Non-intrusive load monitoring approaches for disaggregated energy sensing: a survey.

Zoha, Ahmed, Gluhak, Alexander, Imran, Muhammad Ali and Rajasegarar, Sutharshan 2012, Non-intrusive load monitoring approaches for disaggregated energy sensing: a survey., Sensors, vol. 12, no. 12, pp. 16838-16866, doi: 10.3390/s121216838.

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Title Non-intrusive load monitoring approaches for disaggregated energy sensing: a survey.
Author(s) Zoha, Ahmed
Gluhak, Alexander
Imran, Muhammad Ali
Rajasegarar, SutharshanORCID iD for Rajasegarar, Sutharshan
Journal name Sensors
Volume number 12
Issue number 12
Start page 16838
End page 16866
Total pages 29
Publisher Multidisciplinary Digital Publishing Institute (MDPI)
Place of publication Basel, Switzerland
Publication date 2012
ISSN 1424-8220
Keyword(s) Non-Intrusive Load Monitoring (NILM)
Intrusive Load Monitoring (ILM)
disaggregation algorithms
load signatures
energy management
Summary Appliance Load Monitoring (ALM) is essential for energy management solutions, allowing them to obtain appliance-specific energy consumption statistics that can further be used to devise load scheduling strategies for optimal energy utilization. Fine-grained energy monitoring can be achieved by deploying smart power outlets on every device of interest; however it incurs extra hardware cost and installation complexity. Non-Intrusive Load Monitoring (NILM) is an attractive method for energy disaggregation, as it can discern devices from the aggregated data acquired from a single point of measurement. This paper provides a comprehensive overview of NILM system and its associated methods and techniques used for disaggregated energy sensing. We review the state-of-the art load signatures and disaggregation algorithms used for appliance recognition and highlight challenges and future research directions.
Language eng
DOI 10.3390/s121216838
Field of Research 030199 Analytical Chemistry not elsewhere classified
090699 Electrical and Electronic Engineering not elsewhere classified
080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1.1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2012, Multidisciplinary Digital Publishing Institute (MDPI)
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