2008년 춘계 학술대회
Current Result Document : 11 / 11
| 한글제목(Korean Title) |
AMR 데이터에서의 전력 부하 패턴 분류 |
| 영문제목(English Title) |
Power Load Pattern Classification from AMR Data |
| 저자(Author) |
Minghao Piao
박진형
이헌규
신진호
류근호
Minghao Piao
Jinhyung Park
Heongyu Lee
Jinho Shin
Keunho Ryu
|
| 원문수록처(Citation) |
VOL 15 NO. 01 PP. 0231 ~ 0234 (2008. 05) |
한글내용
(Korean Abstract) |
|
영문내용
(English 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. |
| 키워드(Keyword) |
데이터 베이스
|
| 파일첨부 |
PDF 다운로드
|
