Compressive Sampling for Non-Intrusive Appliance Load Monitoring (NALM) using Current Waveforms

Ying Wang, Alessio Filippi, Ronald Rietman, and Geert Leus


Estimation of Signal Parameters, Compressive Sampling


We propose a NALM technique by exploiting the compressive sampling and sparse reconstruction framework. We estimate the contribution of the individual appliances by measuring the current of the total load. We further assume to know the steady-state current waveform of each appliance. We exploit the sparsity of the current signal to compress the measurement via random sampling, which lowers significantly the processing complexity, the storage and the communication burden. Using the proposed sparse reconstruction approach, we can still identify the on/off status of each appliance from the compressed measurement as if the original non-compressed measurement is used.

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