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Changing Electricity Tariff—An Empirical Analysis Based on Commercial Customers’ Data from Poland

Energies (Basel), 2023-10, Vol.16 (19), p.6853 [Peer Reviewed Journal]

COPYRIGHT 2023 MDPI AG ;2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1996-1073 ;EISSN: 1996-1073 ;DOI: 10.3390/en16196853

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  • Title:
    Changing Electricity Tariff—An Empirical Analysis Based on Commercial Customers’ Data from Poland
  • Author: Ząbkowski, Tomasz ; Gajowniczek, Krzysztof ; Matejko, Grzegorz ; Brożyna, Jacek ; Mentel, Grzegorz ; Charytanowicz, Małgorzata ; Jarnicka, Jolanta ; Olwert, Anna ; Radziszewska, Weronika
  • Subjects: changing electricity tariff ; classification tree ; commercial customers ; Cost control ; Customers ; Electricity ; energy consumption ; energy efficiency ; k-nearest neighbors ; Liberalization ; Literature reviews ; Small & medium sized enterprises-SME
  • Is Part Of: Energies (Basel), 2023-10, Vol.16 (19), p.6853
  • Description: Nearly 60% of commercial customers are connected to a low-voltage network in Poland with a contractual capacity of more than 40 kW and are assigned a fixed tariff with flat prices for the whole year, no matter the usage volume. With smart meters, more data about how businesses use energy are becoming available to both energy providers and customers. This enables innovation in the structure and type of tariffs on offer in the energy market. Customers can explore their usage patterns to choose the most suitable tariff to benefit from lower prices and thus generate savings. In this paper, we analyzed whether customers’ electricity usage matched their optimal tariff and further investigated which of them could benefit or lose from switching the tariff based on the real dataset with the hourly energy readings of 1212 commercial entities in Poland recorded between 2016 and 2019. Three modelling approaches, i.e., the k-nearest neighbors, classification tree and random forest, were tested for optimal tariff classification, while for the benchmark, we used a simple approach, in which the tariff was proposed based on the customers’ previous electricity usage. The main findings from the research are threefold: (1) out of all the analyzed entities, on average, 76% of them could have benefited from the tariff switching, which suggests that customers may not be aware of the tariff change benefits, or they had chosen a tariff plan that was not tailored to them; (2) a random forest model offers a viable approach to accurate tariff classification; (3) the policy implication from the research is the need to increase the customers’ awareness about the tariffs and propose reliable tools for selecting the optimal tariff.
  • Publisher: Basel: MDPI AG
  • Language: English
  • Identifier: ISSN: 1996-1073
    EISSN: 1996-1073
    DOI: 10.3390/en16196853
  • Source: Geneva Foundation Free Medical Journals at publisher websites
    ROAD: Directory of Open Access Scholarly Resources
    ProQuest Central
    DOAJ Directory of Open Access Journals
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