Big data analytics: an aid to detection of non-technical losses in power utilities

Giovanni Micheli (), Emiliano Soda (), Maria Teresa Vespucci (), Marco Gobbi () and Alessandro Bertani ()
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Giovanni Micheli: University of Bergamo
Emiliano Soda: CESI
Maria Teresa Vespucci: University of Bergamo
Marco Gobbi: CESI
Alessandro Bertani: CESI

Computational Management Science, 2019, vol. 16, issue 1, No 14, 329-343

Abstract: Abstract The great amount of data collected by the Advanced Metering Infrastructure can help electric utilities to detect energy theft, a phenomenon that globally costs over 25 billions of dollars per year. To address this challenge, this paper describes a new approach to non-technical loss analysis in power utilities using a variant of the P2P computing that allows identifying frauds in the absence of total reachability of smart meters. Specifically, the proposed approach compares data recorded by the smart meters and by the collector in the same neighborhood area and detects the fraudulent customers through the application of a Multiple Linear Regression model. Using real utility data, the regression model has been compared with other data mining techniques such as SVM, neural networks and logistic regression, in order to validate the proposed approach. The empirical results show that the Multiple Linear Regression model can efficiently identify the energy thieves even in areas with problems of meters reachability.

Keywords: Energy theft detection; Meters reachability; Multiple linear regression; Data mining (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10287-018-0325-x

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