A New Approach for Nontechnical Losses Detection Based on Optimum-Path Forest

Ramos, Caio C. O.; Sousa, Aline Nonato de; Papa, João Paulo; Falcão, Alexandre X.

doi:10.1109/tpwrs.2010.2051823

Cited by 116 publications

(58 citation statements)

References 18 publications

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“…In (Ramos et al, 2010) a new approach, Optimum Path Forest (OPF), is applied to fraud detection in electricity consumption. The work shows good results in a problem similar to the targeted.…”

Section: Opfmentioning

confidence: 99%

“…Research in pattern classification field has been made to tackle this problem (Ramos et al, 2010), (Nagi and Mohamad, 2010), (Muniz et al, 2009), (Jiang et al, 2000) In Uruguay the national electric power company (henceforth call UTE) faces the problem by manually monitoring a group of customers. A group of experts looks at the monthly consumption curve of each customer and indicates those with some kind of suspicious behavior.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Improving Electric Fraud Detection Using Class Imbalance Strategies

Martino¹,

Decia²,

Molinelli³

et al. 2012

Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods

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Abstract:Improving nontechnical loss detection is a huge challenge for electric companies. The great number of clients and the diversity of the different types of fraud makes this a very complex task. In this paper we present a fraud detection strategy based on class imbalance research. An automatic detection tool combining classification strategies is proposed. Individual classifiers such as One Class SVM, Cost Sensitive SVM (CS-SVM), Optimum Path Forest (OPF) and C4.5 Tree, and combination functions are designed taken special care in the data's class imbalance nature. Analysis over consumers historical kWh load profile data from Uruguayan Electric Company (UTE) shows that using combination and balancing techniques improves automatic detection performance.

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Section: Opfmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improving Electric Fraud Detection Using Class Imbalance Strategies

Martino¹,

Decia²,

Molinelli³

et al. 2012

Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods

View full text Add to dashboard Cite

show abstract

“…E. W. S. dos Angelos [8] presented a cluster-based classification strategy with an unsupervised algorithm of two steps to identify suspected profiles of power consumption, providing a good assertiveness in real life systems. Over the past years, other studies in this field have been addressed applying different computational techniques to improve the detection of non-technical losses [9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Fraud Detection in Electric Power Distribution Networks using an Ann-Based Knowledge-Discovery Process

Costa¹,

Alberto²,

Portela³

et al. 2013

IJAIA

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“…The optimum-path forest classifier is a graph-based technique that is less common in the literature and is reported as outperforming SVM and ANN in [51,52].…”

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confidence: 99%

Solutions for detection of non-technical losses in the electricity grid: A review

Viegas

Esteves

Melício

et al. 2017

Renewable and Sustainable Energy Reviews

149

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This paper is a review of literature with an analysis on a selection of scientific studies for detection of non-technical losses. Non-technical losses occurring in the electric grid at level of transmission or of distribution have negative impact on economies, affecting utilities, paying consumers and states. The paper is concerned with the lines of research pursued, the main techniques used and the limitations on current solutions. Also, a typology for the categorization of solutions for detection of non-technical losses is proposed and the sources and possible attack/vulnerability points are identified. The selected literature covers a wide range of solutions associated with non-technical losses. Of the 103 selected studies, 6 are theoretical, 25 propose hardware solutions and 72 propose non-hardware solutions. Data based classification models and data from consumption with high resolution are respectively required in about 47% and 35% of the reported solutions. Available solutions cover a wide range of cases, with the main limitation found being the lack of an unified solution, which enables the detection of all kinds of non-technical losses.

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A New Approach for Nontechnical Losses Detection Based on Optimum-Path Forest

Cited by 116 publications

References 18 publications

Improving Electric Fraud Detection Using Class Imbalance Strategies

Improving Electric Fraud Detection Using Class Imbalance Strategies

Fraud Detection in Electric Power Distribution Networks using an Ann-Based Knowledge-Discovery Process

Solutions for detection of non-technical losses in the electricity grid: A review

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