Generative Artificial Intelligence in the Energy Transition: A Scoping Review
DOI:
https://doi.org/10.70577/asce.v5i3.922Palabras clave:
Artificial intelligence, Energy policy, Energy resources, Renewable energy sourcesResumen
The energy transition faced challenges associated with the integration of variable renewable energy sources, the management of uncertainty, and the growing complexity of energy systems, and generative artificial intelligence (AI) emerged as a tool that remained largely unsystematized. The objective of this study was to analyze and synthesize, through a Scoping Review conducted under the PRISMA-ScR guidelines, the scientific evidence on the use of generative AI in the energy transition, to identify its main application domains, methodological approaches, and research gaps.
The literature search was carried out on December 17, 2025, in IEEE Xplore, Scopus, Web of Science, and Springer Nature, under previously defined inclusion and exclusion criteria; peer-reviewed studies published between 2020 and 2026 were included, which resulted in a final corpus of 12 studies analyzed through a qualitative and descriptive approach. The included studies evidenced three main application axes: the generation of scenarios and synthetic data, the optimization of operational and maintenance processes, and support for decision-making in intelligent energy systems. Limitations persisted regarding limited validation in real-world settings, data dependency, computational scalability, and the absence of specific regulatory frameworks. It was concluded that generative AI constituted an emerging, cross-cutting approach within the energy transition, whose practical consolidation requires further empirical validation. The protocol was registered in the Open Science Framework (OSF) under code 10.17605/OSF.IO/BYM7A.
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Adewoyin, M. A. y Adediwin, O. y Audu, A. J. (2025). Artificial Intelligence and Sustainable Energy Development: A Review of Applications, Challenges, and Future Directions. International Journal of Multidisciplinary Research and Growth Evaluation., 6(2), 196-203. https://doi.org/10.54660/.ijmrge.2025.6.2.196-203 DOI: https://doi.org/10.54660/.IJMRGE.2025.6.2.196-203
Algburi, S. y Sabeeh Abed Al Kareem, S. y Sapaev, I. B. y Mukhitdinov, O. y Hassan, Q. y Khalaf, D. H. y Jabbar, F. I. (2025). The role of artificial intelligence in accelerating renewable energy adoption for global energy transformation. Unconventional Resources, 8. https://doi.org/10.1016/j.uncres.2025.100229 DOI: https://doi.org/10.1016/j.uncres.2025.100229
Alsaigh, R. y Mehmood, R. y Katib, I. (2023). AI explainability and governance in smart energy systems: A review. Frontiers in Energy Research, 11. https://doi.org/10.3389/fenrg.2023.1071291 DOI: https://doi.org/10.3389/fenrg.2023.1071291
Alsayegh, A. y Masood, T. (2024). Leveraging generative AI for knowledge-driven information retrieval in the energy sector. MATEC Web of Conferences, 401, 10008. https://doi.org/10.1051/matecconf/202440110008 DOI: https://doi.org/10.1051/matecconf/202440110008
Alvarez, D. I. y González-Ladrón-de-Guevara, F. y Rojas Espinoza, J. y Borge-Diez, D. y Galindo, S. P. y Flores-Vázquez, C. (2025). The Evolution of AI Applications in the Energy System Transition: A Bibliometric Analysis of Research Development, the Current State and Future Challenges. Energies, 18(6). https://doi.org/10.3390/en18061523 DOI: https://doi.org/10.3390/en18061523
Annapareddy, V. N. y Seenu, A. (2023). Generative AI in Predictive Maintenance and Performance Enhancement of Solar Battery Storage Systems. International Journal of Humanities Education.
Antonesi, G. y Cioara, T. y Anghel, I. y Michalakopoulos, V. y Sarmas, E. y Toderean, L. (2025). From Transformers to Large Language Models: A systematic review of AI applications in the energy sector towards Agentic Digital Twins. DOI: https://doi.org/10.1016/j.apenergy.2025.126670
Balogun Barnabas Friday y Dhakir Abbas Ali. (2024). Balancing Innovation and Sustainability: Assessing the Impact of Generative AI on Energy Consumption. International Journal of Innovative Research and Development, 13(9). https://doi.org/10.24940/ijird/2024/v13/i9/sep24021 DOI: https://doi.org/10.24940/ijird/2024/v13/i9/SEP24021
Benchikh, S. y Jarou, T. y Lamrani, R. (2024). Enhancing renewable energy systems with advanced artificial intelligence solutions. SCT Proceedings in Interdisciplinary Insights and Innovations, 2, 319. https://doi.org/10.56294/piii2024319 DOI: https://doi.org/10.56294/piii2024319
Benti, N. E. y Chaka, M. D. y Semie, A. G. (2023). Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects. Sustainability (Switzerland), 15(9). https://doi.org/10.3390/su15097087 DOI: https://doi.org/10.3390/su15097087
Briceño, C. P. y Rojas, M. y Mendez, I. y Ponce, P. y Mei, Q. (2025a). Harnessing Generative AI for Photovoltaic Systems to Enhance Safety Features of Tailored Industrial Solar Solutions. En A. Kumar (Ed.), Powering Future: Clean Energy Solutions for a Sustainable Planet.
Briceño, C. P. y Rojas, M. y Mendez, I. y Ponce, P. y Mei, Q. (2025b). Harnessing Generative AI for Photovoltaic Systems to Enhance Safety Features of Tailored Industrial Solar Solutions (pp. 213-235). https://doi.org/10.1007/978-981-96-5055-2_13 DOI: https://doi.org/10.1007/978-981-96-5055-2_13
Chatterjee, S. y Khan, P. W. y Byun, Y. C. (2024). Recent advances and applications of machine learning in the variable renewable energy sector. Energy Reports, 12, 5044-5065. https://doi.org/10.1016/j.egyr.2024.09.073 DOI: https://doi.org/10.1016/j.egyr.2024.09.073
Dui, H. y Zeng, Q. y Xie, M. (2025). Generative AI-based spatiotemporal resilience, green and low-carbon transformation strategy of smart renewable energy systems. Frontiers of Engineering Management, 12(4), 1220-1235. https://doi.org/10.1007/s42524-025-4147-6 DOI: https://doi.org/10.1007/s42524-025-4147-6
Dumas, J. y Lanaspeze, A. W. D. y Cornélusse, B. y Sutera, A. (2021). A deep generative model for probabilistic energy forecasting in power systems: normalizing flows. http://arxiv.org/abs/2106.09370 DOI: https://doi.org/10.1016/j.apenergy.2021.117871
Erdiwansyah y Mamat, R. y Syafrizal y Ghazali, M. F. y Basrawi, F. y Rosdi, S. M. (2025). Emerging role of generative AI in renewable energy forecasting and system optimization. Sustainable Chemistry for Climate Action, 7. https://doi.org/10.1016/j.scca.2025.100099 DOI: https://doi.org/10.1016/j.scca.2025.100099
Erhinyodavwe, O. y Eyide, O. y Enudi, M. y Anirejuoritse Mene, J. y Okuyade, O. S. (2024). Artificial Intelligence (AI) Generative Design and Machine Language Application in Solar and Wind Energy. INTERNATIONAL JOURNAL OF APPLIED & ADVANCED ENGINEERING RES, 05(5).
Figliola, A. y Barberio, M. y Del Razo Montiel, A. y Rienzo, P. (2025). Generative Artificial Intelligence (AI) and Sustainable-Aided Design: Opportunities and Challenges. 409-419. https://doi.org/10.1007/978-981-97-8588-9_39 DOI: https://doi.org/10.1007/978-981-97-8588-9_39
Gao, Y. y Miyata, S. y Akashi, Y. (2022). Multi-step solar irradiation prediction based on weather forecast and generative deep learning model. DOI: https://doi.org/10.1016/j.renene.2022.02.051
Ghazi Bishaw, F. y Khairi Ishak, M. y Hassan Atyia, T. (2024). Review Artificial Intelligence Applications in Renewable Energy Systems Integration. 566-582. DOI: https://doi.org/10.52783/jes.2983
Ginzburg-Ganz, E. y Horodi, E. D. y Shadafny, O. y Savir, U. y Machlev, R. y Levron, Y. (2025). Statistical Foundations of Generative AI for Optimal Control Problems in Power Systems: Comprehensive Review and Future Directions. Energies, 18(10). https://doi.org/10.3390/en18102461 DOI: https://doi.org/10.3390/en18102461
Glaws, A. y King, R. N. y Emami, P. y Buster, G. y Benton, B. N. y Zhang, X. y Zamzam, A. y Venkataramanan, V. y Macwan, R. (2025). Designing Future Energy Systems With
Generative AI. Computing in Science and Engineering, 27(4), 8-18. https://doi.org/10.1109/MCSE.2025.3567208 DOI: https://doi.org/10.1109/MCSE.2025.3567208
Goodfellow, I. y Pouget-Abadie, J. y Mirza, M. y Xu, B. y Warde-Farley, D. y Ozair, S. y Courville, A. y Bengio, Y. (2020). Generative adversarial networks. Communications of the ACM, 63(11), 139-144. https://doi.org/10.1145/3422622 DOI: https://doi.org/10.1145/3422622
Haputhanthri, D. y Samarajeewa, C. y De Silva, D. y Manic, M. y Mills, N. y Moraliyage, H. y Jennings, A. (2025). Generative AI Agents for Hyper Predictive Maintenance of Solar Energy
Systems. IECON Proceedings (Industrial Electronics Conference). https://doi.org/10.1109/IECON58223.2025.11221450 DOI: https://doi.org/10.1109/IECON58223.2025.11221450
James, M. (2025). Generative AI for Predictive Maintenance in Renewable Energy: Wind Turbines, Solar Farms, and Beyond.
Khan, S. y Mim, J. J. y Shorna, J. F. y Hasan, M. A. y Tarek, H. R. y Islam, M. A. y Hossain, N. (2026). Machine learning for renewable energy advancements: Prospects and emerging techniques. Energy Reports, 15. https://doi.org/10.1016/j.egyr.2025.109008 DOI: https://doi.org/10.1016/j.egyr.2025.109008
Kumarage, P. M. y Saarela, M. (2026). Explainable Generative AI: A Two-Stage Review of Existing Techniques and Future Research Directions. AI, 7(1), 31. https://doi.org/10.3390/ai7010031 DOI: https://doi.org/10.3390/ai7010031
Lin, Z. y Guo, Z. y Huang, D. y Song, C. y Tan, H. y Song, X. y Zhang, H. y Yan, J. (2023). Leveraging Generative AI for Renewable Energy: Photovoltaic Panel Semantic Segmentation Case Study. 36. DOI: https://doi.org/10.46855/energy-proceedings-11182
Majumder, S. y Dong, L. y Doudi, F. y Cai, Y. y Tian, C. y Kalathil, D. y Ding, K. y Thatte, A. A. y Li, N. y Xie, L. (2024). Exploring the capabilities and limitations of large language models in the electric energy sector. Joule, 8(6), 1544-1549. https://doi.org/10.1016/j.joule.2024.05.009 DOI: https://doi.org/10.1016/j.joule.2024.05.009
Meza Jimenez, V. y Perez, E. (2022). The Role of Artificial Intelligence in Latin America’s Energy Transition. DOI: https://doi.org/10.1109/TLA.2022.9904766
Mousavi, R. y Mousavi, A. y Mousavi, Y. y Tavasoli, M. y Arab, A. y Kucukdemiral, I. B. y Alfi, A. y Fekih, A. (2025). Revolutionizing solar energy resources: The central role of generative AI in elevating system sustainability and efficiency. Applied Energy, 382. https://doi.org/10.1016/j.apenergy.2025.125296 DOI: https://doi.org/10.1016/j.apenergy.2025.125296
Rane, N. L. (2024). Contribution of ChatGPT and Other Generative Artificial Intelligence (AI) in Renewable and Sustainable Energy. Journal of Advances in Artificial Intelligence, 2(1), 1-26. https://doi.org/10.18178/JAAI.2024.2.1.1-26 DOI: https://doi.org/10.18178/JAAI.2024.2.1.1-26
Razak, T. R. y Ismail, M. H. y Darus, M. Y. y Jarimi, H. y Su, Y. (2025). Artificial Intelligence in Renewable Energy: A Systematic Review of Trends in Solar, Wind, and Smart Grid Applications. Research and Reviews in Sustainability, 1(1), 1-22. https://doi.org/10.5334/rss.6 DOI: https://doi.org/10.5334/rss.6
Rolnick, D. y Donti, P. L. y Kaack, L. H. y Kochanski, K. y Lacoste, A. y Sankaran, K. y Ross, A. S. y Milojevic-Dupont, N. y Jaques, N. y Waldman-Brown, A. y Luccioni, A. S. y Maharaj, T. y Sherwin, E. D. y Mukkavilli, S. K. y Kording, K. P. y Gomes, C. P. y Ng, A. Y. y Hassabis, D. y Platt, J. C. y … Bengio, Y. (2023). Tackling Climate Change with Machine Learning. ACM Computing Surveys, 55(2). https://doi.org/10.1145/3485128 DOI: https://doi.org/10.1145/3485128
Ruan, J. y Liang, G. y Zhao, H. y Liu, G. y Sun, X. y Qiu, J. y Xu, Z. y Wen, F. y Dong, Z. Y. (2024). Applying Large Language Models to Power Systems: Potential Security Threats. http://arxiv.org/abs/2311.13361 DOI: https://doi.org/10.1109/TSG.2024.3373256
Safari, A. y Daneshvar, M. y Anvari-Moghaddam, A. (2024). Energy Intelligence: A Systematic Review of Artificial Intelligence for Energy Management. Applied Sciences (Switzerland), 14(23). https://doi.org/10.3390/app142311112 DOI: https://doi.org/10.3390/app142311112
Saif-Alyousfi, A. Y. H. y Alshammari, T. R. (2026). Artificial intelligence adoption and private investment in renewable energy: Evidence from firm-level data across developed and emerging economies. Energy Strategy Reviews, 64. https://doi.org/10.1016/j.esr.2026.102054 DOI: https://doi.org/10.1016/j.esr.2026.102054
Selvakumar, P. y Das, A. y Manjunath, T. C. (2026). Generative AI for Bioenergy System Design. En Ramirez, J. Reyes Juárez, Kumar, Abhishek, Rathore, & Pramod (Eds.), Enhancing Renewable Energy Systems With Generative AI. DOI: https://doi.org/10.4018/979-8-3373-4272-6.ch005
Silva, C. y Pereira, V. S. y Baptista, J. y Pinto, T. (2025). Artificial Intelligence and Energy. Energies, 18(18). https://doi.org/10.3390/en18184823 DOI: https://doi.org/10.3390/en18184823
Thirupathi, L. y Nitya, S. y Sathwik Reddy, K. (2026). Generative AI for Intelligent, Resilient, and Sustainable Renewable Energy Systems From Data to Deployment. En Ramírez, J. Reyes DOI: https://doi.org/10.4018/979-8-3373-4272-6.ch006
Juárez, Kumar, Abhishek, Rathore, & Pramod (Eds.), Enhancing Renewable Energy Systems With Generative AI.
Tricco, A. C. y Lillie, E. y Zarin, W. y O’Brien, K. y Colquhoun, H. y Kastner, M. y Levac, D. y Ng, C. y Sharpe, J. P. y Wilson, K. y Kenny, M. y Warren, R. y Wilson, C. y Stelfox, H. T. y Straus, S. E. (2016). A scoping review on the conduct and reporting of scoping reviews. BMC Medical Research Methodology, 16(1). https://doi.org/10.1186/s12874-016-0116-4 DOI: https://doi.org/10.1186/s12874-016-0116-4
Vamvakas, D. y Papaioannou, I. y Tsaknakis, C. y Sgouros, T. y Korkas, C. (2025). Generative AI for Sustainable Smart Environments: A Review of Energy Systems, Buildings, and User-Centric Decision-Making. Energies, 18(23). https://doi.org/10.3390/en18236163 DOI: https://doi.org/10.3390/en18236163
Velmurugan, R. y Sudarvel, J. y Bhuvaneswari, R. y Thirumalaisamy, R. (2026). A Study of Generative AI for Energy Consumption Behavior Analysis. En Ramirez, J. Reyes Juárez, Kumar, Abhishek, Rathore, & Pramod (Eds.), Enhancing Renewable Energy Systems With Generative AI. DOI: https://doi.org/10.4018/979-8-3373-4272-6.ch009
Xiang, H. y Li, X. y Liao, X. y Cui, W. y Liu, F. y Li, D. (2025). Artificial Intelligence in Renewable Energy Systems: Applications and Security Challenges. En Energies (Vol. 18, Número 8). Multidisciplinary Digital Publishing Institute (MDPI). https://doi.org/10.3390/en18081931 DOI: https://doi.org/10.3390/en18081931
Zhang, T. y Strbac, G. (2025). Novel Artificial Intelligence Applications in Energy: A Systematic Review. Energies, 18(14). https://doi.org/10.3390/en18143747 DOI: https://doi.org/10.3390/en18143747
Zinoviev, V. y Koeva, D. y Tsankov, P. y Kutkarska, R. (2026). Review of Artificial Intelligence Applications in the Digital Energy and Renewable Energy Infrastructures. Energies, 19(5), 1250. https://doi.org/10.3390/en19051250 DOI: https://doi.org/10.3390/en19051250
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