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Introducing MILM: A Hybrid Minimal-Intrusive Load Monitoring Approach: Poster

Published: 22 June 2021 Publication History

Abstract

The shift towards an advanced electricity metering infrastructure has gained traction because of several smart meter roll-outs. This accelerated research in Non-Intrusive Load Monitoring techniques. These techniques highly benefit from the temporal resolution improvements achieved by smart meters. Nevertheless, industrial adoption is low, not least because the achieved disaggregation performance is rather poor for unsupervised approaches. This work sketches a way to utilize intrusive sensors in combination with a standard NILM system to enhance training and maximize overall system's performance while minimizing the number of required intrusive sensors.

References

[1]
A. Reinhardt and C. Klemenjak. 2020. How does load disaggregation performance depend on data characteristics? insights from a benchmarking study. In Proceedings of the 11th ACM International Conference on Future Energy Systems. 167--177.
[2]
Tiago Serrenho, Paolo Zangheri, and Paolo Bertoldi. 2015. Energy Feedback Systems: Evaluation of Meta-studies on energy savings through feedback. Technical Report EUR 27992 EN. Publications Office of the European Union.
[3]
Benjamin Völker, Marc Pfeifer, Philipp M Scholl, and Bernd Becker. 2020. FIRED: A Fully-labeled hIgh-fRequency Electricity Disaggregation Dataset. In Proceedings of the 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation. 294--297.

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e-Energy '21: Proceedings of the Twelfth ACM International Conference on Future Energy Systems
June 2021
528 pages
ISBN:9781450383332
DOI:10.1145/3447555
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 June 2021

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Author Tags

  1. Minimal-Intrusive Load Monitoring
  2. NILM
  3. appliance classification

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  • Short-paper
  • Research
  • Refereed limited

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e-Energy '21

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Overall Acceptance Rate 160 of 446 submissions, 36%

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