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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...

Ausführliche Beschreibung

Gespeichert in:
Hauptverfasser: JianYuan Wang, Long Cheng
Format: Artigo
Sprache:Inglês
Veröffentlicht: Elsevier 2025-05-01
Schriftenreihe:International Journal of Electrical Power & Energy Systems
Schlagworte:
Non-Intrusive Load Identification
MTF Encoding
Feature Fusion
YOLOv8 Neural Network
XGBoost Algorithm
Online-Zugang:http://www.sciencedirect.com/science/article/pii/S0142061525001243
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