Energy Theft Detection Using Gradient Boosting Theft Detector With Feature Engineering-Based Preprocessing

Rajiv Punmiya

doi:10.1109/TSG.2019.2892595

Energy Theft Detection Using Gradient Boosting Theft Detector With Feature Engineering-Based Preprocessing

2019-03-01

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DOI：10.1109/TSG.2019.2892595

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For the smart grid energy theft identification, this letter introduces a gradient boosting theft detector (GBTD) based on the three latest gradient boosting classifiers (GBCs): 1) extreme gradient boosting; 2) categorical boosting; and 3) light gradient boosting method. While most of existing machine learning (ML) algorithms just focus on fine tuning the hyper-parameters of the classifiers, our ML algorithm, GBTD, focuses on the feature engineering-based preprocessing to improve detection performance as well as time-complexity. GBTD improves both detection rate and false positive rate (FPR) of those GBCs by generating stochastic features like standard deviation, mean, minimum, and maximum value of daily electricity usage. GBTD also reduces the classifier complexity with weighted feature-importance-based extraction techniques. Emphasis has been laid upon the practical application of the proposed ML for theft detection by minimizing FPR and reducing data storage space and improving time-complexity of the GBTD classifiers. Additionally, this letter proposes an updated version of the existing six theft cases to mimic real-world theft patterns and applies them to the dataset for numerical evaluation of the proposed algorithm.

Keywords:

Energy theft detection

machine learning

gradient boosting

XGBoost

CatBoost

LightGBM

SVM

feature engineering

Energy Theft Detection Using Gradient Boosting Theft Detector With Feature Engineering-Based Preprocessing

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