IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i7p1788-d156810.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/7/1788/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/11/7/1788/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Barja-Martinez, Sara & Aragüés-Peñalba, Mònica & Munné-Collado, Íngrid & Lloret-Gallego, Pau & Bullich-Massagué, Eduard & Villafafila-Robles, Roberto, 2021. "Artificial intelligence techniques for enabling Big Data services in distribution networks: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yildiz, B. & Bilbao, J.I. & Dore, J. & Sproul, A.B., 2017. "Recent advances in the analysis of residential electricity consumption and applications of smart meter data," Applied Energy, Elsevier, vol. 208(C), pages 402-427.
    2. Pinheiro, Marco G. & Madeira, Sara C. & Francisco, Alexandre P., 2023. "Short-term electricity load forecasting—A systematic approach from system level to secondary substations," Applied Energy, Elsevier, vol. 332(C).
    3. Barja-Martinez, Sara & Aragüés-Peñalba, Mònica & Munné-Collado, Íngrid & Lloret-Gallego, Pau & Bullich-Massagué, Eduard & Villafafila-Robles, Roberto, 2021. "Artificial intelligence techniques for enabling Big Data services in distribution networks: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
    4. Amato, Umberto & Antoniadis, Anestis & De Feis, Italia & Goude, Yannig & Lagache, Audrey, 2021. "Forecasting high resolution electricity demand data with additive models including smooth and jagged components," International Journal of Forecasting, Elsevier, vol. 37(1), pages 171-185.
    5. Ahmed, R. & Sreeram, V. & Mishra, Y. & Arif, M.D., 2020. "A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    6. Rongheng Lin & Budan Wu & Yun Su, 2018. "An Adaptive Weighted Pearson Similarity Measurement Method for Load Curve Clustering," Energies, MDPI, vol. 11(9), pages 1-17, September.
    7. Namrye Son, 2021. "Comparison of the Deep Learning Performance for Short-Term Power Load Forecasting," Sustainability, MDPI, vol. 13(22), pages 1-25, November.
    8. Nowotarski, Jakub & Weron, Rafał, 2018. "Recent advances in electricity price forecasting: A review of probabilistic forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1548-1568.
    9. Chou, Jui-Sheng & Tran, Duc-Son, 2018. "Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders," Energy, Elsevier, vol. 165(PB), pages 709-726.
    10. Gautham Krishnadas & Aristides Kiprakis, 2020. "A Machine Learning Pipeline for Demand Response Capacity Scheduling," Energies, MDPI, vol. 13(7), pages 1-25, April.
    11. Ulaa AlHaddad & Abdullah Basuhail & Maher Khemakhem & Fathy Elbouraey Eassa & Kamal Jambi, 2023. "Towards Sustainable Energy Grids: A Machine Learning-Based Ensemble Methods Approach for Outages Estimation in Extreme Weather Events," Sustainability, MDPI, vol. 15(16), pages 1-19, August.
    12. Florian Ziel & Kevin Berk, 2019. "Multivariate Forecasting Evaluation: On Sensitive and Strictly Proper Scoring Rules," Papers 1910.07325, arXiv.org.
    13. Yang, Zhongshan & Wang, Jian, 2018. "A hybrid forecasting approach applied in wind speed forecasting based on a data processing strategy and an optimized artificial intelligence algorithm," Energy, Elsevier, vol. 160(C), pages 87-100.
    14. Guillaume Guerard & Hugo Pousseur & Ihab Taleb, 2021. "Isolated Areas Consumption Short-Term Forecasting Method," Energies, MDPI, vol. 14(23), pages 1-23, November.
    15. Jaržemskis Andrius & Jaržemskienė Ilona, 2022. "European Green Deal Implications on Country Level Energy Consumption," Folia Oeconomica Stetinensia, Sciendo, vol. 22(2), pages 97-122, December.
    16. Emilio Ghiani & Alessandro Serpi & Virginia Pilloni & Giuliana Sias & Marco Simone & Gianluca Marcialis & Giuliano Armano & Paolo Attilio Pegoraro, 2018. "A Multidisciplinary Approach for the Development of Smart Distribution Networks," Energies, MDPI, vol. 11(10), pages 1-29, September.
    17. Di Piazza, A. & Di Piazza, M.C. & La Tona, G. & Luna, M., 2021. "An artificial neural network-based forecasting model of energy-related time series for electrical grid management," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 184(C), pages 294-305.
    18. Ma, Weiwu & Fang, Song & Liu, Gang & Zhou, Ruoyu, 2017. "Modeling of district load forecasting for distributed energy system," Applied Energy, Elsevier, vol. 204(C), pages 181-205.
    19. Zulfiqar, M. & Kamran, M. & Rasheed, M.B. & Alquthami, T. & Milyani, A.H., 2023. "A hybrid framework for short term load forecasting with a navel feature engineering and adaptive grasshopper optimization in smart grid," Applied Energy, Elsevier, vol. 338(C).
    20. Federico Divina & Aude Gilson & Francisco Goméz-Vela & Miguel García Torres & José F. Torres, 2018. "Stacking Ensemble Learning for Short-Term Electricity Consumption Forecasting," Energies, MDPI, vol. 11(4), pages 1-31, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:11:y:2018:i:7:p:1788-:d:156810. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.