Abstract
The purpose of this study is to explore the behavioural profiles of energy consumers, i.e. households (1) which have considered installing renewable energy sources (RES) and (2) which want to become prosumers. The identification of the user profile is vital so as to gain knowledge about users of small-scale generators in order to provide them with a personalised offer. The findings from this study could be valuable for local authorities, energy utilities and producers of RES installations. The main determinants of the willingness to install RES among households were explored by means of the empirical analysis of data collected by a survey of 960 households in Lower Silesia, a south-western region of Poland, in November and December 2015. The research identified the correlation between the households’ willingness to install RES (to become prosumers) and (1) socio-economic variables, (2) pro-ecological and pro-efficient behaviour variables, and (3) attitudinal variables. The importance of the variables was verified by a logit model and by the decision tree method. The authors used both methods to determine the key features of energy consumers and to make predictions about whether they are inclined to invest in RES and to become energy prosumers. The results obtained from these two methods were compared.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
The values in brackets show what percentage of respondents are willing to install RES in this segment, then among the RG. For each variable, the RG is highlighted in Table 2.
References
Act of 20 February 2015 on renewable energy sources. (2015). Journal of Laws, Item 478.
Albert, A., & Maasoumy, M. (2016). Predictive segmentation of energy consumers. Applied Energy, 177, 435–448. https://doi.org/10.1016/j.apenergy.2016.05.128.
Deeks, J. J., & Altman, D. G. (2004). Diagnostic tests 4: Likelihood ratios. BMJ, 329(7458), 168–169. https://doi.org/10.1136/bmj.329.7458.168.
Diamantopoulos, A., Schlegelmilch, B. B., Sinkovics, R. R., & Bohlen, G. M. (2003). Can socio-demographics still play a role in profiling green consumers? A review of the evidence and an empirical investigation. Journal of Business Research, 56(6), 465–480. https://doi.org/10.1016/S0148-2963(01)00241-7.
Diaz-Rainey, I., & Ashton, J. K. (2011). Profiling potential green electricity tariff adopters: Green consumerism as an environmental policy tool? Business Strategy and the Environment, 20, 456–470. https://doi.org/10.1002/bse.699.
Federacja Konsumentów. (2016). Jak zostać prosumentem. Accessed January 26, 2017, from, http://www.federacja-konsumentow.org.pl/n,159,1307,91,1,raport-federacji-konsumentow.html
Gautier, A., Hoet, B., Jacqmin, J., & van Driessche, S. (2019). Self-consumption choice of residential PV owners under net-metering. Energy Policy, 128, 648–653. https://doi.org/10.1016/j.enpol.2019.01.055.
Glas, A. S., Lijmer, J. G., Prins, M. H., Bonsel, G. J., & Bossuyt, P. M. M. (2003). The diagnostic odds ratio: A single indicator of test performance. Journal of Clinical Epidemiology, 56(11), 1129–1135. https://doi.org/10.1016/S0895-4356(03)00177-X.
Górecki, B. R. (2013). Ekonometria podstawy teorii i praktyki. Warszawa: Key Text.
Gruszczyński, M. (Ed.). (2010). Mikroekonometria. Modele i metody analizy danych indywidualnych. Warszawa: Oficyna Wolters Kluwer.
Hutt, M. D., & Spesh, T. W. (1997). Zarządzanie marketingiem. Strategia rynku dóbr i usłg przemysłowych. Warszawa: PWN.
Kotler, P. (1986). The prosumer movement: A new challenge for marketers. Advances in Consumer Research, 13, 510–513.
Kubli, M., Loock, M., & Wüstenhagen, R. (2018). The flexible prosumer: Measuring the willingness to co-create distributed flexibility. Energy Policy, 114, 540–548. https://doi.org/10.1016/j.enpol.2017.12.044.
Kufel, T. (2013). Ekonometria. Rozwiązywanie problemów z wykorzystaniem programu GRETL. Warszawa: PWN.
Łapczyński, M. (2002). Badania segmentów rynku motoryzacyjnego z zastosowaniem drzew klasyfikacyjnych (CART). Zeszyty Naukowe Akademii Ekonomicznej w Krakowie, 586, 87–102.
Łapczyński, M. (2010). Drzewa klasyfikacyjne i regresyjne w badaniach marketingowych. Kraków: Wydawnictwo Uniwersytetu Ekonomicznego w Krakowie.
Markowicz, I. (2010). Statystyczna analiza żywotności firm. Szczecin: Wydawnictwo Naukowe Uniwersytetu Szczecińskiego.
McKinney, W. (2010). Data structures for statistical computing in python (pp. 51–56). Proceedings of the 9th Python in Science Conference.
Oberst, C. A., Schmitz, H., & Madlener, R. (2019). Are prosumer households that much different? Evidence from stated residential energy consumption in Germany. Ecological Economics, 158, 101–115. https://doi.org/10.1016/j.ecolecon.2018.12.014.
Osińska, M. (Ed.). (2007). Ekonometria współczesna. Toruń: Wydawnictwo „Dom Organizatora”.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., VanderPlas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, E. (2011). Scikit-learn: Machine learning in python. Journal of Machine Learning Research, 12, 2825–2830.
Penc, J. (1997). Leksykon biznesu. Warszawa: Agencja Wydawnicza Placet.
Raschka, S. (2015). Python machine learning. Packt Publishing.
Ropuszyńska-Surma, E., & Weglarz, M. (2018a). Identyfikacja czynników wpływających na przyszłych prosumentów. Studia i Prace Wydziału Nauk Ekonomicznych i Zarządzania, 54(3), 331–346.
Ropuszyńska-Surma, E., & Weglarz, M. (2018b). Profiling end user of renewable energy sources among residential consumers in Poland. Sustainability, 10(12). https://doi.org/10.3390/su10124452.
Ropuszyńska-Surma, E., Weglarz, M., & Szwabiński, J. (2018). Energy prosumers. Profiling the energy microgeneration market in lower Silesia, Poland. Operations Research and Decisions, 28(1), 75–94. https://doi.org/10.5277/ord180106.
Scarpa, R., & Willis, K. (2010). Willingness-to-pay for renewable energy: Primary and discretionary choice of British households’ for micro-generation technologies. Energy Economics, 32(1), 129–136. https://doi.org/10.1016/j.eneco.2009.06.004.
Song, Y. Y., & Lu, Y. (2015). Decision tree methods: Applications for classification and prediction. Shanghai Archives of Psychiatry, 27(2), 130–135. https://doi.org/10.11919/j.issn.1002-0829.215044.
Sütterlin, B., Brunner, T. A., & Siegrist, M. (2011). Who puts the most energy into energy conservation? A segmentation of energy consumers based on energy-related behavioral characteristics. Energy Policy, 39(12), 8137–8152. https://doi.org/10.1016/j.enpol.2011.10.008.
Toffler, A. (1980). The third wave. New York: William Morrow and Company.
van Rossum, G., & Drake, F. L. (Eds.). (2001). Python reference manual. Virginia: Python Labs.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Appendix
Appendix
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Ropuszyńska-Surma, E., Węglarz, M. (2020). The Behavioural Profiles of Energy Consumers: Comparison of the Decision Tree Method and the Logit Model. In: Sroka, W. (eds) Perspectives on Consumer Behaviour. Contributions to Management Science. Springer, Cham. https://doi.org/10.1007/978-3-030-47380-8_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-47380-8_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-47379-2
Online ISBN: 978-3-030-47380-8
eBook Packages: Business and ManagementBusiness and Management (R0)