ISSN (Online) : 2456 - 0774

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ISSN (Online) 2456 - 0774



Abstract: Power robbery is one of the serious issues of electric utilities. Such electricity theft produce financial loss to the utility companies. It is not possible to inspect manually such theft in large amount of data. For detecting such electricity theft introduces a gradient boosting theft detector (GBTD) based on the three latest gradient boosting classifiers (GBCs): extreme gradient boosting (XGBoost), categorical boosting (Cat Boost), and light gradient boosting method (LightGBM). XGBoost is one machine learning algorithm which gives high accuracy in less time. In this we apply pre-processing on smart meter data then does feature selection. Practical application of the proposed GBTD for theft detection by minimizing FPR and reducing data storage space and improving time-complexity of the GBTD classifiers which detect nontechnical loss (NTL) detection.
Keywords: Electricity data, , machine learning, XgBoost

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