한국정보과학회:학술대회논문집 (Proceedings of the Korean Information Science Society Conference)
- 한국정보과학회 2008년도 한국컴퓨터종합학술대회논문집 Vol.35 No.1 (C)
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- Pages.26-30
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- 2008
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- 1598-5164(pISSN)
전력 부하 패턴 자동 예측을 위한 분류 기법
Classification Methods for Automated Prediction of Power Load Patterns
- ;
- 박진형 (충북대학교 데이터베이스/바이오인포매틱스 연구실) ;
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이헌규
(충북대학교 데이터베이스/바이오인포매틱스 연구실) ;
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류근호
(충북대학교 데이터베이스/바이오인포매틱스 연구실)
- Minghao, Piao (Database/Bioinformatics Laboratory, Chungbuk National University) ;
- Park, Jin-Hyung (Database/Bioinformatics Laboratory, Chungbuk National University) ;
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Lee, Heon-Gyu
(Database/Bioinformatics Laboratory, Chungbuk National University) ;
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Ryu, Keun-Ho
(Database/Bioinformatics Laboratory, Chungbuk National University)
- 발행 : 2008.06.30
초록
Currently an automated methodology based on data mining techniques is presented for the prediction of customer load patterns in long duration load profiles. The proposed our approach consists of three stages: (i) data pre-processing: noise or outlier is removed and the continuous attribute-valued features are transformed to discrete values, (ii) cluster analysis: k-means clustering is used to create load pattern classes and the representative load profiles for each class and (iii) classification: we evaluated several supervised learning methods in order to select a suitable prediction method. According to the proposed methodology, power load measured from AMR (automatic meter reading) system, as well as customer indexes, were used as inputs for clustering. The output of clustering was the classification of representative load profiles (or classes). In order to evaluate the result of forecasting load patterns, the several classification methods were applied on a set of high voltage customers of the Korea power system and derived class labels from clustering and other features are used as input to produce classifiers. Lastly, the result of our experiments was presented.
키워드