Sequence-to-point learning with neural networks for nonintrusive load monitoring

TL;DR

This paper introduces a sequence-to-point neural network approach for nonintrusive load monitoring, effectively learning appliance signatures and significantly improving energy disaggregation accuracy.

Contribution

It proposes a novel sequence-to-point learning method using CNNs that inherently captures appliance signatures, reducing the identifiability problem in NILM.

Findings

Achieves state-of-the-art performance on real household data

Reduces error measures by 84% and 92%

Demonstrates CNNs can learn appliance signatures automatically

Abstract

Energy disaggregation (a.k.a nonintrusive load monitoring, NILM), a single-channel blind source separation problem, aims to decompose the mains which records the whole house electricity consumption into appliance-wise readings. This problem is difficult because it is inherently unidentifiable. Recent approaches have shown that the identifiability problem could be reduced by introducing domain knowledge into the model. Deep neural networks have been shown to be a promising approach for these problems, but sliding windows are necessary to handle the long sequences which arise in signal processing problems, which raises issues about how to combine predictions from different sliding windows. In this paper, we propose sequence-to-point learning, where the input is a window of the mains and the output is a single point of the target appliance. We use convolutional neural networks to train the model. Interestingly, we systematically show that the convolutional neural networks can inherently learn the signatures of the target appliances, which are automatically added into the model to reduce the identifiability problem. We applied the proposed neural network approaches to real-world household energy data, and show that the methods achieve state-of-the-art performance, improving two standard error measures by 84% and 92%.