Williams Ossai
Machine learning-based predictive modelling of renewable energy adoption in developing countries
Ossai, Williams; Fagbola, Temitayo Matthew
Abstract
This study explores global renewable energy trends in alignment with the 2030 Sustainable Development Goals. Employing and fine-tuning the ExtraTreesRegressor, models were developed to predict adoption levels of electricity from solar, wind, hydro, and biomass sources. Strategic random search parameters were used to optimize the ExtraTreesRegressor. Evaluation based on Mean Square Error (MSE) and R-squared (R2) scores revealed that the ExtraTreesRegressor, outperformed other state-of-the-art regression models. Notably, the solar model exhibited commendable performance in test set evaluation (MSE: 0.4450, R2: 0.9849) and cross-validation (MSE: 4.3279, R2: 0.9079). Similarly, the wind model showed robust outcomes in both test set evaluation (MSE: 1.2233, R2: 0.9969) and cross-validation (MSE: 5.3136, R2: 0.9846). However, the hydro model faced nuanced challenges with test set evaluation (MSE: 33.3474, R2: 0.9960) and cross-validation (MSE: 20.4235, R2: 0.9961). The biomass model achieved notable results in test set evaluation (MSE: 0.3196, R2: 0.9960) and cross-validation (MSE: 0.5943, R2: 0.9901). Based on the findings from this study, GDP, non-renewable electricity consumption, and population size have been identified as key drivers of renewable energy adoption. Insights from this research will contribute to a deeper understanding of the intricate dynamics influencing renewable energy landscapes in developing countries.
Citation
Ossai, W., & Fagbola, T. M. (2025). Machine learning-based predictive modelling of renewable energy adoption in developing countries. Energy Reports, 14, 66-84. https://doi.org/10.1016/j.egyr.2025.05.066
Journal Article Type | Article |
---|---|
Acceptance Date | May 23, 2025 |
Online Publication Date | Jun 7, 2025 |
Publication Date | Dec 1, 2025 |
Deposit Date | Jun 7, 2025 |
Publicly Available Date | Jun 19, 2025 |
Journal | Energy Reports |
Print ISSN | 2352-4847 |
Publisher | Elsevier |
Peer Reviewed | Peer Reviewed |
Volume | 14 |
Pages | 66-84 |
DOI | https://doi.org/10.1016/j.egyr.2025.05.066 |
Keywords | Machine Learning; Renewable Energy Sources; Renewable Energy Penetration; Developing countries; Predictive Modelling; Sustainable Development Goals; ExtraTreesRegressor |
Public URL | https://hull-repository.worktribe.com/output/5235717 |
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Copyright Statement
© 2025 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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