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Frequency Selective Auto-Encoder for Smart Meter Data Compression.

Jihoon Lee1, Seungwook Yoon2, Euiseok Hwang1

  • 1School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), 123 Cheomdangwagi-ro, Buk-gu, Gwangju 61005, Korea.

Sensors (Basel, Switzerland)
|March 6, 2021
PubMed
Summary

This study introduces a frequency-selective auto-encoder (AE) model for compressing smart meter data. The new method enhances data reconstruction quality and reduces computational load for intelligent power grids.

Keywords:
auto-encoderdata compressiondigital signal processingsmart meter

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Area of Science:

  • Electrical Engineering
  • Data Science
  • Artificial Intelligence

Background:

  • The Internet of Things (IoT) enables intelligent power grids with smart meters, generating vast data for improved grid visibility.
  • Limited storage and communication capacities of IoT devices necessitate efficient data compression techniques.
  • Existing deep learning auto-encoder (AE) models struggle with varying spectral properties in high-frequency power data, limiting compression performance.

Purpose of the Study:

  • To propose an improved auto-encoder (AE) compression model for smart meter data in IoT environments.
  • To enhance reconstruction quality and maintain compression ratio (CR) for time-varying power data.
  • To reduce computational complexity in smart grid data analysis.

Main Methods:

  • A frequency-selective auto-encoder (AE) compression model is proposed, utilizing a frequency selection method.
  • Power data is divided into time windows based on spectral properties (high, medium, low frequency).
  • Separate AE models are trained and selectively applied to data windows, optimizing compression for specific spectral characteristics.

Main Results:

  • The frequency-selective AE model demonstrates significantly higher reconstruction performance compared to existing models at the same compression ratio (CR).
  • Simulations on the Dutch residential energy dataset validate the model's effectiveness.
  • The proposed model reduces computational complexity associated with the learning process analysis.

Conclusions:

  • The frequency-selective AE model offers a superior approach to compressing smart meter data, addressing limitations of current methods.
  • This technique improves the efficiency and effectiveness of data handling in intelligent power grids.
  • The model provides a scalable solution for managing large datasets in IoT-enabled energy systems.