Modeling Energy Access Challenges in Europe: A Neural Network Approach to Predicting Household Heating Inadequacy Using Macro-Energy Indicators

Monika Kulisz, Justyna Kujawska (), Michał Cioch and Wojciech Cel
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Monika Kulisz: Faculty of Management, Lublin University of Technology, 20-618 Lublin, Poland
Justyna Kujawska: Faculty of Environmental Engineering, Lublin University of Technology, 20-618 Lublin, Poland
Michał Cioch: Faculty of Management, Lublin University of Technology, 20-618 Lublin, Poland
Wojciech Cel: Faculty of Environmental Engineering, Lublin University of Technology, 20-618 Lublin, Poland

Energies, 2024, vol. 17, issue 23, 1-14

Abstract: This study explores the use of machine learning models to predict the percentage of the population unable to keep their houses adequately warm in European countries. The research focuses on applying three machine learning models—ElasticNet, decision trees, and neural networks—using macro-energy indicator data from Eurostat for 27 European countries. Neural networks with Bayesian regularization (BR) achieved the best performance in terms of prediction accuracy, with a regression value of 0.98179, and the lowest root mean squared error (RMSE) of 1.8981. The results demonstrate the superior ability of the BR algorithm to generalize data, outperforming other models like ElasticNet and decision trees, which also provided valuable insights but with lower precision. The findings highlight the potential of machine learning to predict the percentage of the population unable to keep their houses adequately warm, enabling policymakers to allocate resources more efficiently and target vulnerable populations. This research is the result of the application of machine learning models to solve the problem of energy poverty.

Keywords: energy poverty; machine learning; sustainable development; equitable energy access (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
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