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Analysis Process based on Modify K-means for Efficiency Improvement of Electric Power Data Pattern Detection

전력데이터 패턴 추출의 효율성 향상을 위한 변형된 K-means 기반의 분석 프로세스

  • Jung, Se Hoon (Dept. of Multimedia Eng., Sunchon National University (Gwangyang SW Convergence Institute)) ;
  • Shin, Chang Sun (Dept. of Information Communication and Multimedia Eng., Sunchon National University) ;
  • Cho, Yong Yun (Dept. of Information Communication and Multimedia Eng., Sunchon National University) ;
  • Park, Jang Woo (Dept. of Information Communication and Multimedia Eng., Sunchon National University) ;
  • Park, Myung Hye (Korea Electric Power Research Institute) ;
  • Kim, Young Hyun (Korea Electric Power Research Institute) ;
  • Lee, Seung Bae (Korea Electric Power Research Institute) ;
  • Sim, Chun Bo (Dept. of Information Communication and Multimedia Eng., Sunchon National University)
  • Received : 2017.06.13
  • Accepted : 2017.11.03
  • Published : 2017.12.31

Abstract

There have been ongoing researches to identify and analyze the patterns of electric power IoT data inside sensor nodes to supplement the stable supply of power and the efficiency of energy consumption. This study set out to propose an analysis process for electric power IoT data with the K-means algorithm, which is an unsupervised learning technique rather than a supervised one. There are a couple of problems with the old K-means algorithm, and one of them is the selection of cluster number K in a heuristic or random method. That approach is proper for the age of standardized data. The investigator proposed an analysis process of selecting an automated cluster number K through principal component analysis and the space division of normal distribution and incorporated it into electric power IoT data. The performance evaluation results show that it recorded a higher level of performance than the old algorithm in the cluster classification and analysis of pitches and rolls included in the communication bodies of utility poles.

Keywords

References

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