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Energy Disaggregation Using Elastic Matching Algorithms.

Pascal A Schirmer1, Iosif Mporas1, Michael Paraskevas2

  • 1Communications and Intelligent Systems Group, School of Engineering and Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UK.

Entropy (Basel, Switzerland)
|December 8, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces an energy disaggregation architecture using elastic matching algorithms. The minimum variance matching algorithm demonstrated superior performance, enhancing accuracy by 2.7% compared to the baseline.

Keywords:
dynamic time warpingelastic matching algorithmsenergy disaggregationminimum variance matchingnon-intrusive load monitoring (NILM)

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Area of Science:

  • Energy systems analysis
  • Computational intelligence

Background:

  • Energy disaggregation is crucial for energy management.
  • Machine learning methods require extensive training data.
  • Elastic matching offers a data-efficient alternative.

Purpose of the Study:

  • To present an energy disaggregation architecture utilizing elastic matching algorithms.
  • To evaluate the performance of various elastic matching algorithms for energy disaggregation.
  • To identify the most effective elastic matching algorithm for this task.

Main Methods:

  • Developed an energy disaggregation architecture employing template matching.
  • Utilized a database of reference energy consumption signatures.
  • Compared five different elastic matching algorithms on diverse datasets.

Main Results:

  • The minimum variance matching algorithm outperformed other evaluated elastic matching algorithms.
  • This algorithm achieved a 2.7% improvement in energy disaggregation accuracy over the dynamic time warping baseline.
  • Elastic matching avoids complex model training processes.

Conclusions:

  • Elastic matching algorithms provide an effective approach for energy disaggregation.
  • The minimum variance matching algorithm is highly suitable for energy disaggregation tasks.
  • This method offers a viable alternative to data-intensive machine learning models.