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A density-based matrix transformation clustering method for electrical load.

Naiwen Li1, Xian Wu1, Jianjun Dong2,3

  • 1School of Business Administration, Liaoning Technical University, Huludao, Liaoning, China.

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|August 11, 2022
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Summary
This summary is machine-generated.

This study introduces a new Density-based Matrix Transformation (DBMT) clustering method for electrical load feature extraction. The novel approach accurately identifies peaks, valleys, and trends in load curves, outperforming existing methods.

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Area of Science:

  • Electrical Engineering
  • Data Science
  • Machine Learning

Background:

  • Accurate electrical load feature extraction is crucial for power companies.
  • Existing methods may struggle with the complex characteristics of raw electrical load data.

Purpose of the Study:

  • To propose a novel clustering algorithm, Density-based Matrix Transformation (DBMT), for electrical load feature extraction.
  • To develop a variant of Dynamic Time Warp (DTW) distance, dsDTW, to handle specific electrical load data characteristics.

Main Methods:

  • The proposed DBMT algorithm reorders data to group similar items, forming a block diagonal matrix.
  • A modified DTW distance (dsDTW) is introduced to align peaks, valleys, and trends while managing missing values.
  • The dsDTW distance matrix is used as input for the DBMT clustering.

Main Results:

  • The DBMT method adaptively determines the number of clusters and effectively filters noise without global parameters.
  • The combined dsDTW and DBMT approach demonstrates accurate feature extraction of electrical load curves.
  • Performance is validated against other clustering methods, showing superior results.

Conclusions:

  • The proposed dsDTW-DBMT method offers a robust solution for electrical load feature extraction.
  • This technique provides reliable insights for power companies by accurately identifying load curve patterns.
  • The algorithm's ability to handle missing data and noise enhances its practical applicability.