Simultaneous Detection of Multiple Appliances From Smart-Meter Measurements via Multi-Label Consistent Deep Dictionary Learning and Deep Transform Learning

Abstract

Currently there are several well-known approaches to non-intrusive appliance load monitoring—rule based, stochastic finite state machines, neural networks, and sparse coding. Recently several studies have proposed a new approach based on multi-label classification. Different appliances are treated as separate classes, and the task is to identify the classes given the aggregate smart-meter reading. Prior studies in this area have used off-the-shelf algorithms like multi label ${K}$ nearest neighbor and random ${K}$ -label sets to address this problem. In this paper, we propose a deep learning-based technique. There are hardly any studies in deep learning based multi-label classification; two new deep learning techniques to solve the said problem are fundamental contributions of this paper. These are deep dictionary learning and deep transform learning. Thorough experimental results on benchmark datasets show marked improvement over existing studies.