,

Suliman²,

Saripuddin³

et al. 2021

Journal of Electrical and Computer Engineering

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There are many methods or algorithms applicable for detecting electricity theft. However, comparative studies on supervised learning methods for electricity theft detection are still insufficient. In this paper, comparisons based on predictive accuracy, recall, precision, AUC, and F1-score of several supervised learning methods such as decision tree (DT), artificial neural network (ANN), deep artificial neural network (DANN), and AdaBoost are presented and their performances are analyzed. A public dataset from the State Grid Corporation of China (SGCC) was used for this study. The dataset consisted of power consumption in kWh unit. Based on the analysis results, the DANN outperforms compared to other supervised learning classifiers such as ANN, AdaBoost, and DT in recall, F1-Score, and AUC. A future research direction is the experiments can be performed on other supervised learning algorithms with different types of datasets and suitable preprocessing methods can be applied to produce better performance.

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“…A machine learning solution is tried in [11] based on customer consumption patterns and energy bills. It produces a list of clients to be inspected and supports the electric power utilities dealing with the problem of NTL.…”

Section: Introductionmentioning

confidence: 99%

Online electricity theft detection and prevention scheme for smart cities

et al. 2020

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Fraud Detection in Electric Power Distribution: An Approach That Maximizes the Economic Return

Cited by 52 publications

References 24 publications

Detection of Non-Technical Losses in Power Utilities—A Comprehensive Systematic Review

Detection of Non-Technical Losses in Power Utilities—A Comprehensive Systematic Review

A Comprehensive Analysis of Supervised Learning Techniques for Electricity Theft Detection

Online electricity theft detection and prevention scheme for smart cities

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