State of art overview of Non-Intrusive Load Monitoring applications in smart grids

Abstract

Nowadays, non-intrusive load monitoring (NILM) systems represent an effective alternative to monitoring individual appliances' consumption, avoiding the costs and spatial constraints imposed by the installation of additional sensors. Modern artificial intelligence algorithms, such as machine learning algorithms, make it possible to split the aggregate power absorption profile of a user into the individual power absorption profiles of the main household appliances. However, there is still no full awareness of how these systems can be used effectively and in what situations they can provide consistent support.This paper illustrates the most promising management, diagnostics, and automation activities that can be carried out with the help of an efficient NILM system, referring to the most significant works in literature. A discussion of challenges and future research directions is also provided.

Keywords

• Non-intrusive load monitoring (NILM)
• Energy management
• Smart grid
• Anomaly detection
• Ambient assisted living (AAL)
• Demand response (DR)