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Do Customers Choose Proper Tariff? Empirical Analysis Based on Polish Data Using Unsupervised Techniques

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  • Rafik Nafkha

    (Department of Informatics, Faculty of Applied Informatics and Mathematics, Warsaw University of Life Sciences-SGGW, Nowoursynowska 159, 02-787 Warsaw, Poland)

  • Krzysztof Gajowniczek

    (Department of Informatics, Faculty of Applied Informatics and Mathematics, Warsaw University of Life Sciences-SGGW, Nowoursynowska 159, 02-787 Warsaw, Poland)

  • Tomasz Ząbkowski

    (Department of Informatics, Faculty of Applied Informatics and Mathematics, Warsaw University of Life Sciences-SGGW, Nowoursynowska 159, 02-787 Warsaw, Poland)

Abstract

Individual electricity customers that are connected to low voltage network in Poland are usually assigned to the most common G11 tariff group with flat prices for the whole year, no matter the usage volume. Given the diversity of customers’ behavior inside the same specific group, we aim to propose an approach to assign the customers based on some objective factors rather than subjective fixed assignment. With the smart metering data and statistical methods for clustering we can explore and recommend each customer the most suitable tariff to benefit from lower prices thus generate the savings. Further, the paper applies hierarchical, k-means and Kohonen approaches to assign the customers to the proper tariff, assuming that the customer can gain the biggest expenses reduction from the tariff switch. The analysis was conducted based on the Polish dataset with an hourly energy readings among 197 entities.

Suggested Citation

  • Rafik Nafkha & Krzysztof Gajowniczek & Tomasz Ząbkowski, 2018. "Do Customers Choose Proper Tariff? Empirical Analysis Based on Polish Data Using Unsupervised Techniques," Energies, MDPI, vol. 11(3), pages 1-17, February.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:3:p:514-:d:133773
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    References listed on IDEAS

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    Cited by:

    1. Tomasz Ząbkowski & Krzysztof Gajowniczek & Grzegorz Matejko & Jacek Brożyna & Grzegorz Mentel & Małgorzata Charytanowicz & Jolanta Jarnicka & Anna Olwert & Weronika Radziszewska, 2023. "Changing Electricity Tariff—An Empirical Analysis Based on Commercial Customers’ Data from Poland," Energies, MDPI, vol. 16(19), pages 1-17, September.
    2. Michał Gostkowski & Tomasz Rokicki & Luiza Ochnio & Grzegorz Koszela & Kamil Wojtczuk & Marcin Ratajczak & Hubert Szczepaniuk & Piotr Bórawski & Aneta Bełdycka-Bórawska, 2021. "Clustering Analysis of Energy Consumption in the Countries of the Visegrad Group," Energies, MDPI, vol. 14(18), pages 1-25, September.
    3. Jerzy Andruszkiewicz & Józef Lorenc & Agnieszka Weychan, 2019. "Demand Price Elasticity of Residential Electricity Consumers with Zonal Tariff Settlement Based on Their Load Profiles," Energies, MDPI, vol. 12(22), pages 1-22, November.
    4. Francisco Martínez-Álvarez & Alicia Troncoso & José C. Riquelme, 2018. "Data Science and Big Data in Energy Forecasting," Energies, MDPI, vol. 11(11), pages 1-2, November.
    5. 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.
    6. Gołębiowska, Bernadeta & Bartczak, Anna & Budziński, Wiktor, 2021. "Impact of social comparison on preferences for Demand Side Management in Poland," Energy Policy, Elsevier, vol. 149(C).
    7. Rafik Nafkha & Tomasz Ząbkowski & Krzysztof Gajowniczek, 2021. "Deep Learning-Based Approaches to Optimize the Electricity Contract Capacity Problem for Commercial Customers," Energies, MDPI, vol. 14(8), pages 1-17, April.
    8. Krzysztof Gajowniczek & Marcin Bator & Tomasz Ząbkowski & Arkadiusz Orłowski & Chu Kiong Loo, 2020. "Simulation Study on the Electricity Data Streams Time Series Clustering," Energies, MDPI, vol. 13(4), pages 1-25, February.
    9. Bernadeta Gołębiowska & Anna Bartczak & Wiktor Budziński, 2019. "Impact of social comparison on DSM in Poland," Working Papers 2019-10, Faculty of Economic Sciences, University of Warsaw.

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