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New Smart Grids Findings from Catholic University of Korea Discussed (Energy Theft Detection Using Gradient Boosting Theft Detector With Feature Engineering-based Preprocessing)

Source: 
NewsRx Policy and Law Daily

2019 MAR 21 (NewsRx) -- By a News Reporter-Staff News Editor at NewsRx Policy and Law Daily -- Current study results on Energy - Smart Grids have been published. According to news reporting originating from Bucheon, South Korea, by NewsRx correspondents, research stated, “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.”

Funders for this research include National Research Foundation of Korea (NRF) - Korea government (MSIT), Catholic University of Korea.

Our news editors obtained a quote from the research from the Catholic University of Korea, “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.”

According to the news editors, the research concluded: “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.”

For more information on this research see: Energy Theft Detection Using Gradient Boosting Theft Detector With Feature Engineering-based Preprocessing. IEEE Transactions on Smart Grid, 2019;10(2):2326-2329. IEEE Transactions on Smart Grid can be contacted at: Ieee-Inst Electrical Electronics Engineers Inc, 445 Hoes Lane, Piscataway, NJ 08855-4141, USA. (Institute of Electrical and Electronics Engineers - http://www.ieee.org/; IEEE Transactions on Smart Grid - http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=5165411)

The news editors report that additional information may be obtained by contacting S. Choe, Catholic University of Korea, Dept. of Informat Commun & Elect Engn, Bucheon 420743, South Korea.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1109/TSG.2019.2892595. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

 

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