LAUSR

Appliance identification and classification are primary requirement for successful deployment of demand side management in smart grid. Traditionally, load identification is carried out after extracting handcrafted features from electrical signals followed by classification algorithms, which is laborious and time consuming. To nullify the abovementioned shortcomings, this article proposes a 1-D convolutional neural network (CNN) with sinusoidal kernel initializers for classification of domestic appliances operating individually or in combination with other devices. For efficient operation, 15 primary and 225 multiscale sinusoidal kernels (MSK) are formed and included in the CNN architecture for extracting important discriminative features from the current signals. Classification accuracy of the proposed CNN module, named as MSK-CNN, is further improved by adding a new nonlinear activation function (AF), SL-ReLU, having an adjustable logarithmic function along with softsign and ReLU functions. The efficacy of MSK-CNN is tested on a practical dataset, created by the authors, consists current signals of different real life residential appliances. It is showed that MSK-CNN can successfully distinguish 18 single and 12 appliance combinations, and can attain an accuracy of 98.61%. The experimental outcomes reveal superiority of the MSK-CNN over classical CNN modules with other kernel initializers and AFs in terms of classification accuracy and training convergence.