Non-Intrusive Load Classification and Recognition Using Soft-Voting Ensemble Learning Algorithm With Decision Tree, K-Nearest Neighbor Algorithm and Multilayer Perceptron

Non-intrusive load monitoring (NILM) detects the energy consumption of individual appliances by monitoring the overall electricity usage in a building. By analyzing voltage and current characteristics, NILM can recognize the usage patterns of various appliances, thus facilitating energy conservation...

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Bibliografische Detailangaben

Hauptverfasser:	Nien-Che Yang, Ke-Lin Sung
Format:	Artigo
Sprache:	Inglês
Veröffentlicht:	IEEE 2023-01-01
Schriftenreihe:	IEEE Access
Schlagworte:	Decision tree ensemble learning K-nearest neighbors multilayer perceptron non-intrusive load monitoring normalization
Online-Zugang:	https://ieeexplore.ieee.org/document/10238467/
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