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