Fraud detection in low-voltage electricity consumers using socio-economic indicators and billing profile in smart grids

Pulz, Jonatas; Muller, Renan Bergonsi; Romero, Flávio Ramalho; Méffe, André; Neto, Álvaro F. Garcez; Jesus, Aldo S.

doi:10.1049/oap-cired.2017.1258

Cited by 17 publications

(6 citation statements)

References 5 publications

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“…For households, the authors employed a decision tree (DT) algorithm to predict the energy consumption value and, subsequently, a support vector machine (SVM) classifier was trained on multiple features to locate customers with anomalous consumption behaviour. On a similar task, Pulz et al [17] used census data to extract social indicators to find the interdependence between losses and socio-economic indices for ETD under various scenarios. Such aggregated data-driven approaches are useful; however, problems like non-stationary high-volume data measurements need to be addressed to compose useful clusters.…”

Section: Related Work and Contributionsmentioning

confidence: 99%

Robust Data Driven Analysis for Electricity Theft Attack-Resilient Power Grid

Khan

Javaid

Taylor

et al. 2023

IEEE Trans. Power Syst.

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The role of electricity theft detection (ETD) is critical to maintain cost-efficiency in smart grids. However, existing ETD methods cannot efficiently handle the sheer volume of data now available, being limited by issues such as missing values, high variance and non-linearity. An integrated infrastructure is also required for synchronizing diverse procedures in electricity theft classification. To help address such problems, a novel ETD framework is proposed that combines three distinct modules. The first module handles missing values, outliers, and unstandardised electricity consumption data. The second module employs a newly proposed hybrid class balancing approach to deal with highly imbalanced datasets. The third module utilises an improved artificial neural network (iANN) based classification engine, to predict electricity theft cases accurately and efficiently. We propose three distinctive mechanisms, including hyper-parameters tuning, regularization and skip connections, to improve the performance of standard ANN to handle more complex classification tasks using smart meter (SM) data. Furthermore, various structures of iANN are investigated to improve the generalization and function fitting capabilities of the final classification. Numerical results from real-world energy usage datasets confirm that the proposed ETD model has superior performance compared to existing machine learning and deep learning methods, and can effectively be applied to industrial applications.

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Section: Related Work and Contributionsmentioning

confidence: 99%

Robust Data Driven Analysis for Electricity Theft Attack-Resilient Power Grid

Khan

Javaid

Taylor

et al. 2023

IEEE Trans. Power Syst.

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“…Faktor lingkungan dan komunitas sosial memiliki pengaruh yang sangat penting bagi tindakan dan pencegahan kejahatan (Chua et.al., 2007 (Pulz, et.al., 2017). Sebuah penelitian dari Igwe (2011) mengemukakan bahwa faktor sosial-ekonomi seperti pengangguran dan kemiskinan, keduanya merupakan faktor penyumbang tindak kecurangan (Igwe, 2011).…”

Section: Pembahasanunclassified

Pencegahan Fraud Dengan Pengendalian Internal Dalam Perspektif Alquran

Sudarmanto

Utami

2021

JIEI

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The losses of fraudulent practices that are still occurring have become a problem for organizations and society today. The problem discussed in this study is limited fraud prevention with internal control seen from the perspective of the Qur’an, which is a holy book and the highest source of law for Moslems. This research is important to know how the perspective of the Qur'an in seeing and providing solutions to existing problems. The aim of this multi-case qualitative research is to explore methods of prevention against fraud by applying internal controls from the perspective of the Qur'an. As a conclusion, that internal control is part of corporate governance, so that management must consider the factors of thought, emotion, age, loyalty, justice, and the socio-economic environment of employees in formulating a policy. Internal controls need to be designed to be able to anticipate and prevent fraud that involves colluding with others. Internal controls can increase the ability to detect fraud by increasing effectiveness and utilizing the latest technology. Contextually, several principles in internal control are contained in several verses in the Qur’an. And what is more important is that in offering a solution to a problem, the Qur’an always provides a perspective not only materially but also spiritually.

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“…Observer meter-SVM [46,47] Smart meter-SVM [48] Smart meter-observer metermaximum information coefficient (MIC) -clustering technique [49] They are also known as supervised and unsupervised techniques, which will be used later for comparison purposes.…”

Section: Supervised Learningmentioning

confidence: 99%

Innovative Methodology to Identify Errors in Electric Energy Measurement Systems in Power Utilities

et al. 2021

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Many electric utilities currently have a low level of smart meter implementation on traditional distribution grids. These utilities commonly have a problem associated with non-technical energy losses (NTLs) to unidentified energy flows consumed, but not billed in power distribution grids. They are usually due to either the electricity theft carried out by their own customers or failures in the utilities’ energy measurement systems. Non-technical energy losses lead to significant economic losses for electric utilities around the world. For instance, in Latin America and the Caribbean countries, NTLs represent around 15% of total energy generated in 2018, varying between 5 and 30% depending on the country because of the strong correlation with social, economic, political, and technical variables. According to this, electric utilities have a strong interest in finding new techniques and methods to mitigate this problem as much as possible. This research presents the results of determining with the precision of the existing data-oriented methods for detecting NTL through a methodology based on data analytics, machine learning, and artificial intelligence (multivariate data, analysis methods, classification, grouping algorithms, i.e., k-means and neural networks). The proposed methodology was implemented using the MATLAB computational tool, demonstrating improvements in the probability to identify the suspected customer’s measurement systems with error in their records that should be revised to reduce the NTLs in the distribution system and using the information from utilities’ databases associated with customer information (customer information system), the distribution grid (geographic information system), and socio-economic data. The proposed methodology was tested and validated in a real situation as a part of a recent Ecuadorian electric project.

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Fraud detection in low-voltage electricity consumers using socio-economic indicators and billing profile in smart grids

Cited by 17 publications

References 5 publications

Robust Data Driven Analysis for Electricity Theft Attack-Resilient Power Grid

Robust Data Driven Analysis for Electricity Theft Attack-Resilient Power Grid

Pencegahan Fraud Dengan Pengendalian Internal Dalam Perspektif Alquran

Innovative Methodology to Identify Errors in Electric Energy Measurement Systems in Power Utilities

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