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클러스터링 기법을 이용한 수용가별 전력 데이터 패턴 분석
유승형,김홍석,오도은,노재구,Ryu, Seunghyoung,Kim, Hongseok,Oh, Doeun,No, Jaekoo 한국전력공사 2016 KEPCO Journal on electric power and energy Vol.2 No.1
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.