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

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@Article{RAZAVI:2019:AE,

• author = "Rouzbeh Razavi and Amin Gharipour and Martin Fleury and Ikpe Justice Akpan",
• title = "A practical feature-engineering framework for electricity theft detection in smart grids",
• journal = "Applied Energy",
• year = "2019",
• volume = "238",
• pages = "481--494",
• keywords = "genetic algorithms, genetic programming, Theft detection, Feature engineering, Data mining, Smart meters",
• ISSN = "0306-2619",
• URL = "https://ideas.repec.org/a/eee/appene/v238y2019icp481-494.html",
• URL = "http://www.sciencedirect.com/science/article/pii/S0306261919300753",
• DOI = "doi:10.1016/j.apenergy.2019.01.076",
• 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. Using 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",

}

Genetic Programming entries for Rouzbeh Razavi Amin Gharipour Martin Fleury Ikpe Justice Akpan

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