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

Lefkothea Papada and Dimitris Kaliampakos ()
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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

Energies, 2024, vol. 17, issue 13, 1-19

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.

Keywords: energy poverty; Artificial Intelligence; Artificial Neural Networks; indicators; Greece (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: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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