GREEN DATA MINING / Alem Mehani ,Hani  

Public ISBD Titre : GREEN DATA MINING Type de document : texte imprimé Auteurs : Alem Mehani ,Hani, Auteur ; Harrag,Fouzi, Directeur de thèse Editeur : Setif:UFA Année de publication : 2021 Importance : 1 vol (57 f .) Format : 29 cm Langues : Français (fre) Catégories : Thèses & Mémoires:Informatique Mots-clés : Electricity theft detection Machine Learning Index. décimale : 004 - Informatique Résumé : Electricity theft is a big problem faced by all energy distribution services, and it is still on the rise. It will reduce the quality of supply, increase production costs, cause legitimate consumers to pay higher costs, and aect the entire economy. Therefore, in recent years there has been an increase in research on electricity theft detection technology. Unsuitable and illegal calibration of electric energy meters in the production process may cause non-technical losses. Non-technical losses have always been the main problem of the resulting security risks and immeasurable loss of revenue. In locations where most meters have been tampered with, it is impossible to distinguish between damaged meter terminals and/or illegal applications during the inspection process. In fact, the power distribution company will never be able to prevent the theft of electricity. But measures can be taken to detect, prevent and reduce it. The data analysis of the electricity consumption is helpful in detecting electricity theft because of the abnormal electricity consumption pattern of energy thieves. To address these issues Electricity Theft Detection (ETD) model is proposed that consists of four steps: interpolation, data balancing, feature extraction and classication. Côte titre : MAI/0470 En ligne : https://drive.google.com/file/d/154RYzPkVVd2RQvsljsGrZ6sRnAXyeBij/view?usp=shari [...] Format de la ressource électronique : pdf GREEN DATA MINING [texte imprimé] / Alem Mehani ,Hani, Auteur ; Harrag,Fouzi, Directeur de thèse . - [S.l.] : Setif:UFA, 2021 . - 1 vol (57 f .) ; 29 cm. Langues : Français (fre) Catégories : Thèses & Mémoires:Informatique Mots-clés : Electricity theft detection Machine Learning Index. décimale : 004 - Informatique Résumé : Electricity theft is a big problem faced by all energy distribution services, and it is still on the rise. It will reduce the quality of supply, increase production costs, cause legitimate consumers to pay higher costs, and aect the entire economy. Therefore, in recent years there has been an increase in research on electricity theft detection technology. Unsuitable and illegal calibration of electric energy meters in the production process may cause non-technical losses. Non-technical losses have always been the main problem of the resulting security risks and immeasurable loss of revenue. In locations where most meters have been tampered with, it is impossible to distinguish between damaged meter terminals and/or illegal applications during the inspection process. In fact, the power distribution company will never be able to prevent the theft of electricity. But measures can be taken to detect, prevent and reduce it. The data analysis of the electricity consumption is helpful in detecting electricity theft because of the abnormal electricity consumption pattern of energy thieves. To address these issues Electricity Theft Detection (ETD) model is proposed that consists of four steps: interpolation, data balancing, feature extraction and classication. Côte titre : MAI/0470 En ligne : https://drive.google.com/file/d/154RYzPkVVd2RQvsljsGrZ6sRnAXyeBij/view?usp=shari [...] Format de la ressource électronique : pdf

Exemplaires (1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
MAI/0470	MAI/0470	Mémoire	Bibliothéque des sciences	Anglais	Disponible Disponible

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