Unsupervised Learning With Hybrid Models for Detecting Electricity Theft in Smart Grids

In the energy sector, electricity theft presents serious financial and security risks. By fusing supervised learning models (Random Forest) with unsupervised learning algorithms (Isolation Forest, One-Class Support Vector Machine (SVM), Local Outlier Factor (LOF), and Density-Based Spatial Clusterin...

Descrición completa

Detalles Bibliográficos

Autor Principal:	Ali Jaber Almalki
Formato:	Artigo
Idioma:	Inglês
Publicado:	IEEE 2024-01-01
Series:	IEEE Access
Assuntos:	Electricity theft detection smart grids unsupervised learning hybrid models anomaly detection supervised learning
Acceso en liña:	https://ieeexplore.ieee.org/document/10752929/
Tags:	Engadir etiqueta Sen Etiquetas, Sexa o primeiro en etiquetar este rexistro!