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Clustering Power Consumption Data in Smart Grid

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Smart Grid Inspired Future Technologies

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Abstract

For power distributors it is very important to have detailed information about the power consumption characteristics of their customers. These information is essential to plan correctly the required amount of energy from power-plants in order to minimize the difference between the demand and supply and to optimize the load of transportation grid as well. For industrial power consumer customers, on the market the actual rate of electric power may depend on their power consumption characteristics. By using intelligent meters and analyzing their behavior, relevant information can be obtained and the consumers can be classified in order to find the best rates for the supplier as well as for the consumer. In this paper, we introduce new results on clustering the consumers. The clustering method is based on forecasting the consumption time series. The numerical results prove that the method is capable of clustering consumers with different consumption patterns with good performance as a result the forecast based method proved to be the a promising tool in real applications.

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Notes

  1. 1.

    Paper with more details on results is submitted for publication.

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Acknowledgment

This publication/research has been supported by PPKE KAP 15-084-1.2-ITK Grant. This source of support is gratefully acknowledged.

Author information

Authors and Affiliations

  1. Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary

    Kálmán Tornai &  András Oláh

Authors
  1. Kálmán Tornai
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  2. András Oláh
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Corresponding author

Correspondence to Kálmán Tornai .

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Exeter, Exeter, United Kingdom

    Jia Hu

  2. Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, British Columbia, Canada

    Victor C. M. Leung

  3. University of Essex, Essex, United Kingdom

    Kun Yang

  4. Simula Research Laboratory, Fornebu, Norway

    Yan Zhang

  5. Imperial College, London, United Kingdom

    Jianliang Gao

  6. The Institute of Information and System Science, Xi’an Jiaotong University, Xi’an, China

    Shusen Yang

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© 2017 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Tornai, K., Oláh, . (2017). Clustering Power Consumption Data in Smart Grid. In: Hu, J., Leung, V., Yang, K., Zhang, Y., Gao, J., Yang, S. (eds) Smart Grid Inspired Future Technologies. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 175. Springer, Cham. https://doi.org/10.1007/978-3-319-47729-9_3

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  • DOI: https://doi.org/10.1007/978-3-319-47729-9_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47728-2

  • Online ISBN: 978-3-319-47729-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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