A residual convolutional neural network with multi-block for appliance recognition in non-intrusive load identification

Abstract

Non-intrusive load monitoring (NILM) is a promising technique for energy consumption monitoring that can recognize load states and appliance types without relying on excessive sensing meters. With the development of the Internet of Things in intelligent buildings, the NILM technique will have broad application prospects. According to the different characteristics of load electrical signals, this work constructs 2D load signatures, including building the weighted voltage–current (WVI ) trajectory image, Markov Transition Field (MTF) image, and current spectral sequence-based GAF (I-GAF) image. Furthermore, a deep learning model named Residual Convolutional Neural Network with Energy-normalization and Squeeze-and-excitation blocks (EN-SE-RECNN) is proposed to mine information on the constructed load signatures and realize the appliance identification task. The accuracy of the proposed method on PLAID, WHITED, and HRAD datasets reached 97.43%, 95.99%, and 98.14%, respectively. And it shows that the proposed method significantly improves the recognition performance compared to existing methods.