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Dynamic Shape Modeling of Consumers’ Daily Load Based on Data Mining

  • Lianmei Zhang
  • Shihong Chen
  • Qiping Hu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3584)

Abstract

The shape characteristic of daily power consumption of consumers can be applied to guide their power consumption behaviors and improve load structures of power system. It is also the basis to obtain the shape characteristic of daily power consumption of a trade and conduct researches in the state estimate of distribution networks etc. Traditional analytical approaches are limited to qualitative analysis with a small coverage only. We propose a model which can perform in-depth analysis of customer power consumption behaviors by data mining through similar sequence analysis to overcome the drawbacks of traditional approaches. The model uses real-time sampling of the energy data of consumers to form the shape characteristic curves. The application and testing of the model under an instance is analyzed in this paper.

Keywords

Power Consumption Data Mining Power System Shape Characteristic Load Forecast 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Mori, H., Kosemura, N., Kondo, T., Numa, K.: Data mining for short-term load forecasting. In: Power Engineering Society Winter Meeting, vol. 1, pp. 623–624. IEEE, Los Alamitos (2002)Google Scholar
  2. 2.
    Heunis, S.W., Herman, R.: A Thermal Loading Guide for Residential Distribution Transformers Based on Time-Variant Current Load. IEEE Transactions On Power Systems 19(3), 1294–1298 (2004)CrossRefGoogle Scholar
  3. 3.
    Tso, S.K., Lin, J.K., Ho, H.K., Mak, C.M., Yung, K.M., Ho, Y.K.: Data mining for detection of sensitive buses and influential buses in a power system subjected to disturbances. IEEE Transactions on Power Systems 19(1), 563–568 (2004)CrossRefGoogle Scholar
  4. 4.
    Chang, R.F., Lu, C.N.: Load profile assignment of low voltage customers for power retail market applications. IEE Proceedings on Generation, Transmission and Distribution 150(3), 263–267 (2003)CrossRefGoogle Scholar
  5. 5.
    Gulski, E., Quak, B., Wester, F.J., de Vries, F., Mayoral, M.B.: Application of data mining techniques for power cable diagnosis. In: Proceedings of the 7th International Conference on Properties and Applications of Dielectric Materials, June 1-5, vol. 3, pp. 986–989 (2003)Google Scholar
  6. 6.
    Liao, S.S., Tang, T.H., Liu, W.-Y.: Finding Relevant Sequences in Time Series Containing Crisp, Interval, and Fuzzy Interval Data. IEEE Transactions On Systems, Man, And Cybernetics—Part B: Cybernetics 34(5), 2071–2079 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Lianmei Zhang
    • 1
  • Shihong Chen
    • 1
  • Qiping Hu
    • 1
  1. 1.Electrical Engineering CollegeWuhan UniversityChina

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