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Intelligent retrieval method for power grid operation data based on improved SimHash and multi-attribute decision

Songyan Zhao1, Xiaoli Guo2, Zhaoyang Qu2

  • 1School of Computer Science, Northeast Electric Power University, Jilin, China. 1032322086@qq.com.

Scientific Reports
|December 5, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an intelligent retrieval method for power grid operation data using improved SimHash and multi-attribute decision making. The approach significantly enhances search accuracy and speed for critical energy data analysis.

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Area of Science:

  • Electrical Engineering
  • Computer Science
  • Data Science

Background:

  • The energy revolution necessitates advanced power systems integrating electric and computing power.
  • Traditional data retrieval methods for power grids are inefficient, hindering real-time analysis and decision-making.
  • Vast amounts of power grid data require faster, more accurate, and personalized access.

Purpose of the Study:

  • To propose an intelligent retrieval approach for power grid operation data.
  • To address the limitations of traditional directory-based queries in handling large-scale power data.
  • To improve the efficiency and accuracy of accessing critical power grid information.

Main Methods:

  • Utilizing an improved SimHash algorithm for data feature extraction.
  • Employing multi-attribute decision-making for enhanced data ranking and selection.
  • Designing an intelligent parallel retrieval algorithm (MR-ST) based on the MapReduce model.
  • Analyzing real-time grid operation data on a cloud platform.

Main Results:

  • The proposed method demonstrates significant improvements in search accuracy rate.
  • The search completion rate is notably enhanced compared to traditional approaches.
  • Search time is considerably reduced, facilitating real-time data availability.
  • Experimental validation confirms the method's effectiveness and precision.

Conclusions:

  • The developed intelligent retrieval system effectively addresses the challenges of accessing power grid operation data.
  • The integration of SimHash and multi-attribute decision making offers a superior solution for data retrieval.
  • The MR-ST algorithm shows promise for real-time, large-scale power data analysis in cloud environments.