Improving energy efficiency by monitoring household electrical consumption is of significant importance with the climate change\r\nconcerns of the present time. A solution for the electrical consumptionmanagement problemis the use of a nonintrusive appliance\r\nload monitoring (NIALM) system. This system captures the signals from the aggregate consumption, extracts the features from\r\nthese signals and classifies the extracted features in order to identify the switched-on appliances. This paper focuses solely on\r\nfeature extraction through applying the matrix pencil method, a well-known parametric estimation technique, to the drawn electric\r\ncurrent. The result is a compact representation of the current signal in terms of complex numbers referred to as poles and residues.\r\nThese complex numbers are shown to be characteristic of the considered load and can thus serve as features in any subsequent\r\nclassification module. In the absence of noise, simulations indicate an almost perfect agreement between theoretical and estimated\r\nvalues of poles and residues. For real data, poles and residues are used to determine a feature vector consisting of the contribution\r\nof the fundamental, the third, and the fifth harmonic currents to the maximum of the total load current. The result is a threedimensional\r\nfeature space with reduced intercluster overlap.
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