Advertisement

The Behavioural Profiles of Energy Consumers: Comparison of the Decision Tree Method and the Logit Model

  • Edyta Ropuszyńska-SurmaEmail author
  • Magdalena Węglarz
Chapter
  • 18 Downloads
Part of the Contributions to Management Science book series (MANAGEMENT SC.)

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.

Keywords

Prosumer Micro-installation Renewable energy sources Logit model Decision tree method 

References

  1. Act of 20 February 2015 on renewable energy sources. (2015). Journal of Laws, Item 478.Google Scholar
  2. 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.CrossRefGoogle Scholar
  3. 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.CrossRefGoogle Scholar
  4. 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.CrossRefGoogle Scholar
  5. 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.CrossRefGoogle Scholar
  6. 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
  7. 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.CrossRefGoogle Scholar
  8. 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.CrossRefGoogle Scholar
  9. Górecki, B. R. (2013). Ekonometria podstawy teorii i praktyki. Warszawa: Key Text.Google Scholar
  10. Gruszczyński, M. (Ed.). (2010). Mikroekonometria. Modele i metody analizy danych indywidualnych. Warszawa: Oficyna Wolters Kluwer.Google Scholar
  11. Hutt, M. D., & Spesh, T. W. (1997). Zarządzanie marketingiem. Strategia rynku dóbr i usłg przemysłowych. Warszawa: PWN.Google Scholar
  12. Kotler, P. (1986). The prosumer movement: A new challenge for marketers. Advances in Consumer Research, 13, 510–513.Google Scholar
  13. 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.CrossRefGoogle Scholar
  14. Kufel, T. (2013). Ekonometria. Rozwiązywanie problemów z wykorzystaniem programu GRETL. Warszawa: PWN.Google Scholar
  15. Łapczyński, M. (2002). Badania segmentów rynku motoryzacyjnego z zastosowaniem drzew klasyfikacyjnych (CART). Zeszyty Naukowe Akademii Ekonomicznej w Krakowie, 586, 87–102.Google Scholar
  16. Łapczyński, M. (2010). Drzewa klasyfikacyjne i regresyjne w badaniach marketingowych. Kraków: Wydawnictwo Uniwersytetu Ekonomicznego w Krakowie.Google Scholar
  17. Markowicz, I. (2010). Statystyczna analiza żywotności firm. Szczecin: Wydawnictwo Naukowe Uniwersytetu Szczecińskiego.Google Scholar
  18. McKinney, W. (2010). Data structures for statistical computing in python (pp. 51–56). Proceedings of the 9th Python in Science Conference.Google Scholar
  19. 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.CrossRefGoogle Scholar
  20. Osińska, M. (Ed.). (2007). Ekonometria współczesna. Toruń: Wydawnictwo „Dom Organizatora”.Google Scholar
  21. 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.Google Scholar
  22. Penc, J. (1997). Leksykon biznesu. Warszawa: Agencja Wydawnicza Placet.Google Scholar
  23. Raschka, S. (2015). Python machine learning. Packt Publishing.Google Scholar
  24. Ropuszyńska-Surma, E., & Weglarz, M. (2018a). Identyfikacja czynników wpływających na przyszłych prosumentów. Studia i Prace Wydziału Nauk Ekonomicznych i Zarządzania, 54(3), 331–346.Google Scholar
  25. 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.
  26. 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.Google Scholar
  27. 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.CrossRefGoogle Scholar
  28. 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.Google Scholar
  29. 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.CrossRefGoogle Scholar
  30. Toffler, A. (1980). The third wave. New York: William Morrow and Company.Google Scholar
  31. van Rossum, G., & Drake, F. L. (Eds.). (2001). Python reference manual. Virginia: Python Labs.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Wroclaw University of Science and TechnologyWrocławPoland

Personalised recommendations