Exploring CNN Model with Inrush Current Pattern for Non-Intrusive Load Monitoring

Non-Intrusive Load Monitoring (NILM) has gradually become a research focus in recent years to measure the power consumption in households for energy conservation. Most of the existing algorithms on NILM models independently measure when the total current load of appliances occurs, and NILM usually undergoes the problem of signatures of the appliance. This paper presents a distingue NILM design to measure and classify the appliances by investigating the inrush current pattern when the alliances begin. The proposed method is implemented while the five appliances operate simultaneously. The high sampling rate of field-programmable gate array (FPGA) is used to sample the inrush current, and then the current is converted to be image patterns using the kurtogram technique. These images are arranged to be four groups of data set depending on the number of appliances operating simultaneously. Furthermore, the five proposed modifications convolutional neural networks (CNN), which is based on very deep convolutional networks (VGGNet), are designed by adjusting the size to decrease the training time and increase faster operation. The proposed CNNs are then implement as a classification model to compare with the previous models. The F1 score and Recall are used to measure the accuracy classification. The results showed that the proposed system could be achieved at 99.06 accuracy classification.