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NILM Techniques for Intelligent Home Energy Management and Ambient Assisted Living: A Review

1
Faculty of Science & Technology, University of Algarve, 8005-294 Faro, Portugal
2
IDMEC, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal
3
Department of Electronics, University of Alcalá, 28805 Madrid, Spain
4
CISUC, University of Coimbra, 3030-290 Coimbra, Portugal
*
Author to whom correspondence should be addressed.
Energies 2019, 12(11), 2203; https://doi.org/10.3390/en12112203
Received: 30 March 2019 / Revised: 30 April 2019 / Accepted: 6 June 2019 / Published: 10 June 2019
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Abstract

The ongoing deployment of smart meters and different commercial devices has made electricity disaggregation feasible in buildings and households, based on a single measure of the current and, sometimes, of the voltage. Energy disaggregation is intended to separate the total power consumption into specific appliance loads, which can be achieved by applying Non-Intrusive Load Monitoring (NILM) techniques with a minimum invasion of privacy. NILM techniques are becoming more and more widespread in recent years, as a consequence of the interest companies and consumers have in efficient energy consumption and management. This work presents a detailed review of NILM methods, focusing particularly on recent proposals and their applications, particularly in the areas of Home Energy Management Systems (HEMS) and Ambient Assisted Living (AAL), where the ability to determine the on/off status of certain devices can provide key information for making further decisions. As well as complementing previous reviews on the NILM field and providing a discussion of the applications of NILM in HEMS and AAL, this paper provides guidelines for future research in these topics. View Full-Text
Keywords: non-intrusive load monitoring; home energy management systems; ambient assisted living; demand response; machine learning; internet of things; smart grids non-intrusive load monitoring; home energy management systems; ambient assisted living; demand response; machine learning; internet of things; smart grids
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Ruano, A.; Hernandez, A.; Ureña, J.; Ruano, M.; Garcia, J. NILM Techniques for Intelligent Home Energy Management and Ambient Assisted Living: A Review. Energies 2019, 12, 2203.

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