Assessment metrics for unsupervised non-intrusive load disaggregation learning algorithms

Abstract

Non-intrusive appliance load monitoring (NILM) is the process for disaggregating total electricity consumption into its contributing appliances. Unsupervised NILM algorithm is an attractive method as the need for data annotation can be eliminated. In order to evaluate the performance of unsupervised NILM learning algorithm, most of the research work evaluates the algorithm performance using accuracy type metrics. These assessment metrics can not only identify total disaggregation error, but also distinguish the disaggregation error of each other single appliance. In order to better quantify the nature of disaggregation algorithm, we propose two assessment metrics: the total disaggregation accuracy and the disaggregation accuracy for a single appliance. In this paper, we evaluate the performance of unsupervised disaggregation methods using these assessment metrics. The experiment results show that our assessment metrics can evaluate the unsupervised algorithms effectively.