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On the Use of Off-the-Shelf Machine Learning Techniques to Predict Energy Demands of Power TAC Consumers

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Agent-Mediated Electronic Commerce. Designing Trading Strategies and Mechanisms for Electronic Markets (AMEC/TADA 2015, AMEC/TADA 2016)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 271))

Abstract

The Power Trading Agent Competition (Power TAC) is a feature-rich simulation that simulates an energy market in a smart grid, where software brokers can buy energy in wholesale markets and sell energy in tariff markets to consumers. Successful brokers can maximize their profits by buying energy at low prices in the wholesale market and selling them at high prices to the consumers. However, this requires that the brokers have accurate predictions of the energy consumption of consumers so that they do not end up having excess energy or insufficient energy in the marketplace. In this paper, we conduct a preliminary investigation that uses standard off-the-shelf machine learning techniques to cluster and predict the consumption of a restricted set of consumers. Our results show that a combination of the popular k-means, k-medoids, and DBSCAN clustering algorithm together with an autoregressive lag model can predict, reasonably accurately, the consumption of consumers.

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Notes

  1. 1.

    https://github.com/powertac/powertac-tools.

  2. 2.

    http://www.powertac.org/wiki/index.php/AgentUDE15.

  3. 3.

    http://www.powertac.org/wiki/index.php/Maxon15.

  4. 4.

    http://www.powertac.org/wiki/index.php/Mertacor2015.

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Acknowledgments

This research is partially supported by NSF grants 1345232 and 1337884. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the sponsoring organizations, agencies, or the U.S. government.

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Authors and Affiliations

  1. Department of Computer Science, New Mexico State University, Las Cruces, New Mexico, 88003, USA

    Francisco Natividad, Russell Y. Folk, William Yeoh & Huiping Cao

Authors
  1. Francisco Natividad
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  2. Russell Y. Folk
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  3. William Yeoh
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  4. Huiping Cao
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Corresponding author

Correspondence to William Yeoh .

Editor information

Editors and Affiliations

  1. School of Informatics, University of Edinburgh , Cambridge, United Kingdom

    Sofia Ceppi

  2. Department of Computer Science, Ashkelon Academic College , Ashkelon, Israel

    Esther David

  3. Department of EE and CS, Vanderbilt University , Nashville, Tennessee, USA

    Chen Hajaj

  4. Smart Systems Group, Heriot-Watt University , Edinburgh, United Kingdom

    Valentin Robu

  5. Information Technology, The American College of Greece , Agia Paraskevi, Athens, Greece

    Ioannis A. Vetsikas

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Natividad, F., Folk, R.Y., Yeoh, W., Cao, H. (2017). On the Use of Off-the-Shelf Machine Learning Techniques to Predict Energy Demands of Power TAC Consumers. In: Ceppi, S., David, E., Hajaj, C., Robu, V., Vetsikas, I. (eds) Agent-Mediated Electronic Commerce. Designing Trading Strategies and Mechanisms for Electronic Markets. AMEC/TADA AMEC/TADA 2015 2016. Lecture Notes in Business Information Processing, vol 271. Springer, Cham. https://doi.org/10.1007/978-3-319-54229-4_8

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  • DOI: https://doi.org/10.1007/978-3-319-54229-4_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54228-7

  • Online ISBN: 978-3-319-54229-4

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