An Improved YOLOv8-XGBoost load rapid identification method based on multi-feature fusion

Existing non-intrusive load monitoring (NILM) approaches face challenges including limited identification accuracy, computationally intensive architectures, and constrained generalization performance. To address these issues, this paper proposes an Improved YOLOv8-XGBoost rapid load identification m...

Täydet tiedot

Bibliografiset tiedot

Päätekijät:	JianYuan Wang, Long Cheng
Aineistotyyppi:	Artigo
Kieli:	Inglês
Julkaistu:	Elsevier 2025-05-01
Sarja:	International Journal of Electrical Power & Energy Systems
Aiheet:	Non-Intrusive Load Identification MTF Encoding Feature Fusion YOLOv8 Neural Network XGBoost Algorithm
Linkit:	http://www.sciencedirect.com/science/article/pii/S0142061525001243
Tagit:	Lisää tagi Ei tageja, Lisää ensimmäinen tagi!