Nonintrusive Load Disaggregation Based on Attention Neural Networks

Abstract

Nonintrusive load monitoring (NILM), also known as energy disaggregation, infers the energy consumption of individual appliances from household metered electricity data. Recently, NILM has garnered significant attention as it can assist households in reducing energy usage and improving their electricity behaviors. In this paper, we propose a two-subnetwork model consisting of a regression subnetwork and a seq2point-based classification subnetwork for NILM. In the regression subnetwork, stacked dilated convolutions are utilized to extract multiscale features. Subsequently, a self-attention mechanism is applied to the multiscale features to obtain their contextual representations. The proposed model, compared to existing load disaggregation models, has a larger receptive field and can capture crucial information within the data. The study utilizes the low-frequency UK-DALE dataset, released in 2015, containing timestamps, power of various appliances, and device state labels. House1 and House5 are employed as the training set, while House2 data is reserved for testing. The proposed model achieves lower errors for all appliances compared to other algorithms. Specifically, the proposed model shows a 13.85% improvement in mean absolute error (MAE), a 21.27% improvement in signal aggregate error (SAE), and a 26.15% improvement in F1 score over existing algorithms. Our proposed approach evidently exhibits superior disaggregation accuracy compared to existing methods.