Volume 6 • Issue 1 • PP: 13–22 • 2026
Business Analytics for Green Electricity Transition Planning: Explainable Forecasting of Renewable Electricity Shares from Cross-Country Energy Indicators
Open Access & Copyright
© 2026 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
View PDF Abstract
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.
Keywords
References
[1] Tanveer Ahmad, Dongdong Zhang, Chong Huang, Hui Zhang, Ning Dai, Yabin Song, and Huanhuan Chen. A review on renewable energy and electricity requirement forecasting models for smart grid and buildings. Sustainable Cities and Society, 55:102052, 2020. doi: 10.1016/j.scs.2020.102052.
[2] Muhibbul Arman, Md Nazmul Hasan, and Imran Hossain Rasel. Clean energy transition in usa: Big data analytics for renewable energy forecasting and carbon reduction. Journal of Management World, 2024 (3):192–206, 2024. doi: 10.53935/jomw.v2024i4.1196.
[3] Oluwafemi Awolesi, Corinne A. Salter, and Margaret Reams. A systematic review on the path to inclusive and sustainable energy transitions. Energies, 17(14):3512, 2024. doi: 10.3390/en17143512.
[4] Natei Ermias Benti, Mesfin Diro Chaka, and Addisu Gezahegn Semie. Forecasting renewable energy generation with machine learning and deep learning: Current advances and future prospects. Sustainability, 15(9):7087, 2023. doi: 10.3390/su15097087.
[5] Vitor Castro, Marios Georgiou, Thomas Jackson, Ian R. Hodgkinson, Lisa Jackson, and Steve Lockwood. Digital data demand and renewable energy limits: Forecasting the impacts on global electricity supply and sustainability. Energy Policy, 195:114404, 2024. doi: 10.1016/j.enpol.2024.114404.
[6] Subhajit Chatterjee, Prince Waqas Khan, and Yung-Cheol Byun. Recent advances and applications of machine learning in the variable renewable energy sector. Energy Reports, 12:5044–5065, 2024. doi: 10.1016/j.egyr.2024.09.073.
[7] Tao Hong, Pierre Pinson, Yi Wang, Rafał Weron, Dazhi Yang, and Hamidreza Zareipour. Energy forecasting: A review and outlook. IEEE Open Access Journal of Power and Energy, 7:376–388, 2020. doi: 10.1109/OAJPE.2020.3029979.
[8] International Energy Agency. Electricity 2024: Analysis and forecast to 2026, 2024. URL https: //www.iea.org/reports/electricity-2024. Published January 24, 2024.
[9] International Energy Agency. Renewables 2024: Analysis and forecast to 2030, 2024. URL https: //www.iea.org/reports/renewables-2024. Published 2024.
[10] International Renewable Energy Agency. World energy transitions outlook 2024: 1.5◦c pathway, 2024. URL https://www.irena.org/Publications/2024/Nov/ World-Energy-Transitions-Outlook-2024. Published November 2024.
[11] Jes´us Lago, Fisseha De Ridder, and Bart De Schutter. Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark. Applied Energy, 293: 116983, 2021. doi: 10.1016/j.apenergy.2021.116983.
[12] Amir Mosavi, Mohsen Salimi, Sina Faizollahzadeh Ardabili, Timon Rabczuk, Shahaboddin Shamshirband, and Annamaria R. Varkonyi-Koczy. State of the art of machine learning models in energy systems, a systematic review. Energies, 12(7):1301, 2019. doi: 10.3390/en12071301.
[13] Our World in Data. Data on energy by our world in data. GitHub repository, 2026. URL https: //github.com/owid/energy-data. Accessed April 14, 2026.
[14] Akash Talwariya, Pushpendra Singh, Jalpa H. Jobanputra, and Mohan Lal Kolhe. Machine learning based renewable energy generation and energy consumption forecasting. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 45(2):3266–3278, 2023. doi: 10.1080/15567036.2023.2194257.
[15] Rita Teixeira, Adelaide Cerveira, Eduardo J. Solteiro Pires, and Jos´e Baptista. Advancing renewable energy forecasting: A comprehensive review of renewable energy forecasting methods. Energies, 17 (14):3480, 2024. doi: 10.3390/en17143480.
[16] Cyril Voyant, Gilles Notton, Soteris Kalogirou, Marie-Laure Nivet, Christophe Paoli, Fabrice Motte, and Alexis Fouilloy. Machine learning methods for solar radiation forecasting: A review. Renewable Energy, 105:569–582, 2017. doi: 10.1016/j.renene.2016.12.095.
[17] Rafał Weron. Electricity price forecasting: A review of the state-of-the-art with a look into the future. International Journal of Forecasting, 30(4):1030–1081, 2014. doi: 10.1016/j.ijforecast.2014.08.008.
[18] Yang Yang, Hao Lou, Jinran Wu, Shaotong Zhang, and Shangce Gao. A survey on wind power forecasting with machine learning approaches. Neural Computing and Applications, 36:12753–12773, 2024. doi: 10.1007/s00521-024-09923-4.
[19] Changtian Ying,WeiqingWang, Jiong Yu, Qi Li, Donghua Yu, and Jianhua Liu. Deep learning for renewable energy forecasting: A taxonomy, and systematic literature review. Journal of Cleaner Production, 384:135414, 2023. doi: 10.1016/j.jclepro.2022.135414.
Cite This Article
Choose your preferred format
Publisher's Note
The statements, opinions, and data presented in this article are solely those of the author(s) and do not necessarily represent those of ASPG, the journal, or its editors. ASPG and the editors disclaim responsibility for any harm arising from the use of any ideas, methods, instructions, or products described in this article, to the fullest extent permitted by applicable law.