Non Intrusive Load Monitoring (NILM): A State of the Art

Abstract

Abstract. The recent increase in smart meters installations in households and small bussiness by electric companies has led to interest in monitoring load techniques in order to provide better quality service and get useful information about appliance usage and user consumption behavior. This works summarizes the current state of the art in Non Intrusive Load Monitoring from its beginning, describes the main process followed in the literature to perform this technique and shows current methods and techniques followed nowadays. The possible application of this techniques in the context of ambient intelligence, energy efficiency, occupancy detection are described. This work also points the current challenges in the field and the future lines of research in this broad topic.

Exploiting Smart Meter Power Consumption Measurements for Human Activity Recognition (HAR) with a Motif-Detection-Based Non-Intrusive Load Monitoring (NILM) Approach

“…By 22 August 2019, Liu [ 4 ] identified 617 relevant scientific documents in the field of NILM. Most of the work focuses on breaking down the energy consumption of individual household appliances and, thus, motivating the residents to save energy in order to reduce emissions [ 1 , 2 , 4 , 10 ].…”

“…Further approaches exist that use NILM as a Home Energy Management System (HEMS) to compensate peak loads and, thus, ensure grid stability (e.g., charging the batteries of electric cars primarily in off-peak times) [ 2 , 11 ], to detect appliance malfunction [ 1 , 11 ], to detect energy theft [ 1 ], or for HAR in the AAL domain [ 1 , 12 , 13 , 14 , 15 , 16 ].…”

“…To provide residents with feedback about the power consumption of the appliances in their households, and to motivate them to save energy, numerous organizations and researchers are working on methods to monitor household power consumption, in detail, at the appliance level [ 1 , 2 , 3 , 4 ]. These methods are called Appliance Load Monitoring (ALM).…”

“…NILM is more cost-effective and easier to deploy than ILM because it requires only a single sense point, but disaggregating the data is a current challenge. Due to many different appliances that can be present in a household, and the overlapping of different appliances in the total power consumption, it is still not possible to fully disaggregate the total power consumption of a household [ 1 , 2 , 3 , 4 , 5 ].…”

Reduce Cost Smart Power Management System by Utilize Single Board Computer Artificial Neural Networks for Smart Systems

“…In [12], another energy management method introduced for Hold Loads as well as large industrial loads. The Non-Intrusive Load Monitoring (NILM) algorithm presented by really actualizing submetering system for each store to estimate its modern improvement dependent on receptacle pressing calculations and criticism frameworks constrained by the Artificial Intelligence (AI) Calculation to finish up with a vitality proficient keen home and savvy lattices.…”