Unsupervised algorithms for non-intrusive load monitoring: An up-to-date overview

Research on Smart Grids has recently focused on the energy monitoring issue, with the objective to maximize the user consumption awareness in building contexts on one hand, and to provide a detailed description of customer habits to the utilities on the other. One of the hottest topic in this field is represented by Non-Intrusive Load Monitoring (NILM): it refers to those techniques aimed at decomposing the consumption aggregated data acquired at a single point of measurement into the diverse consumption profiles of appliances operating in the electrical system under study. The focus here is on unsupervised algorithms, which are the most interesting and of practical use in real case scenarios. Indeed, these methods rely on a sustainable amount of a-priori knowledge related to the applicative context of interest, thus minimizing the user intervention to operate, and are targeted to extract all information to operate directly from the measured aggregate data. This paper reports and describes the most promising unsupervised NILM methods recently proposed in the literature, by dividing them into two main categories: load classification and source separation approaches. An overview of the public available dataset used on purpose and a comparative analysis of the algorithms performance is provided, together with a discussion of challenges and future research directions.