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This research was funded by the Polish Ministry of Science and Higher Education, grant
numbers: FD-NZ-020/2024, FD-20/IS-6/019, FD-NZ-066/2024, and FD-20/IS-6/003.
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.
