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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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Autori principali: Nien-Che Yang, Ke-Lin Sung
Natura: Artigo
Lingua:Inglês
Pubblicazione: IEEE 2023-01-01
Serie:IEEE Access
Soggetti:
Decision tree
ensemble learning
K-nearest neighbors
multilayer perceptron
non-intrusive load monitoring
normalization
Accesso online:https://ieeexplore.ieee.org/document/10238467/
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