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Machine learning-based predictive modelling of renewable energy adoption in developing countries

Ossai, Williams; Fagbola, Temitayo Matthew

Authors

Williams Ossai



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
This output contributes to the following UN Sustainable Development Goals:

SDG 11 - Sustainable Cities and Communities

Make cities and human settlements inclusive, safe, resilient and sustainable

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