| Title |
Customer Clustering Method Using Repeated Small-sized Clustering to improve the Classifying Ability of Typical Daily Load Profile |
| Authors |
김영일(Kim, Young-Il) ; 송재주(Song, Jae-Ju) ; 오도은(Oh, Do-Eun) ; 정남준(Jung, Nam-Joon) ; 양일권(Yang, Il-Kwon) |
| Keywords |
Customer clustering ; K-means ; Typical Load Profile ; AMR (Automatic Meter Reading) |
| Abstract |
Customer clustering method is used to make a TDLP (typical daily load profile) to estimate the quater hourly load profile of non-AMR (Automatic Meter Reading) customer. In this paper, repeated small-sized clustering method is supposed to improve the classifying ability of TDLP. K-means algorithm is well-known clustering technology of data mining. To reduce the local maxima of k-means algorithm, proposed method clusters average load profiles to small-sized clusters and selects the highest error rated cluster and clusters this to small-sized clusters repeatedly to minimize the local maxima. |