Improving Non-Intrusive Load Disaggregation through an Attention-Based Deep Neural Network

Abstract

Energy disaggregation, known in the literature as Non-Intrusive Load Monitoring (NILM), is the task of inferring the power demand of the individual appliances given the aggregate power demand recorded by a single smart meter which monitors multiple appliances. In this paper, we propose a deep neural network that combines a regression subnetwork with a classification subnetwork for solving the NILM problem. Specifically, we improve the generalization capability of the overall architecture by including an encoder-decoder with a tailored attention mechanism in the regression subnetwork. The attention mechanism is inspired by the temporal attention that has been successfully applied in neural machine translation, text summarization, and speech recognition. The experiments conducted on two publicly available datasets--REDD and UK-DALE--show that our proposed deep neural network outperforms the state-of-the-art in all the considered experimental conditions. We also show that modeling attention translates into the network's ability to correctly detect the turning on or off an appliance and to locate signal sections with high power consumption, which are of extreme interest in the field of energy disaggregation.

References in corpus (7)

• Sequence to Sequence Learning with Neural Networks
• 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
• NILMTK: An Open Source Toolkit for Non-intrusive Load Monitoring
• Scale- and Context-Aware Convolutional Non-intrusive Load Monitoring
• Non-Intrusive Energy Disaggregation Using Non-negative Matrix Factorization with Sum-to-k Constraint
• Subtask Gated Networks for Non-Intrusive Load Monitoring

Cited by in corpus (2)

• Mixed-Integer Nonlinear Programming for State-based Non-Intrusive Load Monitoring
• A Scoping Review of Energy Load Disaggregation