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

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  • 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
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    References listed on IDEAS

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    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.
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    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.

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