Uncertainty quantification for appliance recognition in non-intrusive load monitoring using Bayesian deep learning

Abstract

Non-Intrusive Load Monitoring (NILM) can be used to detect, recognize, and classify switching events of individual electrical appliances from an aggregate power signal that is measured at the main line of the grid connection. A limitation of existing solutions is that deep learning models tend to overfit the data and do not express their uncertainty when making predictions. This paper shows that uncertainty infor-mation can be obtained in a natural way by making use of Bayesian Neural Networks. Having this infor-mation is very valuable, because it supplies relevant information about potential misclassifications of the model to an end-user. The source of these misclassifications can be attributed to ambiguous data, or the model requiring more examples to learn from. In this work, an increase in generalization performance is observed when making use of Stochastic Gradient Hamiltonian Monte Carlo over Stochastic Gradient des-cent, and the usefulness of uncertainty in a NILM context is discussed.(c) 2022 Elsevier B.V. All rights reserved.