Electricity theft detection using smart meter data

Sahoo, Sanujit; Nikovski, Daniel; Muso, Toru; Tsuru, Kaoru

doi:10.1109/isgt.2015.7131776

Cited by 80 publications

(47 citation statements)

References 5 publications

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“…However, we did not notice this for the recall and made observations of non-linearity similar to related work in Ref. 27, as depicted in Table 1. With the limitations of precision and recall, the F 1 score did not prove to work as a reliable performance measure.…”

Section: Handling Class Imbalance and Evaluation Metricmentioning

confidence: 44%

The Challenge of Non-Technical Loss Detection Using Artificial Intelligence: A Survey

Glauner¹,

Meira²,

Valtchev³

et al. 2017

IJCIS

187

137

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Detection of non-technical losses (NTL) which include electricity theft, faulty meters or billing errors has attracted increasing attention from researchers in electrical engineering and computer science. NTLs cause significant harm to the economy, as in some countries they may range up to 40% of the total electricity distributed. The predominant research direction is employing artificial intelligence to predict whether a customer causes NTL. This paper first provides an overview of how NTLs are defined and their impact on economies, which include loss of revenue and profit of electricity providers and decrease of the stability and reliability of electrical power grids. It then surveys the state-of-the-art research efforts in a up-to-date and comprehensive review of algorithms, features and data sets used. It finally identifies the key scientific and engineering challenges in NTL detection and suggests how they could be addressed in the future.

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Section: Handling Class Imbalance and Evaluation Metricmentioning

confidence: 44%

The Challenge of Non-Technical Loss Detection Using Artificial Intelligence: A Survey

Glauner¹,

Meira²,

Valtchev³

et al. 2017

IJCIS

187

137

View full text Add to dashboard Cite

show abstract

“…In such systems, if any tampering is done with the electricity meter EB gets notified about it so that they can take any further action like disconnection of electricity transmission. [8] A common approach for electricity theft is to tamper with the electricity meter installed in the residential or corporate place. There are many ways of doing this.…”

Section: IIImentioning

confidence: 99%

Theft Detection and Disconnection in Automated Electricity Energy Meter: A Survey

Sayyed¹

2018

IJRASET

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In this paper, we study and discuss various automated systems for electricity energy meter. These systems provide automation and eliminate human involvement for meter reading process, theft detection and disconnection of electricity transmission. These automated systems provide accuracy in billing and also enables the consumers to do power optimization by providing electricity consumption information on frequent basis. This consumption information can be provided to the user either through Webpage, Android application or through SMS. This paper focuses on various techniques that can be used for providing security to electricity meter from electricity theft attempts. Theses automated systems also introduce automatic disconnection of the electricity in the case of any tampering happens or in case where consumer fail to pay the electricity bill on time. This paper also discusses various challenges of existing system and how the automated systems can overcome from them.

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“…One of the common problems in this sector has been the electrical losses. Electrical losses can be classified into two groups (Sahoo, Nikovski, Muso, & Tsuru, 2015). On one hand, there are technical losses which usually occur due to the dissipation of energy.…”

Section: Uparela Gonzalez Jimenez and Quinteromentioning

confidence: 99%

Intelligent system for non-technical losses management in residential users of the electricity sector

Cantillo¹,

González²,

Mares³

et al. 2018

Ing. Inv.

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The identification of irregular users is an important assignment in the recovery of energy in the distribution sector. This analysis requires low error levels to minimize non-technical electrical losses in power grid. However, the detection of fraudulent users who have billing does not present a generalized methodology. This issue is complex and varies according to the case study. This paper presents a novel methodology to identify residential fraudulent users by using intelligent systems. The proposed intelligent system consists of three fundamental modules. The first module performs the classification of users with similar power consumption curves using self-organizing maps and genetic algorithms. The second module allows carrying out the monthly electricity demand forecasting through of recursive adjustment of ARIMA models. The third module performs the detection of fraudulent users through an artificial neural network for pattern recognition. For the design and validation of the proposed intelligent system, several tests were performed in each developed module. The database used for the design and evaluation of the modules was constructed with data supplied by the energy distribution company of the Colombian Caribbean Region. The results obtained by the proposed intelligent system show a better performance versus the detection rates obtained by the company. RESUMENLa identificación de usuarios con consumo fraudulento es una actividad importante en la recuperación de energía en el sector de la distribución. Este análisis requiere bajos niveles de error para minimizar las pérdidas eléctricas no técnicas en la red de distribución. Sin embargo, la detección de usuarios fraudulentos con facturación no tiene una metodología generalizada. Este es un problema complejo y varía de acuerdo con cada caso de estudio. Este artículo presenta una nueva metodología para la identificación inteligente de usuarios fraudulentos residenciales basada en sistemas inteligentes. El sistema inteligente propuesto consiste en tres módulos fundamentales. El primer módulo clasifica a los usuarios con curvas de consumo similares a través de mapas auto-organizativos y algoritmo genéticos. El segundo módulo realiza la predicción de consumos mensuales mediante ajustes recursivos de modelos ARIMA. El tercer módulo es el responsable de llevar a cabo la detección de usuarios irregulares por medio de una red neuronal para reconocimiento de patrones. Para el diseño y validación del sistema inteligente propuesto se realizaron pruebas en cada módulo que lo integra para diferentes tipos de clientes del mercado. La base de datos utilizada para el diseño y evaluación de los módulos fue construida a partir de los datos suministrados por la empresa de distribución de energía de la Costa Caribe Colombiana. Los resultados obtenidos por el sistema inteligente propuesto muestran un mejor desempeño frente a los índices de detección obtenidos por la empresa.Palabras clave: pérdidas no técnicas, consumo irregular de electricidad, detección de fraudes, sistemas i...

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Electricity theft detection using smart meter data

Cited by 80 publications

References 5 publications

The Challenge of Non-Technical Loss Detection Using Artificial Intelligence: A Survey

The Challenge of Non-Technical Loss Detection Using Artificial Intelligence: A Survey

Theft Detection and Disconnection in Automated Electricity Energy Meter: A Survey

Intelligent system for non-technical losses management in residential users of the electricity sector

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