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Consumer Segmentation Through Multi-instance Clustering Time-Series Energy Data from Smart Meters

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Soft Computing for Sustainability Science

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 358))

Abstract

With the rollout of smart metering infrastructure at large scale, demand-response programs may now be tailored based on consumption and production patterns mined from sensed data. In previous works, groups of similar energy consumption profiles were obtained. But, discovering typical consumption profiles is not enough, it is also important to reveal various preferences, behaviors and characteristics of individual consumers. However, the current approaches cannot determine clusters of similar consumer or prosumer households. To tackle this issue, we propose to model the consumer clustering problem as a multi-instance clustering problem and we apply a multi-instance clustering algorithm to solve it. We model a consumer as a bag and each bag consists of instances, where each instance will represent a day or a month of consumption. Internal indices were used for evaluating our clustering process. The obtained results are general applicable, and will be useful in a general business analytics context.

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Notes

  1. 1.

    It is called prosumer to those consumers who have installed solar panels and therefore, also they produce energy that can consume or put on the power grid; thus they produced and consumed, hence the name prosumer.

  2. 2.

    http://www.eandis.be.

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Authors and Affiliations

  1. Department of Computer Science, Central University of Las Villas, Santa Clara, Cuba

    Alejandro Gómez-Boix & Leticia Arco

  2. COMO Lab, Vrije Universiteit Brussel, Brussel, Belgium

    Ann Nowé

Authors
  1. Alejandro Gómez-Boix
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  2. Leticia Arco
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  3. Ann Nowé
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Corresponding authors

Correspondence to Alejandro Gómez-Boix or Leticia Arco .

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Editors and Affiliations

  1. Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain

    Carlos Cruz Corona

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Gómez-Boix, A., Arco, L., Nowé, A. (2018). Consumer Segmentation Through Multi-instance Clustering Time-Series Energy Data from Smart Meters. In: Cruz Corona, C. (eds) Soft Computing for Sustainability Science. Studies in Fuzziness and Soft Computing, vol 358. Springer, Cham. https://doi.org/10.1007/978-3-319-62359-7_6

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  • DOI: https://doi.org/10.1007/978-3-319-62359-7_6

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