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Found 4 papers, 2 papers with code
Non-Intrusive Load Monitoring with Fully Convolutional Networks
Non-intrusive load monitoring or energy disaggregation involves estimating the power consumption of individual appliances from measurements of the total power consumption of a home.
Sequence-to-point learning with neural networks for nonintrusive load monitoring
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.
Latent Bayesian melding for integrating individual and population models
In many statistical problems, a more coarse-grained model may be suitable for population-level behaviour, whereas a more detailed model is appropriate for accurate modelling of individual behaviour.
Signal Aggregate Constraints in Additive Factorial HMMs, with Application to Energy Disaggregation
Blind source separation problems are difficult because they are inherently unidentifiable, yet the entire goal is to identify meaningful sources.
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