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Handling Data Heterogeneity in Electricity Load Disaggregation via Optimized Complete Ensemble Empirical Mode

Kwok Tai Chui1, Brij B Gupta2,3,4, Ryan Wen Liu5

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Sensors (Basel, Switzerland)
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This study introduces a novel method to merge electricity load disaggregation (ELD) datasets, improving smart meter data analysis for energy conservation. The optimized complete ensemble empirical model decomposition and wavelet packet transform (OCEEMD-WPT) approach significantly enhances the signal-to-noise ratio.

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complete ensemble empirical mode decompositiondata heterogeneityelectricity load disaggregationnonintrusive load monitoringsmart gridsmart meterwavelet packet transform

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Area of Science:

  • Energy research
  • Artificial intelligence
  • Environmental science

Background:

  • Global warming necessitates carbon emission reduction.
  • Smart meters collect household electricity usage data.
  • Electricity load disaggregation (ELD) analyzes individual appliance consumption.

Purpose of the Study:

  • To propose a novel powerline noise transformation approach for merging ELD datasets.
  • To enhance the size and utility of training datasets for ELD models.
  • To improve the accuracy and reliability of electricity usage analysis.

Main Methods:

  • Developed an optimized complete ensemble empirical model decomposition and wavelet packet transform (OCEEMD-WPT) method.
  • Applied the OCEEMD-WPT approach to merge multiple ELD datasets.
  • Compared the proposed method against CEEMD-WPT, CEEMD, and WPT.

Main Results:

  • The OCEEMD-WPT approach significantly improved the signal-to-noise ratio (SNR).
  • Merging datasets increased training data size and facilitated cross-dataset utilization.
  • The method demonstrated superior performance compared to existing techniques.

Conclusions:

  • The proposed OCEEMD-WPT method is effective for merging ELD datasets.
  • This approach offers practical benefits for energy conservation efforts.
  • Enhanced ELD analysis contributes to a better understanding of electricity consumption patterns.