Volume 6 • Issue 1 • PP: 09–15 • 2026
Business Analytics for Green Electricity Transition Planning: Explainable Forecasting of Renewable Electricity Shares from Cross-Country Energy Indicators
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© 2026 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
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Renewable electricity growth is central to sustainable development, decarbonization, and green-technology planning. However, much of the forecasting literature remains focused on plant-level or narrow-horizon technical prediction, with limited attention to country-level decision support for investment screening, transition monitoring, and strategic benchmarking. This study develops a business analytics framework to forecast the renewable share of electricity generation and classify countries by renewable-transition level using a cross-country panel based on the Our World in Data Energy database. The empirical sample comprises 5,162 country-year observations from 213 countries over the period 2000–2025 and includes measures of electricity demand, electricity generation, primary energy use, greenhouse-gas emissions, and energy-system structure. Three regression models and three classification models were evaluated using a fixed train–test de sign. The random-forest regressor achieved the best continuous forecasting performance, with MAE = 3.536, RMSE = 6.466, and R2 = 0.960, while the random-forest classifier delivered the best tier-classification performance, with 93.998% accuracy and macro-F1 = 0.940. Feature-importance analysis identified greenhouse-gas emissions, energy intensity, electricity generation, electricity demand, and per-capita electricity consumption as the most influential predictors. The findings indicate that renewable-transition benchmarking can be framed as a managerial analytics problem, extending sustainability research beyond descriptive monitoring toward practical decision support for business and policy planning.
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References
[1] International Energy Agency, “Electricity 2024: Analysis and forecast to 2026,” International Energy Agency, Paris, France, Tech. Rep., Jan. 2024.
[2] International Energy Agency (IEA), “Renewables 2024: Analysis and forecasts to 2030,” International Energy Agency, Paris, France, Tech. Rep., Oct. 2024.
[3] International Renewable Energy Agency, “World energy transitions outlook 2024: 1.5 ◦C pathway,” International Renewable Energy Agency, Abu Dhabi, United Arab Emirates, Tech. Rep., Nov. 2024.
[4] R. Weron, “Electricity price forecasting: A review of the state-of-the-art with a look into the future,” International Journal of Forecasting, vol. 30, no. 4, pp. 1030– 1081, 2014.
[5] C. Voyant, G. Notton, S. Kalogirou, M.-L. Nivet, C. Paoli, F. Motte, and A. Fouilloy, “Machine learning methods for solar radiation forecasting: A review,” Renewable Energy, vol. 105, pp. 569–582, 2017.
[6] J. Lago, F. D. Ridder, and B. D. Schutter, “Forecasting day-ahead electricity prices: A review of state-ofthe- art algorithms, best practices and an open-access benchmark,” Applied Energy, vol. 293, p. 116983, 2021.
[7] R. Teixeira, A. Cerveira, E. J. S. Pires, and J. Baptista, “Advancing renewable energy forecasting: A comprehensive review of renewable energy forecasting methods,” Energies, vol. 17, no. 14, p. 3480, 2024.
[8] Our World in Data, “Data on energy by our world in data,” GitHub repository, 2026.
[9] A. Mosavi, M. Salimi, S. F. Ardabili, T. Rabczuk, S. Shamshirband, and A. R. Varkonyi-Koczy, “State of the art of machine learning models in energy systems,a systematic review,” Energies, vol. 12, no. 7, p. 1301, 2019.
[10] T. Hong, P. Pinson, Y. Wang, R. Weron, D. Yang, and H. Zareipour, “Energy forecasting: A review and outlook,” IEEE Open Access Journal of Power and Energy, vol. 7, pp. 376–388, 2020.
[11] N. E. Benti, M. D. Chaka, and A. G. Semie, “Forecasting renewable energy generation with machine learning and deep learning: Current advances and future prospects,” Sustainability, vol. 15, no. 9, p. 7087, 2023.
[12] S. Chatterjee, P. W. Khan, and Y.-C. Byun, “Recent advances and applications of machine learning in the variable renewable energy sector,” Energy Reports, vol. 12, pp. 5044–5065, 2024.
[13] C. Ying,W.Wang, J. Yu, Q. Li, D. Yu, and J. Liu, “Deep learning for renewable energy forecasting: A taxonomy, and systematic literature review,” Journal of Cleaner Production, vol. 384, p. 135414, 2023.
[14] A. Talwariya, P. Singh, J. H. Jobanputra, and M. L. Kolhe, “Machine learning based renewable energy generation and energy consumption forecasting,” Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, vol. 45, no. 2, pp. 3266–3278, 2023.
[15] Y. Yang, H. Lou, J.Wu, S. Zhang, and S. Gao, “A survey on wind power forecasting with machine learning approaches,” Neural Computing and Applications, vol. 36, pp. 12 753–12 773, 2024.
[16] V. Castro, M. Georgiou, T. Jackson, I. R. Hodgkinson, L. Jackson, and S. Lockwood, “Digital data demand and renewable energy limits: Forecasting the impacts on global electricity supply and sustainability,” Energy Policy, vol. 195, p. 114404, 2024.
[17] M. Arman, M. N. Hasan, and I. H. Rasel, “Clean energy transition in USA: Big data analytics for renewable energy forecasting and carbon reduction,” Journal of Management World, vol. 2024, no. 3, pp. 192–206, 2024.
[18] O. Awolesi, C. A. Salter, and M. Reams, “A systematic review on the path to inclusive and sustainable energy transitions,” Energies, vol. 17, no. 14, p. 3512, 2024.
[19] T. Ahmad, D. Zhang, C. Huang, H. Zhang, N. Dai, Y. Song, and H. Chen, “A review on renewable energy and electricity requirement forecasting models for smart grid and buildings,” Sustainable Cities and Society, vol. 55, p. 102052, 2020.
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