A practical feature-engineering framework for electricity theft detection in smart grids

Rouzbeh Razavi, Amin Gharipour, Martin Fleury and Ikpe Justice Akpan

Applied Energy, 2019, vol. 238, issue C, 494 pages

Abstract: Despite many potential advantages, Advanced Metering Infrastructures have introduced new ways to falsify meter readings and commit electricity theft. This study contributes a new model-agnostic, feature-engineering framework for theft detection in smart grids. The framework introduces a combination of Finite Mixture Model clustering for customer segmentation and a Genetic Programming algorithm for identifying new features suitable for prediction. Utilizing demand data from more than 4000 households, a Gradient Boosting Machine algorithm is applied within the framework, significantly outperforming the results of prior machine-learning, theft-detection methods. This study further examines some important practical aspects of deploying theft detection including: the detection delay; the required size of historical demand data; the accuracy in detecting thefts of various types and intensity; detecting irregular and unseen attacks; and the computational complexity of the detection algorithm.

Keywords: Theft detection; Feature engineering; Data mining; Smart meters (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (17)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:238:y:2019:i:c:p:481-494

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DOI: 10.1016/j.apenergy.2019.01.076

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