Cluster-Based Approach to Estimate Demand in the Polish Power System Using Commercial Customers’ Data

Tomasz Ząbkowski (), Krzysztof Gajowniczek, Grzegorz Matejko, Jacek Brożyna, Grzegorz Mentel, Małgorzata Charytanowicz, Jolanta Jarnicka, Anna Olwert, Weronika Radziszewska and Jörg Verstraete
Additional contact information
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
Jörg Verstraete: Institute of Fluid-Flow Machinery, Polish Academy of Sciences, Fiszera 14, 80-231 Gdańsk, Poland

Energies, 2023, vol. 16, issue 24, 1-21

Abstract: This paper presents an approach to estimate demand in the Polish Power System (PPS) using the historical electricity usage of 27 thousand commercial customers, observed between 2016 and 2020. The customer data were clustered and samples as well as features were created to build neural network models. The goal of this research is to analyze if the clustering of customers can help to explain demand in the PPS. Additionally, considering that the datasets available for commercial customers are typically much smaller, it was analyzed what a minimal sample size drawn from the clusters would have to be in order to accurately estimate demand in the PPS. The evaluation and experiments were conducted for each year separately; the results proved that, considering adjusted R 2 and mean absolute percentage error, our clustering-based method can deliver a high accuracy in the load estimation.

Keywords: energy usage; commercial customers; clustering; neural networks; demand model; Polish Power System (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 (1)

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