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Customer Segmentation Based on the Electricity Demand Signature: The Andalusian Case

Author

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  • Andrés Camero

    (Departamento de Lenguajes y Ciencias de la Computación, Universidad de Málaga, 29071 Málaga, Spain)

  • Gabriel Luque

    (Departamento de Lenguajes y Ciencias de la Computación, Universidad de Málaga, 29071 Málaga, Spain)

  • Yesnier Bravo

    (Bettergy, Parque Tecnológico de Andalucía, 29590 Málaga, Spain)

  • Enrique Alba

    (Departamento de Lenguajes y Ciencias de la Computación, Universidad de Málaga, 29071 Málaga, Spain)

Abstract

A smart meter enables electric utilities to get detailed insights into their customer needs, allowing them to offer tailored products and services, and to succeed in an increasingly competitive market. While in an ideal world companies would treat every customer as an individual, in practice this is rather difficult. For this reason, companies usually have to target smaller groups of customers that are similar. There are several ways of tackling this matter and finding the right approach is a key to success. Therefore, in this study we introduce the electricity demand signature, a novel approach to characterize and cluster electricity customers based on their demand habits. We test our proposal using the electricity demand of 64 buildings in Andalusia, Spain, and compare our results to the state-of-the-art. The results show that our proposal is useful for clustering customers in a meaningful way, and that it is an easy and friendly representation of the behavior of a customer that can be used for further analysis.

Suggested Citation

  • Andrés Camero & Gabriel Luque & Yesnier Bravo & Enrique Alba, 2018. "Customer Segmentation Based on the Electricity Demand Signature: The Andalusian Case," Energies, MDPI, vol. 11(7), pages 1-14, July.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:7:p:1788-:d:156810
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    References listed on IDEAS

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    1. Luis Hernández & Carlos Baladrón & Javier M. Aguiar & Belén Carro & Antonio Sánchez-Esguevillas, 2012. "Classification and Clustering of Electricity Demand Patterns in Industrial Parks," Energies, MDPI, vol. 5(12), pages 1-14, December.
    2. Haben, Stephen & Ward, Jonathan & Vukadinovic Greetham, Danica & Singleton, Colin & Grindrod, Peter, 2014. "A new error measure for forecasts of household-level, high resolution electrical energy consumption," International Journal of Forecasting, Elsevier, vol. 30(2), pages 246-256.
    3. Raza, Muhammad Qamar & Khosravi, Abbas, 2015. "A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 1352-1372.
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