Scale- and Context-Aware Convolutional Non-intrusive Load Monitoring

TL;DR

This paper introduces a novel neural network architecture for non-intrusive load monitoring that leverages multi-scale features, contextual information, and adversarial training to significantly improve energy disaggregation accuracy.

Contribution

The paper proposes a scale- and context-aware neural network with multi-branch architecture, self-attention, and adversarial loss, advancing the state-of-the-art in energy disaggregation.

Findings

Significantly outperforms existing methods on open datasets.

Effective use of multi-scale and contextual features improves accuracy.

Adversarial training enhances model robustness.

Abstract

Non-intrusive load monitoring addresses the challenging task of decomposing the aggregate signal of a household's electricity consumption into appliance-level data without installing dedicated meters. By detecting load malfunction and recommending energy reduction programs, cost-effective non-intrusive load monitoring provides intelligent demand-side management for utilities and end users. In this paper, we boost the accuracy of energy disaggregation with a novel neural network structure named scale- and context-aware network, which exploits multi-scale features and contextual information. Specifically, we develop a multi-branch architecture with multiple receptive field sizes and branch-wise gates that connect the branches in the sub-networks. We build a self-attention module to facilitate the integration of global context, and we incorporate an adversarial loss and on-state augmentation to further improve the model's performance. Extensive simulation results tested on open datasets corroborate the merits of the proposed approach, which significantly outperforms state-of-the-art methods.