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Power Load Pattern Classification from AMR Data

AMR 데이터에서의 전력 부하 패턴 분류

  • Piao, Minghao (Database/Bioinformatics Laboratory, Chungbuk National University) ;
  • Park, Jin-Hyung (Database/Bioinformatics Laboratory, Chungbuk National University) ;
  • Lee, Heon-Gyu (Database/Bioinformatics Laboratory, Chungbuk National University) ;
  • Shin, Jin-Ho (Power Information Technology Group, Korea Electric Power Research Institute) ;
  • Ryu, Keun-Ho (Database/Bioinformatics Laboratory, Chungbuk National University)
  • ;
  • 박진형 (충북대학교 데이터베이스/바이오인포매틱스 연구실) ;
  • 이헌규 (충북대학교 데이터베이스/바이오인포매틱스 연구실) ;
  • 신진호 (한국전력연구원 전력 정보 기술 그룹) ;
  • 류근호 (충북대학교 데이터베이스/바이오인포매틱스 연구실)
  • Published : 2008.05.16

Abstract

Currently an automated methodology based on data mining techniques is presented for the prediction of customer load patterns in load demand data. The main aim of our work is to forecast customers' contract information from capacity of daily power consumption patterns. According to the result, we try to evaluate the contract information's suitability. The proposed our approach consists of three stages: (i) data preprocessing: noise or outlier is detected and removed (ii) cluster analysis: SOMs clustering is used to create load patterns and the representative load profiles and (iii) classification: we applied the K-NNs classifier in order to predict the customers' contract information base on power consumption patterns. According to the our proposed methodology, power load measured from AMR(automatic meter reading) system, as well as customer indexes, were used as inputs. The output was the classification of representative load profiles (or classes). Lastly, in order to evaluate KNN classification technique, the proposed methodology was applied on a set of high voltage customers of the Korea power system and the results of our experiments was presented.

Keywords