Applications Of Feature Weighted Fuzzy C-Means Clustering And Genetic Algorithm Optimization For Load Identification In NILM Systems

An improved fuzzy clustering non-invasive load monitoring method based on genetic algorithm for feature weight optimization is proposed. The non-intrusive load monitoring research needs to extract the features of electrical appliance waveform data, which has the problems of large number of features...

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Veröffentlicht in:	International Conference on Wavelet Analysis and Pattern Recognition (Print) S. 72 - 77
Hauptverfasser:	Li, Peijie, Du, Qintao, Fan, Yiliang, Huang, Yijie, Liang, Xin, Zhang, Weile
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 02.12.2020
Schlagworte:	Clustering algorithms Data mining Feature extraction Feature weight coefficient Filtering algorithms Genetic algorithm Improved fuzzy clustering Manuals Non-intrusive load monitoring Optimization Wavelet analysis
ISSN:	2158-5709
Online-Zugang:	Volltext
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