Nontechnical Loss Detection for Metered Customers in Power Utility Using Support Vector Machines

Nagi, Jawad; Yap, Keem Siah; Tiong, Sieh Kiong; Ahmed, Syed Khaleel; Mohamad, Muhammad Arif

doi:10.1109/tpwrd.2009.2030890

Cited by 372 publications

(197 citation statements)

References 29 publications

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“…They used regression to study the consumption trends of customers, text mining techniques to analyze inspector commentaries, and association rules to extract additional customer information from the electric company. Nagi et al [58], on the other hand, used Support Vector Machines to detect abnormal behavior correlated with NTL activities by analyzing customer load profile information as well as certain other attributes.…”

Section: Energy Fraud Detectionmentioning

confidence: 99%

Data science for building energy management: A review

Molina-Solana

Ros

Ruiz

et al. 2017

Renewable and Sustainable Energy Reviews

209

108

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The energy consumption of residential and commercial buildings has risen steadily in recent years, an increase largely due to their HVAC systems. Expected energy loads, transportation, and storage as well as user behavior influence the quantity and quality of the energy consumed daily in buildings. However, technology is now available that can accurately monitor, collect, and store the huge amount of data involved in this process. Furthermore, this technology is capable of analyzing and exploiting such data in meaningful ways. Not surprisingly, the use of data science techniques to increase energy efficiency is currently attracting a great deal of attention and interest. This paper reviews how Data Science has been applied to address the most difficult problems faced by practitioners in the field of Energy Management, especially in the building sector. The work also discusses the challenges and opportunities that will arise with the advent of fully connected devices and new computational technologies.

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Section: Energy Fraud Detectionmentioning

confidence: 99%

Data science for building energy management: A review

Molina-Solana

Ros

Ruiz

et al. 2017

Renewable and Sustainable Energy Reviews

209

108

View full text Add to dashboard Cite

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“…Daily average consumption features of the last 25 months are used in [10] for less than 400 out of a highly imbalanced data set of 260K customers. These features are then used in a support vector machine (SVM) with a Gaussian kernel for NTL prediction, for which a test recall of 0.53 is achieved.…”

Section: A Backgroundmentioning

confidence: 99%

Distilling provider-independent data for general detection of non-technical losses

Meira

Glauner

State

et al. 2017

2017 IEEE Power and Energy Conference at Illinois (PECI)

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Abstract-Non-technical losses (NTL) in electricity distribution are caused by different reasons, such as poor equipment maintenance, broken meters or electricity theft. NTL occurs especially but not exclusively in emerging countries. Developed countries, even though usually in smaller amounts, have to deal with NTL issues as well. In these countries the estimated annual losses are up to six billion USD. These facts have directed the focus of our work to the NTL detection. Our approach is composed of two steps: 1) We compute several features and combine them in sets characterized by four criteria: temporal, locality, similarity and infrastructure. 2) We then use the sets of features to train three machine learning classifiers: random forest, logistic regression and support vector vachine. Our hypothesis is that features derived only from provider-independent data are adequate for an accurate detection of non-technical losses. We used Area Under the Receiver-operating Curve (AUC) to assess the results.

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“…The principle is to find a classifier, or a discrimination function, the generalization ability (quality forecast) is the largest possible (Nagi et al, 2008b). That is to say, to bring the issue of discrimination in the linear, the search for an optimal hyper plane and two ideas or tricks achieve this objective (Nagi et al, 2010a;2010b):…”

Section: Support Vector Machinementioning

confidence: 99%

Evaluation of Customer Behaviour Irregularities in Cameroon Electricity Network using Support Vector Machine

Bernard¹,

Benoît²,

Hamandjoda³

2017

American Journal of Engineering and Applied Sciences

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Non-Technical Losses (NTLs) in the Cameroonians electricity network are approximately 30 to 40% of production and are estimated at several billion CFA francs per year for National Electricity Company (ENEO); Hence the importance of finding effective solutions to fight against these losses. The purpose of this work was to develop a tool for the fraud detection for Cameroon National Electricity Company (ENEO) using support vector machines which consisted in data preprocessing base on the load profile, development of a model for classification, parameter optimization and detection of customers irregularities and prediction.

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Nontechnical Loss Detection for Metered Customers in Power Utility Using Support Vector Machines

Cited by 372 publications

References 29 publications

Data science for building energy management: A review

Data science for building energy management: A review

Distilling provider-independent data for general detection of non-technical losses

Evaluation of Customer Behaviour Irregularities in Cameroon Electricity Network using Support Vector Machine

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