A Novel Feature Extraction Method for Nonintrusive Appliance Load Monitoring

Improving energy efficiency by monitoring household electrical consumption is of significant importance with the climate change concerns of the present time. A solution for the electrical consumption management problem is the use of a nonintrusive appliance load monitoring (NIALM) system. This system captures the signals from the aggregate consumption, extracts the features from these signals and classifies the extracted features in order to identify the switched-on appliances. This paper focuses solely on feature extraction through applying the matrix pencil method, a well-known parametric estimation technique, to the drawn electric current. The result is a compact representation of the current signal in terms of complex numbers referred to as poles and residues. These complex numbers are shown to be characteristic of the considered load and can thus serve as features in any subsequent classification module. In the absence of noise, simulations indicate an almost perfect agreement between theoretical and estimated values of poles and residues. For real data, poles and residues are used to determine a feature vector consisting of the contribution of the fundamental, the third, and the fifth harmonic currents to the maximum of the total load current. The result is a three-dimensional feature space with reduced intercluster overlap. 1. Introduction The reason behind the drive for the installation of smart meters in homes and businesses is that they facilitate for consumers to monitor their energy consumption, thereby making it easier for them to save energy, carbon emissions, and money. To help customers as well as utilities in the monitoring process, researchers have been studying load disaggregation schemes for more than two decades. One method of load disaggregation is distributed direct sensing which requires a sensor at each device or appliance in order to measure consumption. The one-sensor-per-device requirement is both the blessing and the curse of this method, for it is highly accurate but expensive. To overcome the limitations associated with the direct sensing approach, researchers have explored methods to infer disaggregated energy usage via a single sensor. Pioneering work in this area is non intrusive appliance load monitoring (NIALM), first introduced by Hart in the late 1980s [1]. In contrast to the direct sensing methods, NIALM relies solely on single-point measurements of voltage and current on the power feed entering the household. NIALM consists of four steps: data acquisition, event detection, feature extraction, and event