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A practical feature-engineering framework for electricity theft detection in smart grids
- Razavi, Rouzbeh, Gharipour, Amin, Fleury, Martin, Akpan, Ikpe Justice
- Applied energy 2019 v.238 pp. 481-494
- algorithms, artificial intelligence, electricity, households, infrastructure, models, prediction
- 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.