Multilabel Appliance Classification With Weakly Labeled Data for Non-Intrusive Load Monitoring

Abstract

Non-Intrusive Load Monitoring consists in estimating the power consumption or the states of the appliances using electrical parameters acquired from a single metering point. State-of-the-art approaches are based on deep neural networks, and for training, they require a significant amount of data annotated at the sample level, defined as strong labels. This paper presents an appliance classification method based on a Convolutional Recurrent Neural Network trained with weak supervision. Learning is formulated as a Multiple-Instance Learning problem, and the network is trained on labels provided for an entire segment of the aggregate power, defined as weak labels. Weak labels are coarser annotations that are intrinsically less costly to obtain compared to strong labels. An extensive experimental evaluation has been conducted on the UK-DALE and REFIT datasets comparing the proposed approach to three benchmark methods. The results obtained for different amounts of strongly and weakly labeled data and mixing UK-DALE and REFIT confirm the effectiveness of weak labels compared to fully supervised and semi-supervised benchmarks methods.