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Energy disaggregation (a.k.a nonintrusive load monitoring, NILM), asingle-channel blind source separation problem, aims to decompose the mainswhich records the whole house electricity consumption into appliance-wisereadings. This problem is difficult because it is inherently unidentifiable.Recent approaches have shown that the identifiability problem could be reducedby introducing domain knowledge into the model. Deep neural networks have beenshown to be a promising approach for these problems, but sliding windows arenecessary to handle the long sequences which arise in signal processingproblems, which raises issues about how to combine predictions from differentsliding windows. In this paper, we propose sequence-to-point learning, wherethe input is a window of the mains and the output is a single point of thetarget appliance. We use convolutional neural networks to train the model.Interestingly, we systematically show that the convolutional neural networkscan inherently learn the signatures of the target appliances, which areautomatically added into the model to reduce the identifiability problem. Weapplied the proposed neural network approaches to real-world household energydata, and show that the methods achieve state-of-the-art performance, improvingtwo standard error measures by 84% and 92%.

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