Non-Intrusive Load Monitoring by Load Trajectory and Multi-Feature Based on DCNN
Hui Yin, Kaile Zhou, Shanlin Yang
Abstract
This article proposes a non-intrusive load monitoring (NILM) framework based on a deep convolutional neural network (DCNN) to profile each household appliance <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">on/off</small> status and the residential power consumption. It uses only load trajectory, which can overcome the limitations of existing voltage-current trajectory NILM techniques. The DCNN architecture with a load trajectory as the input enables the NILM to directly analyze the electricity consumption at the appliance-level. Meanwhile, the temporal feature transferring procedure improves load monitoring performance and extends its application range include monitoring appliances based on multiple and combined characteristics. Furthermore, the power variation augmentation technique enhances the load signature uniqueness. The fusion of temporal and power variation features provides rich identification information for NILM and improves the accuracy of appliance identification. Experimental results demonstrate that the proposed NILM framework is effective and superior for enhancing demand side management and energy efficiency.