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Non-Intrusive Load Monitoring of Buildings Using Spectral Clustering
2022
Sensors
With widely deployed smart meters, non-intrusive energy measurements have become feasible, which may benefit people by furnishing a better understanding of appliance-level energy consumption. This work is a step forward in using graph signal processing for non-intrusive load monitoring (NILM) by proposing two novel techniques: the spectral cluster mean (SC-M) and spectral cluster eigenvector (SC-EV) methods. These methods use spectral clustering for extracting individual appliance energy usage
doi:10.3390/s22114036
pmid:35684657
pmcid:PMC9185269
fatcat:xizzzun4effzxllsfn7ienrud4