Data-Driven Strategy for Appliance Identification Using Phase-Space Reconstruction

The present work proposes to utilize a data-driven technique, namely, phase space reconstruction (PSR), followed by an exploration of multiple delays, which, in turn, enables the generation of multiple distinct image sets for each of the load signatures without involving data-normalization. Considering the multiple generated images, individual convolutional neural networks (CNNs) for each of the images are considered, resulting in variegated predictions which are effectively combined within a majority voting framework in order to limit the erroneous predictions of the individual classifiers. The proposed strategy leads to significant improvement in classification accuracy compared to the relevant existing literature.