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

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.

arXiv commentAccepted by IEEE Transactions on Power Systems

References in corpus (11)

• Neural Machine Translation by Jointly Learning to Align and Translate
• Spectral Normalization for Generative Adversarial Networks
• Self-Attention Generative Adversarial Networks
• Generative Adversarial Text to Image Synthesis
• The UK-DALE dataset, domestic appliance-level electricity demand and whole-house demand from five UK homes
• Neural NILM: Deep Neural Networks Applied to Energy Disaggregation
• VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning
• MidiNet: A Convolutional Generative Adversarial Network for Symbolic-domain Music Generation
• Sequence-to-point learning with neural networks for nonintrusive load monitoring
• Non-Intrusive Load Monitoring with Fully Convolutional Networks
• Scaling Factorial Hidden Markov Models: Stochastic Variational Inference without Messages

Cited by in corpus (2)

• Improving Non-Intrusive Load Disaggregation through an Attention-Based Deep Neural Network
• A Unifying Framework of Attention-based Neural Load Forecasting