Fig. 2

A U-shaped deep convolutional neural network for appliance detection. The input energy consumption matrix is represented by a green layer on the upper left corner and the data flow inside the model is indicated by arrows. Then, the input data would be processed by successive convolution blocks (yellow blocks) for feature extraction. Red layers transit and downscale the output of the previous convolution block with a convolutional layer and a MaxPooling layer reducing data amount; deconvolutional layers (blue layers) reconstruct the feature map using the previous convolution block output. Also, the model concatenates the down-sampled feature maps and up-sampled feature maps upgrading the reconstruction performance, indicated by ball symbols in the graph. After that, the processed feature map would be reformed with the same size as the input matrix, and every data point would be predicted by the sigmoid classifier (magenta layer). Finally, the output matrix at the upper right corner of the plot would present the binary prediction of each data point by using “0” (state “off”) and “1” (state “on”)