Title :
A Survey on Intrusive Load Monitoring for Appliance Recognition
Author :
Ridi, A. ; Gisler, C. ; Hennebert, J.
Author_Institution :
IcoSys Inst., Univ. of Appl. Sci. Western Switzerland, Fribourg, Switzerland
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 low-end 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.
Keywords :
domestic appliances; feature extraction; learning (artificial intelligence); load management; power engineering computing; ILM applications; appliance recognition; electricity load monitoring; equipment monitoring; feature extraction; human activity recognition; intrusive load monitoring; machine learning algorithms; nonintrusive load monitoring; survey; Conferences; Databases; Electricity; Feature extraction; Home appliances; Logic gates; Monitoring;
Conference_Titel :
Pattern Recognition (ICPR), 2014 22nd International Conference on
Conference_Location :
Stockholm
DOI :
10.1109/ICPR.2014.636