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Non-Intrusive Load Monitoring Approaches for Disaggregated Energy Sensing: A Survey

Sensors · 2012 · Vol. 12(12) · pp. 16838–16866
Ahmed ZohaAlexander GluhakMuhammad Ali ImranSutharshan Rajasegarar

Abstract

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.

Smart Grid Energy ManagementContext-Aware Activity Recognition SystemsIoT-based Smart Home SystemsComputer scienceEnergy consumptionEnergy (signal processing)Scheduling (production processes)Real-time computingEnergy managementLoad managementEfficient energy usePoint (geometry)Embedded system

MeSH terms

AlgorithmsEquipment DesignHumansRenewable Energy

Funding

  • Research Councils UK
  • University of Melbourne
  • University of Surrey
  • Arts and Humanities Research Council
  • Medical Research Council
  • Engineering and Physical Sciences Research Council
  • Economic and Social Research Council
Citations
928
FWCI
31.67
field-weighted impact
References
83
Percentile
100%
vs. same field & year
Citations per year
References
Load Signature Study—Part I: Basic Concept, Structure, and Methodology
IEEE Transactions on Power Delivery · 2009 · 435 citations
Nonintrusive appliance load monitoring
Proceedings of the IEEE · 1992 · 3,066 citations
Neural-Network-Based Signature Recognition for Harmonic Source Identification
IEEE Transactions on Power Delivery · 2005 · 444 citations
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