Skip to main content

Comparison of Two Kinds of Distance in Research on the Method of the Extraction of Load Pattern

  • Conference paper
  • First Online:
Advances in Electronic Engineering, Communication and Management Vol.1

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 139))

  • 1042 Accesses

Abstract

The paper clusters power users’ load curves based on fuzzy C means (FCM) clustering algorithm, and to overcome the drawback due to Euclidean distance, the paper also defines a new similarity of curves to extract the power load models which eliminates the limitations of the Euclidean distance that considers only the geometric distance as the similarity measurement of curves.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Vojdani, A.: Smart Integration. IEEE Power and Energy Magazine 6(6), 71–79 (2008)

    Article  Google Scholar 

  2. Chicco, G., Ilie, I.S.: Support vector clustering of electrical load pattern data. IEEE Transactions on Power Systems 24(3), 1619–1628 (2009)

    Article  Google Scholar 

  3. Willis, H.L., Schauer, A.E., Northcote-Green, J.E.D., Vismor, T.D.: Forecasting Distribution System Loads Using Curve Shape Clustering. IEEE Transactions on Power Apparatus and Systems, PAS 102(4), 893–901 (1983)

    Article  Google Scholar 

  4. Keogh, E.J., Pazzani, M.J.: An enhanced representation of time series which allows fast and accurate classification. In: Clustering and Relevance Feedback, Proceedings of the 4th International Conference of Knowledge Discovery and Data Mining. AAAI Press (1998)

    Google Scholar 

  5. Hand, D., Heikki, M., Padhraic, S.: Principles of data mining, pp. 21–23, 186–199. Massachusetts Institute of Technology (2001)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Liu, L., Ding, Q., Zhang, T., Chen, J. (2012). Comparison of Two Kinds of Distance in Research on the Method of the Extraction of Load Pattern. In: Jin, D., Lin, S. (eds) Advances in Electronic Engineering, Communication and Management Vol.1. Lecture Notes in Electrical Engineering, vol 139. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27287-5_52

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-27287-5_52

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-27286-8

  • Online ISBN: 978-3-642-27287-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics