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Apply Graph Signal Processing on NILM: An Unsupervised Approach Featuring Power Sequences.

Bochao Zhao1, Xuhao Li1, Wenpeng Luan1

  • 1School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China.

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Summary

This study introduces a new unsupervised method for non-intrusive load monitoring (NILM) using state transition sequences (STS). This approach improves appliance energy disaggregation accuracy without extra sensors.

Keywords:
dynamic time warpinggraph signal processingnon–intrusive load monitoringstate transition sequences

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Area of Science:

  • Electrical Engineering
  • Computer Science
  • Signal Processing

Background:

  • Non-intrusive load monitoring (NILM) provides appliance-level electricity insights without additional sensors.
  • Existing unsupervised graph signal processing (GSP) methods for NILM can be improved through enhanced feature selection.

Purpose of the Study:

  • To propose a novel unsupervised GSP-based NILM approach (STS-UGSP) utilizing state transition sequences (STS) for improved feature extraction.
  • To enhance the performance of NILM by introducing a new feature representation and matching algorithm.

Main Methods:

  • Extraction of state transition sequences (STS) from power readings.
  • Graph generation using dynamic time warping distances between STSs for similarity quantification.
  • A forward-backward power STS matching algorithm for operational cycle analysis.

Main Results:

  • The proposed STS-UGSP method was validated on three public datasets.
  • STS-UGSP outperformed four benchmark methods in two evaluation metrics.
  • The approach provided more accurate appliance energy consumption estimates compared to ground truth.

Conclusions:

  • The novel STS-UGSP approach offers a promising advancement in unsupervised NILM.
  • Feature extraction using STS enhances the accuracy of load disaggregation.
  • This method provides a cost-effective solution for demand-side management.