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

Tomasz Ząbkowski (), Krzysztof Gajowniczek, Grzegorz Matejko, Jacek Brożyna, Grzegorz Mentel, Małgorzata Charytanowicz, Jolanta Jarnicka, Anna Olwert and Weronika Radziszewska
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Tomasz Ząbkowski: Institute of Information Technology, Warsaw University of Life Sciences-SGGW, Nowoursynowska 159, 02-787 Warsaw, Poland
Krzysztof Gajowniczek: Institute of Information Technology, Warsaw University of Life Sciences-SGGW, Nowoursynowska 159, 02-787 Warsaw, Poland
Grzegorz Matejko: Polskie Towarzystwo Cyfrowe, Krakowskie Przedmieście 57/4, 20-076 Lublin, Poland
Jacek Brożyna: Department of Quantitative Methods, The Faculty of Management, Rzeszow University of Technology, Aleja Powstańców Warszawy 10/S, 35-959 Rzeszow, Poland
Grzegorz Mentel: Department of Quantitative Methods, The Faculty of Management, Rzeszow University of Technology, Aleja Powstańców Warszawy 10/S, 35-959 Rzeszow, Poland
Małgorzata Charytanowicz: Systems Research Institute, Polish Academy of Sciences, Newelska 6, 01-447 Warsaw, Poland
Jolanta Jarnicka: Systems Research Institute, Polish Academy of Sciences, Newelska 6, 01-447 Warsaw, Poland
Anna Olwert: Systems Research Institute, Polish Academy of Sciences, Newelska 6, 01-447 Warsaw, Poland
Weronika Radziszewska: Systems Research Institute, Polish Academy of Sciences, Newelska 6, 01-447 Warsaw, Poland

Energies, 2023, vol. 16, issue 19, 1-17

Abstract: 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.

Keywords: energy consumption; energy efficiency; commercial customers; changing electricity tariff; k-nearest neighbors; classification tree; random forest (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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