Abstract
In non-intrusive load monitoring, identification of electrical loads based on single point measurement of different energy related parameters plays a significant role. In literature, different conventional features such as true power, reactive power, RMS voltage, RMS current, phase angle and frequency in addition to the non-conventional features were employed. In addition, a variety of classifiers such as k-nearest neighbors (k-NN), support vector machine (SVM), random forest and Gaussian mixture models (GMM) have been employed. In this paper, we demonstrate that the classification performance strongly depends on the classifier and associated features selected. The experiments are performed on ACS-F2 Database of Appliance Consumption Signatures consisting of 225 devices belonging to 15 different categories.
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