Customer Load Pattern Analysis using Clustering Techniques

클러스터링 기법을 이용한 수용가별 전력 데이터 패턴 분석

  • Ryu, Seunghyoung (Networking for Information Communications and Energy Lab, Sogang University) ;
  • Kim, Hongseok (Networking for Information Communications and Energy Lab, Sogang University) ;
  • Oh, Doeun (KEPCO Research Institute, Korea Electric Power Corporation) ;
  • No, Jaekoo (KEPCO Research Institute, Korea Electric Power Corporation)
  • Received : 2015.08.28
  • Accepted : 2015.12.14
  • Published : 2016.03.30


Understanding load patterns and customer classification is a basic step in analyzing the behavior of electricity consumers. To achieve that, there have been many researches about clustering customers' daily load data. Nowadays, the deployment of advanced metering infrastructure (AMI) and big-data technologies make it easier to study customers' load data. In this paper, we study load clustering from the view point of yearly and daily load pattern. We compare four clustering methods; K-means clustering, hierarchical clustering (average & Ward's method) and DBSCAN (Density-Based Spatial Clustering of Applications with Noise). We also discuss the relationship between clustering results and Korean Standard Industrial Classification that is one of possible labels for customers' load data. We find that hierarchical clustering with Ward's method is suitable for clustering load data and KSIC can be well characterized by daily load pattern, but not quite well by yearly load pattern.


Supported by : 기초전력연구원


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