Modeling Energy Access Challenges in Europe: A Neural Network Approach to Predicting Household Heating Inadequacy Using Macro-Energy Indicators
Artykuł w czasopiśmie
MNiSW
140
Lista 2024
| Status: | |
| Autorzy: | Kulisz Monika, Kujawska Justyna, Cioch Michał, Cel Wojciech |
| Dyscypliny: | |
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| Rok wydania: | 2024 |
| Wersja dokumentu: | Drukowana | Elektroniczna |
| Język: | angielski |
| Numer czasopisma: | 23 |
| Wolumen/Tom: | 17 |
| Numer artykułu: | 6104 |
| Strony: | 1 - 14 |
| Impact Factor: | 3,0 |
| Efekt badań statutowych | NIE |
| Finansowanie: | 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. |
| Materiał konferencyjny: | NIE |
| Publikacja OA: | TAK |
| Licencja: | |
| Sposób udostępnienia: | Witryna wydawcy |
| Wersja tekstu: | Ostateczna wersja opublikowana |
| Czas opublikowania: | W momencie opublikowania |
| Data opublikowania w OA: | 4 grudnia 2024 |
| Abstrakty: | angielski |
| 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. |
