The Transactions of The Korean Institute of Electrical Engineers (전기학회논문지)

The Korean Institute of Electrical Engineers (대한전기학회)
권호 표지

Study of Temporal Data Mining for Transformer Load Pattern Analysis

변압기 부하패턴 분석을 위한 시간 데이터마이닝 연구

  • 신진호 (한국전력공사 전력연구원) ;
  • 이봉재 (한국전력공사 전력연구원) ;
  • 김영일 (한국전력공사 전력연구원) ;
  • 이헌규 (충북대학교 전자계산학과) ;
  • 류근호 (충북대학교 전기전자 컴퓨터공학부)
  • Published : 2008.11.01
Download PDF
⟨ Previous Next ⟩

Abstract

This paper presents the temporal classification method based on data mining techniques for discovering knowledge from measured load patterns of distribution transformers. Since the power load patterns have time-varying characteristics and very different patterns according to the hour, time, day and week and so on, it gives rise to the uninformative results if only traditional data mining is used. Therefore, we propose a temporal classification rule for analyzing and forecasting transformer load patterns. The main tasks include the load pattern mining framework and the calendar-based expression using temporal association rule and 3-dimensional cube mining to discover load patterns in multiple time granularities.

Keywords

References

  1. S. J. Huang, K. Shih, 'Short-term load forecasting via ARMA model identification including non-Gaussian process considerations', IEEE Trans. Power System, Vol.18, No.2, pp.673-679, 2003 https://doi.org/10.1109/TPWRS.2003.811010
  2. G. Chicco, R. Napoli, P. Postulache, M. Scutaru, C. Toader, 'Customer characterization options for improving the tariff offer', IEEE Trans. Power System, Vol.18, pp.381-387, 2003 https://doi.org/10.1109/TPWRS.2002.807085
  3. B. Pit, D. Kirchen, 'Applications of data mining techniques to load profiling', In Proc. IEEE PICA, pp.131-136, 1999
  4. G. Chicco, R. Napoli, P. Postulache, M. Scutariu, C. Toader, 'Load Patter-Based Classification of Electricity Customers', IEEE Trans. Power System, Vol.19, p.1232-1239, 2004 https://doi.org/10.1109/TPWRS.2004.826810
  5. R. F. Chang. C. N. Lu, 'Load profiling and its applications in power market', IEEE Trans. Power Engineering Society General Meeting, Vol.2, pp.974-978, 2003
  6. N. Amjady, 'Short-term hourly load forecasting using time series modeling with peak load capability', IEEE Trans. Power System. Vol.16, No.3, pp.498-505, 2001 https://doi.org/10.1109/59.932287
  7. B. Liu, W. Hsu, Y. Ma, 'Integrating classification and association rule mining', In Proc. of the 4th Int'l Conference Knowledge Discovery and Data Mining, 1998
  8. Y. Li, P. Ning, 'Discovering calenda-based temporal association rules', In Proc. of the 8th Int'l Symposium on Temporal Representation and Reasoning 2003
  9. K. Verma. O. P. Vyas, 'Efficient calendar based temporal association rule', SIGMOD Rec., VoI.34:3, pp.63-70, 2005
  10. W. Li, J. Han, J. Pei, "CMAR: Accurate and Efficient Classification Based on Multiple Association Rules", In Proc. of 2001 Int'l Conference on Data Mining, 2001
  11. www.csc.liv.ac.uk/~frans/KDD/Software/CMAR/cmar.html