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ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016
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Issue: May 2023

Volume 8 | Issue 5

Impact factor: 8.15

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Paper Title: Electricity Theft Detection Using Machine Learning
Authors Name: Prof. Shubham R. Bhandari , Anuja Nandkumar Kulkarni , Purva Ravindra Sutar , Ruchika Gulab Zodage
Unique Id: IJSDR2305053
Published In: Volume 8 Issue 5, May-2023
Abstract: Electricity robbery is one of the predominant issues of electric powered utilities. Such power robbery produces monetary loss to the software agencies. It isn't always viable to check out manually such robbery in massive quantity of records. For detecting such power robbery introduces a gradient boosting robbery detector. (GBTD) primarily based totally on the 3 present day gradient boosting classifiers (GBCs): intense gradient boosting (XG Boost), specific boosting (Cat Boost), and mild gradient boosting method (Light). XGBoost is one system getting to know set of rules which offers excessive accuracy in less time. In this we practice preprocessing on clever meter records then does characteristic choice. Practical utility of the proposed GBTD for robbery detection through minimizing FPR and lowering records garage area and enhancing time complexity of the GBTD classifiers which come across nontechnical loss (NTL) detection.
Keywords: Artificial Intelligence, Deep Learning, OCR set of rules, Object evaluation, Feature Extraction, Recognition, Classification.
Cite Article: "Electricity Theft Detection Using Machine Learning", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.8, Issue 5, page no.348 - 350, May-2023, Available :http://www.ijsdr.org/papers/IJSDR2305053.pdf
Downloads: 000223171
Publication Details: Published Paper ID: IJSDR2305053
Registration ID:205359
Published In: Volume 8 Issue 5, May-2023
DOI (Digital Object Identifier):
Page No: 348 - 350
Publisher: IJSDR | www.ijsdr.org
ISSN Number: 2455-2631

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