IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i13p3163-d1423469.html
   My bibliography  Save this article

Artificial Neural Networks as a Tool to Understand Complex Energy Poverty Relationships: The Case of Greece

Author

Listed:
  • Lefkothea Papada

    (School of Mining and Metallurgical Engineering, National Technical University of Athens, Zografou Campus, 15772 Zografou, Greece)

  • Dimitris Kaliampakos

    (School of Mining and Metallurgical Engineering, National Technical University of Athens, Zografou Campus, 15772 Zografou, Greece)

Abstract

The present paper provides an innovative approach in the existing methods of studying energy poverty, i.e., a crucial socio-economic challenge of the past decade in Europe. Since the literature has shown that conventional statistical models lack effectiveness in handling unconventional relationships between variables and present limitations in terms of accurate classification and prediction, the paper explores the ability of Artificial Intelligence and, particularly, of Artificial Neural Networks (ANNs), to successfully predict energy poverty in Greece. The analysis included the prediction of seven energy poverty indicators (output indicators) based on certain socio-economic/geographical factors (input variables), via training an ANN, i.e., the Multilayer Perceptron. Three models (Model A, Model B and Model C) of different combinations of the input variables were tested for each one of the seven indicators. The analysis showed that ANNs managed to predict energy poverty at a remarkably good level of accuracy, ranging from 61.71% (lowest value) up to 82.72% (highest accuracy score). The strong relationships that came up on the examined cases confirmed that ANNs are a promising tool towards a deeper understanding of the energy poverty roots, which in turn can lead to more targeted policies.

Suggested Citation

  • Lefkothea Papada & Dimitris Kaliampakos, 2024. "Artificial Neural Networks as a Tool to Understand Complex Energy Poverty Relationships: The Case of Greece," Energies, MDPI, vol. 17(13), pages 1-19, June.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:13:p:3163-:d:1423469
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/13/3163/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/17/13/3163/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Roberts, Deborah & Vera-Toscano, Esperanza & Phimister, Euan, 2015. "Energy poverty in the UK: Is there a difference between rural and urban areas?," 89th Annual Conference, April 13-15, 2015, Warwick University, Coventry, UK 204213, Agricultural Economics Society.
    2. Dalla Longa, Francesco & Sweerts, Bart & van der Zwaan, Bob, 2021. "Exploring the complex origins of energy poverty in The Netherlands with machine learning," Energy Policy, Elsevier, vol. 156(C).
    3. Papada, Lefkothea & Kaliampakos, Dimitris, 2016. "Developing the energy profile of mountainous areas," Energy, Elsevier, vol. 107(C), pages 205-214.
    4. Abbas, Khizar & Butt, Khalid Manzoor & Xu, Deyi & Ali, Muhammad & Baz, Khan & Kharl, Sanwal Hussain & Ahmed, Mansoor, 2022. "Measurements and determinants of extreme multidimensional energy poverty using machine learning," Energy, Elsevier, vol. 251(C).
    5. Ntaintasis, E. & Mirasgedis, S. & Tourkolias, C., 2019. "Comparing different methodological approaches for measuring energy poverty: Evidence from a survey in the region of Attika, Greece," Energy Policy, Elsevier, vol. 125(C), pages 160-169.
    6. Milena N Rajić & Miroslav B Milovanović & Dragan S Antić & Rado M Maksimović & Pedja M Milosavljević & Dragan Lj Pavlović, 2020. "Analyzing energy poverty using intelligent approach," Energy & Environment, , vol. 31(8), pages 1448-1472, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Takako Mochida & Andrew Chapman & Benjamin Craig McLellan, 2025. "Exploring Energy Poverty: Toward a Comprehensive Predictive Framework," Energies, MDPI, vol. 18(10), pages 1-23, May.
    2. Elpida Kalfountzou & Lefkothea Papada & Christos Tourkolias & Sevastianos Mirasgedis & Dimitris Kaliampakos & Dimitris Damigos, 2025. "A Comparative Analysis of Machine Learning Algorithms in Energy Poverty Prediction," Energies, MDPI, vol. 18(5), pages 1-20, February.
    3. Urszula Grzybowska & Agnieszka Wojewódzka-Wiewiórska & Gintarė Vaznonienė & Hanna Dudek, 2024. "Households Vulnerable to Energy Poverty in the Visegrad Group Countries: An Analysis of Socio-Economic Factors Using a Machine Learning Approach," Energies, MDPI, vol. 17(24), pages 1-23, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Elpida Kalfountzou & Lefkothea Papada & Christos Tourkolias & Sevastianos Mirasgedis & Dimitris Kaliampakos & Dimitris Damigos, 2025. "A Comparative Analysis of Machine Learning Algorithms in Energy Poverty Prediction," Energies, MDPI, vol. 18(5), pages 1-20, February.
    2. Balkissoon, Sarah & Fox, Neil & Lupo, Anthony & Haupt, Sue Ellen & Penny, Stephen G. & Miller, Steve J. & Beetstra, Margaret & Sykuta, Michael & Ohler, Adrienne, 2024. "Forecasting energy poverty using different machine learning techniques for Missouri," Energy, Elsevier, vol. 313(C).
    3. Fu Wang & Hong Geng & Donglan Zha & Chaoqun Zhang, 2023. "Multidimensional Energy Poverty in China: Measurement and Spatio-Temporal Disparities Characteristics," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 168(1), pages 45-78, August.
    4. Spandagos, Constantine & Tovar Reaños, Miguel & Lynch, Muireann Á, 2023. "Energy poverty prediction and effective targeting for just transitions with machine learning," Papers WP762, Economic and Social Research Institute (ESRI).
    5. Huang, Yatao & Jiao, Wenxian & Wang, Kang & Li, Erling & Yan, Yutong & Chen, Jingyang & Guo, Xuanxuan, 2022. "Examining the multidimensional energy poverty trap and its determinants: An empirical analysis at household and community levels in six provinces of China," Energy Policy, Elsevier, vol. 169(C).
    6. Esperanza Vera‐Toscano & Heather Brown, 2022. "Empirical Evidence on the Incidence and Persistence of Energy Poverty in Australia," Australian Economic Review, The University of Melbourne, Melbourne Institute of Applied Economic and Social Research, vol. 55(4), pages 515-529, December.
    7. Deller, David & Turner, Glen & Waddams Price, Catherine, 2021. "Energy poverty indicators: Inconsistencies, implications and where next?," Energy Economics, Elsevier, vol. 103(C).
    8. Lin Zheng & Eoghan McKenna, 2025. "Machine Learning with Administrative Data for Energy Poverty Identification in the UK," Energies, MDPI, vol. 18(12), pages 1-26, June.
    9. Spandagos, Constantine & Tovar Reaños, Miguel Angel & Lynch, Muireann Á., 2023. "Energy poverty prediction and effective targeting for just transitions with machine learning," Energy Economics, Elsevier, vol. 128(C).
    10. Awan, Ashar & Bilgili, Faik & Rahut, Dil Bahadur, 2022. "Energy poverty trends and determinants in Pakistan: Empirical evidence from eight waves of HIES 1998–2019," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    11. Best, Rohan & Sinha, Kompal, 2021. "Fuel poverty policy: Go big or go home insulation," Energy Economics, Elsevier, vol. 97(C).
    12. Semple, Torran & Rodrigues, Lucelia & Harvey, John & Figueredo, Grazziela & Nica-Avram, Georgiana & Gillott, Mark & Milligan, Gregor & Goulding, James, 2024. "An empirical critique of the low income low energy efficiency approach to measuring fuel poverty," Energy Policy, Elsevier, vol. 186(C).
    13. Belaïd, Fateh, 2022. "Mapping and understanding the drivers of fuel poverty in emerging economies: The case of Egypt and Jordan," Energy Policy, Elsevier, vol. 162(C).
    14. Yun, Na, 2023. "Nexus among carbon intensity and natural resources utilization on economic development: Econometric analysis from China," Resources Policy, Elsevier, vol. 83(C).
    15. Pedro Moura & Paula Fonseca & Inês Cunha & Nuno Morais, 2024. "Diagnosing Energy Poverty in Portugal through the Lens of a Social Survey," Energies, MDPI, vol. 17(16), pages 1-28, August.
    16. Yuxiang Xie & E. Xie, 2023. "Measuring and Analyzing the Welfare Effects of Energy Poverty in Rural China Based on a Multi-Dimensional Energy Poverty Index," Sustainability, MDPI, vol. 15(18), pages 1-21, September.
    17. Blanka Tundys & Agnieszka Bretyn & Maciej Urbaniak, 2021. "Energy Poverty and Sustainable Economic Development: An Exploration of Correlations and Interdependencies in European Countries," Energies, MDPI, vol. 14(22), pages 1-25, November.
    18. Hache, Emmanuel & Leboullenger, Déborah & Mignon, Valérie, 2017. "Beyond average energy consumption in the French residential housing market: A household classification approach," Energy Policy, Elsevier, vol. 107(C), pages 82-95.
    19. Budría, Santiago & Fermé, Eduardo & Freitas, Diogo Nuno, 2025. "Toward Proactive Policy Design: Identifying 'To-Be' Energy-Poor Households Using Shap for Early Intervention," IZA Discussion Papers 17669, Institute of Labor Economics (IZA).
    20. Budría, Santiago & Bravo Chew, Leslie, 2025. "Enduring Inequalities: Analyzing Energy Poverty Inertia Across K-Means Clusters," IZA Discussion Papers 17809, Institute of Labor Economics (IZA).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:17:y:2024:i:13:p:3163-:d:1423469. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.