Research on Non-Intrusive Load Disaggregation Technology Based on VMD–Nyströmformer–BiTCN

Abstract

Non-intrusive load disaggregation is a technique that monitors the total electrical load of an entire building or household. It uses a single power metering device to measure the total load. Then, it employs algorithms to break it down into the individual usage of different electrical devices. To address issues in load disaggregation models such as long training times, feature interference caused by the activation of other loads, and accuracy deficiencies caused by behavioral interference from users’ electricity usage habits, this paper proposes a VMD–Nyströmformer–BiTCN network architecture. The variational mode decomposition (VMD) filters the raw power data, reducing errors caused by noise and enhancing the accuracy of decomposing the load. A deep learning network utilizes a modified attention model, Nyströmformer, to reduce feature entanglement and accuracy degradation caused by habitual behavior interference during load disaggregation, while ensuring precise accuracy and improving network operational speed. The training network uses a bidirectional temporal convolutional network (BiTCN) and incorporates a residual network to expand the receptive field, allowing it to receive longer load sequence data and acquire more effective load information, thereby improving the disaggregation effectiveness for target appliances.