A Survey on Intrusive Load Monitoring for Appliance Recognition

Abstract

Electricity load monitoring of appliances has become an important task considering the recent economic and ecological trends. In this game, machine learning has an important part to play, allowing for energy consumption understanding, critical equipment monitoring and even human activity recognition. This paper provides a survey of current researches on Intrusive Load Monitoring (ILM) techniques. ILM relies on lowend electricity meter devices spread inside the habitations, as opposed to Non-Intrusive Load Monitoring (NILM) that relies on an unique point of measurement, the smart meter. Potential applications and principles of ILMs are presented and compared to NILM. A focus is also given on feature extraction and machine learning algorithms typically used for ILM applications.

Electric current based power line communication for plug-load device auto identification

“…They are trying plug-load device auto identification with data analysis technology. This is a hot research topic, called as non intrusive load monitoring (NILM), includes plug-load auto identification function typically [10] [12]. Other works such as [3], [4] and [9] also focus on plug-load auto identification from time-series data analysis.…”

“…Inspired by the works of non-intrusive load monitoring (NILM [10] [12]), researchers have studied on the "features" of energy consumption [3] [9] (i.e., pattern of energy usage), trying to identify the device connected to the outlet. We call this approach as passive scheme.…”

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

“…The term intrusive means that the metering device is located in the habitation, close to the appliance being monitored. As mentioned in [37] there are subclassifications depending on the level of intrusion:…”

Load Monitoring Based on the Auxiliary Particle Filter Algorithm

“…The advantage of the method is that the monitoring data is accurate and reliable. The disadvantage is that the practical operability is poor, the implementation cost is high and the user acceptance is low [1,3].…”

“…Compared with the intrusion load monitoring, NILM' s economic investment is small [3,9]. In recent years, with the rapid development of smart home and smart grid, NILM research boom in the world once again rise.…”