IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v382y2025ics0306261925000261.html
   My bibliography  Save this article

Revolutionizing solar energy resources: The central role of generative AI in elevating system sustainability and efficiency

Author

Listed:
  • Mousavi, Rashin
  • Mousavi, Arash
  • Mousavi, Yashar
  • Tavasoli, Mahsa
  • Arab, Aliasghar
  • Kucukdemiral, Ibrahim Beklan
  • Alfi, Alireza
  • Fekih, Afef

Abstract

Driven by growing environmental concerns, such as global warming and the depletion of fossil fuels, the renewable energy industry, particularly solar energy, has risen to global prominence. In this context, generative artificial intelligence (Gen-AI) can play a valuable role in facilitating the development of more efficient, durable, and adaptable solar systems. Gen-AI’s multifaceted proficiency, from predictive maintenance and reducing downtime and costs to vital forecasting for grid management and strategic planning, extends to optimizing site selection for solar farms and smart grid integration, thereby enhancing solar energy flow, grid stability, and sustainable operation. This paper presents a comprehensive exploration of the role of Gen-AI in revolutionizing the solar energy industry. Focusing on various aspects of solar energy systems, including design, optimization, sizing, maintenance, energy forecasting, site selection, and smart grid integration, the study investigates the transformative impact of Gen-AI across these domains. It demonstrates how Gen-AI enhances the efficiency, sustainability, and adaptability of solar systems, driving strategic decision-making and optimizing the integration of solar power within complex energy ecosystems. Furthermore, the paper concludes by discussing the challenges and future prospects of employing Gen-AI in the solar energy domain, providing a comparative analysis of the current and future scenarios, and underscoring the advantages, disadvantages, and challenges of Gen-AI implementation.

Suggested Citation

  • Mousavi, Rashin & Mousavi, Arash & Mousavi, Yashar & Tavasoli, Mahsa & Arab, Aliasghar & Kucukdemiral, Ibrahim Beklan & Alfi, Alireza & Fekih, Afef, 2025. "Revolutionizing solar energy resources: The central role of generative AI in elevating system sustainability and efficiency," Applied Energy, Elsevier, vol. 382(C).
    • Handle: RePEc:eee:appene:v:382:y:2025:i:c:s0306261925000261
    DOI: 10.1016/j.apenergy.2025.125296
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261925000261
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2025.125296?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Hanif, M.F. & Mi, J., 2024. "Harnessing AI for solar energy: Emergence of transformer models," Applied Energy, Elsevier, vol. 369(C).
    2. Rodríguez, Fermín & Fleetwood, Alice & Galarza, Ainhoa & Fontán, Luis, 2018. "Predicting solar energy generation through artificial neural networks using weather forecasts for microgrid control," Renewable Energy, Elsevier, vol. 126(C), pages 855-864.
    3. Yin, Linfei & Zhang, Bin, 2021. "Time series generative adversarial network controller for long-term smart generation control of microgrids," Applied Energy, Elsevier, vol. 281(C).
    4. Yildiz, Baran & Stringer, Naomi & Klymenko, Timothy & Syahman Samhan, Muhammad & Abramowitz, Greg & Bruce, Anna & MacGill, Iain & Egan, Renate & Sproul, Alistair B., 2023. "Real-world data analysis of distributed PV and battery energy storage system curtailment in low voltage networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 186(C).
    5. Anoune, Kamal & Bouya, Mohsine & Astito, Abdelali & Abdellah, Abdellatif Ben, 2018. "Sizing methods and optimization techniques for PV-wind based hybrid renewable energy system: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 93(C), pages 652-673.
    6. Baigorri, Javier & Zaversky, Fritz & Astrain, David, 2023. "Massive grid-scale energy storage for next-generation concentrated solar power: A review of the potential emerging concepts," Renewable and Sustainable Energy Reviews, Elsevier, vol. 185(C).
    7. Van Limbergen, T. & Bonné, R. & Hustings, J. & Valcke, R. & Thijs, S. & Vangronsveld, J. & Manca, J.V., 2022. "Plant microbial fuel cells from the perspective of photovoltaics: Efficiency, power, and applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 169(C).
    8. Wen, Haoran & Du, Yang & Chen, Xiaoyang & Lim, Eng Gee & Wen, Huiqing & Yan, Ke, 2023. "A regional solar forecasting approach using generative adversarial networks with solar irradiance maps," Renewable Energy, Elsevier, vol. 216(C).
    9. Khezri, Rahmat & Mahmoudi, Amin & Aki, Hirohisa, 2022. "Optimal planning of solar photovoltaic and battery storage systems for grid-connected residential sector: Review, challenges and new perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
    10. Mellit, Adel & Kalogirou, Soteris, 2021. "Artificial intelligence and internet of things to improve efficacy of diagnosis and remote sensing of solar photovoltaic systems: Challenges, recommendations and future directions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
    11. Markos A. Kousounadis-Knousen & Ioannis K. Bazionis & Athina P. Georgilaki & Francky Catthoor & Pavlos S. Georgilakis, 2023. "A Review of Solar Power Scenario Generation Methods with Focus on Weather Classifications, Temporal Horizons, and Deep Generative Models," Energies, MDPI, vol. 16(15), pages 1-29, July.
    12. Sun, Jingbo & Wang, Yang & He, Yuan & Cui, Wenrui & Chao, Qingchen & Shan, Baoguo & Wang, Zheng & Yang, Xiaofan, 2024. "The energy security risk assessment of inefficient wind and solar resources under carbon neutrality in China," Applied Energy, Elsevier, vol. 360(C).
    13. Shi, Zhongtuo & Yao, Wei & Li, Zhouping & Zeng, Lingkang & Zhao, Yifan & Zhang, Runfeng & Tang, Yong & Wen, Jinyu, 2020. "Artificial intelligence techniques for stability analysis and control in smart grids: Methodologies, applications, challenges and future directions," Applied Energy, Elsevier, vol. 278(C).
    14. Ahmad, Tanveer & Madonski, Rafal & Zhang, Dongdong & Huang, Chao & Mujeeb, Asad, 2022. "Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    15. Ewa Chodakowska & Joanicjusz Nazarko & Łukasz Nazarko & Hesham S. Rabayah & Raed M. Abendeh & Rami Alawneh, 2023. "ARIMA Models in Solar Radiation Forecasting in Different Geographic Locations," Energies, MDPI, vol. 16(13), pages 1-24, June.
    16. Mohammad Navid Fekri & Ananda Mohon Ghosh & Katarina Grolinger, 2019. "Generating Energy Data for Machine Learning with Recurrent Generative Adversarial Networks," Energies, MDPI, vol. 13(1), pages 1-23, December.
    17. Vaidya, Omkarprasad S. & Kumar, Sushil, 2006. "Analytic hierarchy process: An overview of applications," European Journal of Operational Research, Elsevier, vol. 169(1), pages 1-29, February.
    18. Elfasakhany, Ashraf, 2016. "Performance assessment and productivity of a simple-type solar still integrated with nanocomposite energy storage system," Applied Energy, Elsevier, vol. 183(C), pages 399-407.
    19. Sorrenti, Ilaria & Harild Rasmussen, Theis Bo & You, Shi & Wu, Qiuwei, 2022. "The role of power-to-X in hybrid renewable energy systems: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 165(C).
    20. Robertson, Bryson & Bekker, Jessica & Buckham, Bradley, 2020. "Renewable integration for remote communities: Comparative allowable cost analyses for hydro, solar and wave energy," Applied Energy, Elsevier, vol. 264(C).
    21. Carl-Friedrich Schleussner & Joeri Rogelj & Michiel Schaeffer & Tabea Lissner & Rachel Licker & Erich M. Fischer & Reto Knutti & Anders Levermann & Katja Frieler & William Hare, 2016. "Science and policy characteristics of the Paris Agreement temperature goal," Nature Climate Change, Nature, vol. 6(9), pages 827-835, September.
    22. Yang, Yuqing & Bremner, Stephen & Menictas, Chris & Kay, Merlinde, 2018. "Battery energy storage system size determination in renewable energy systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 109-125.
    23. Ellabban, Omar & Abu-Rub, Haitham & Blaabjerg, Frede, 2014. "Renewable energy resources: Current status, future prospects and their enabling technology," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 748-764.
    24. Peinado Gonzalo, Alfredo & Pliego Marugán, Alberto & García Márquez, Fausto Pedro, 2020. "Survey of maintenance management for photovoltaic power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 134(C).
    25. Mousavi, Yashar & Bevan, Geraint & Kucukdemiral, Ibrahim Beklan & Fekih, Afef, 2022. "Sliding mode control of wind energy conversion systems: Trends and applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    26. Ajay Bandi & Pydi Venkata Satya Ramesh Adapa & Yudu Eswar Vinay Pratap Kumar Kuchi, 2023. "The Power of Generative AI: A Review of Requirements, Models, Input–Output Formats, Evaluation Metrics, and Challenges," Future Internet, MDPI, vol. 15(8), pages 1-60, July.
    27. Li, Fengyun & Zheng, Haofeng & Li, Xingmei, 2022. "A novel hybrid model for multi-step ahead photovoltaic power prediction based on conditional time series generative adversarial networks," Renewable Energy, Elsevier, vol. 199(C), pages 560-586.
    28. Zhang, Gang & Yang, Dazhi & Galanis, George & Androulakis, Emmanouil, 2022. "Solar forecasting with hourly updated numerical weather prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 154(C).
    29. He, Zhaoyu & Guo, Weimin & Zhang, Peng, 2022. "Performance prediction, optimal design and operational control of thermal energy storage using artificial intelligence methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    30. Dwivedi, Yogesh K. & Hughes, Laurie & Ismagilova, Elvira & Aarts, Gert & Coombs, Crispin & Crick, Tom & Duan, Yanqing & Dwivedi, Rohita & Edwards, John & Eirug, Aled & Galanos, Vassilis & Ilavarasan, , 2021. "Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy," International Journal of Information Management, Elsevier, vol. 57(C).
    31. Dong, Wei & Chen, Xianqing & Yang, Qiang, 2022. "Data-driven scenario generation of renewable energy production based on controllable generative adversarial networks with interpretability," Applied Energy, Elsevier, vol. 308(C).
    32. Liu, Canfa & Duan, Guihui & Zhang, Bin & Liu, Yanjun & Yang, Dongsheng & Shuai, Yong & Wang, Zhaolong, 2024. "Precisely nanofabricated cross shaped metamaterial absorbers for nearly perfect solar energy harvesting," Applied Energy, Elsevier, vol. 372(C).
    33. Saba Sedayevatan & Armida Bahrami & Fatemeh Delfani & Ali Sohani, 2023. "Uncertainty Covered Techno-Enviro-Economic Viability Evaluation of a Solar Still Water Desalination Unit Using Monte Carlo Approach," Energies, MDPI, vol. 16(19), pages 1-13, October.
    34. Juan D. Velásquez & Lorena Cadavid & Carlos J. Franco, 2023. "Intelligence Techniques in Sustainable Energy: Analysis of a Decade of Advances," Energies, MDPI, vol. 16(19), pages 1-45, October.
    35. Gaviria, Jorge Felipe & Narváez, Gabriel & Guillen, Camilo & Giraldo, Luis Felipe & Bressan, Michael, 2022. "Machine learning in photovoltaic systems: A review," Renewable Energy, Elsevier, vol. 196(C), pages 298-318.
    36. Arafat, M.Y. & Hossain, M.J. & Alam, Md Morshed, 2024. "Machine learning scopes on microgrid predictive maintenance: Potential frameworks, challenges, and prospects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 190(PA).
    37. Huang, Xiaoqiao & Li, Qiong & Tai, Yonghang & Chen, Zaiqing & Liu, Jun & Shi, Junsheng & Liu, Wuming, 2022. "Time series forecasting for hourly photovoltaic power using conditional generative adversarial network and Bi-LSTM," Energy, Elsevier, vol. 246(C).
    38. Varaha Satra Bharath Kurukuru & Ahteshamul Haque & Mohammed Ali Khan & Subham Sahoo & Azra Malik & Frede Blaabjerg, 2021. "A Review on Artificial Intelligence Applications for Grid-Connected Solar Photovoltaic Systems," Energies, MDPI, vol. 14(15), pages 1-35, August.
    39. Rehman, Anis Ur & Ullah, Zia & Qazi, Hasan Saeed & Hasanien, Hany M. & Khalid, Haris M., 2024. "Reinforcement learning-driven proximal policy optimization-based voltage control for PV and WT integrated power system," Renewable Energy, Elsevier, vol. 227(C).
    40. Guerrero-Lemus, R. & Vega, R. & Kim, Taehyeon & Kimm, Amy & Shephard, L.E., 2016. "Bifacial solar photovoltaics – A technology review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 1533-1549.
    41. Yang, Dazhi, 2018. "A correct validation of the National Solar Radiation Data Base (NSRDB)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 97(C), pages 152-155.
    42. Kannan, Nadarajah & Vakeesan, Divagar, 2016. "Solar energy for future world: - A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 1092-1105.
    43. Jha, Sunil Kr. & Bilalovic, Jasmin & Jha, Anju & Patel, Nilesh & Zhang, Han, 2017. "Renewable energy: Present research and future scope of Artificial Intelligence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 297-317.
    44. Gao, Yuan & Miyata, Shohei & Akashi, Yasunori, 2022. "Multi-step solar irradiation prediction based on weather forecast and generative deep learning model," Renewable Energy, Elsevier, vol. 188(C), pages 637-650.
    45. Ghritlahre, Harish Kumar & Prasad, Radha Krishna, 2018. "Application of ANN technique to predict the performance of solar collector systems - A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 84(C), pages 75-88.
    46. Kow, Ken Weng & Wong, Yee Wan & Rajkumar, Rajparthiban Kumar & Rajkumar, Rajprasad Kumar, 2016. "A review on performance of artificial intelligence and conventional method in mitigating PV grid-tied related power quality events," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 334-346.
    47. Kalogirou, S.A. & Mathioulakis, E. & Belessiotis, V., 2014. "Artificial neural networks for the performance prediction of large solar systems," Renewable Energy, Elsevier, vol. 63(C), pages 90-97.
    48. Haobo Shi & Yanping Xu & Baodi Ding & Jinsong Zhou & Pei Zhang, 2023. "Long-Term Solar Power Time-Series Data Generation Method Based on Generative Adversarial Networks and Sunrise–Sunset Time Correction," Sustainability, MDPI, vol. 15(20), pages 1-19, October.
    49. Youssef, Ayman & El-Telbany, Mohammed & Zekry, Abdelhalim, 2017. "The role of artificial intelligence in photo-voltaic systems design and control: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 72-79.
    50. Han, Kunlun & Yang, Kai & Yin, Linfei, 2022. "Lightweight actor-critic generative adversarial networks for real-time smart generation control of microgrids," Applied Energy, Elsevier, vol. 317(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Luis Martin Dibene Arriola & Fátima Maciel Carrillo González & Néstor Daniel Galán Hernández & Eber Enrique Orozco Guillen & Juan Francisco Mercado Arias & César Paul Paredes Vázquez, 2025. "Agrivoltaics in Tropical Climates: Distributed Generation Proposal for CO 2 Reduction in Luxury Hotels," Energies, MDPI, vol. 18(11), pages 1-23, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhang, Xiangyu & Glaws, Andrew & Cortiella, Alexandre & Emami, Patrick & King, Ryan N., 2025. "Deep generative models in energy system applications: Review, challenges, and future directions," Applied Energy, Elsevier, vol. 380(C).
    2. Nadimi, Reza & Goto, Mika, 2025. "A novel decision support system for enhancing long-term forecast accuracy in virtual power plants using bidirectional long short-term memory networks," Applied Energy, Elsevier, vol. 382(C).
    3. Li, B. & Delpha, C. & Diallo, D. & Migan-Dubois, A., 2021. "Application of Artificial Neural Networks to photovoltaic fault detection and diagnosis: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    4. Liu, Jingxuan & Zang, Haixiang & Zhang, Fengchun & Cheng, Lilin & Ding, Tao & Wei, Zhinong & Sun, Guoqiang, 2023. "A hybrid meteorological data simulation framework based on time-series generative adversarial network for global daily solar radiation estimation," Renewable Energy, Elsevier, vol. 219(P1).
    5. Chen, Zhiqiang & Li, Jianbin & Cheng, Long & Liu, Xiufeng, 2023. "Federated-WDCGAN: A federated smart meter data sharing framework for privacy preservation," Applied Energy, Elsevier, vol. 334(C).
    6. Elinor Ginzburg-Ganz & Eden Dina Horodi & Omar Shadafny & Uri Savir & Ram Machlev & Yoash Levron, 2025. "Statistical Foundations of Generative AI for Optimal Control Problems in Power Systems: Comprehensive Review and Future Directions," Energies, MDPI, vol. 18(10), pages 1-54, May.
    7. Wang, Yuwei & Song, Minghao & Jia, Mengyao & Shi, Lin & Li, Bingkang, 2023. "TimeGAN based distributionally robust optimization for biomass-photovoltaic-hydrogen scheduling under source-load-market uncertainties," Energy, Elsevier, vol. 284(C).
    8. Yin, Linfei & Lin, Chen, 2024. "Matrix Wasserstein distance generative adversarial network with gradient penalty for fast low-carbon economic dispatch of novel power systems," Energy, Elsevier, vol. 298(C).
    9. Xu, Yingying & Shao, Xuefeng & Tanasescu, Cristina, 2024. "How are artificial intelligence, carbon market, and energy sector connected? A systematic analysis of time-frequency spillovers," Energy Economics, Elsevier, vol. 132(C).
    10. Zhang, Haoran & Yu, Xiaohong & Gao, Zixuan, 2025. "Is the end of AI in photovoltaic power? Evidence from China," Energy Economics, Elsevier, vol. 145(C).
    11. Mousavi, Navid & Kothapalli, Ganesh & Habibi, Daryoush & Das, Choton K. & Baniasadi, Ali, 2020. "A novel photovoltaic-pumped hydro storage microgrid applicable to rural areas," Applied Energy, Elsevier, vol. 262(C).
    12. Wang, Y. & Mauree, D. & Sun, Q. & Lin, H. & Scartezzini, J.L. & Wennersten, R., 2020. "A review of approaches to low-carbon transition of high-rise residential buildings in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    13. Moghadam, Saman Salehi & Gholamian, Mohammad Reza & Zahedi, Rahim & Shaqaqifar, Maziar, 2024. "Designing a multi-purpose network of sustainable and closed-loop renewable energy supply chain, considering reliability and circular economy," Applied Energy, Elsevier, vol. 369(C).
    14. Yin, Linfei & He, Xiaoyu, 2023. "Artificial emotional deep Q learning for real-time smart voltage control of cyber-physical social power systems," Energy, Elsevier, vol. 273(C).
    15. Xiao, Zenan & Huang, Xiaoqiao & Liu, Jun & Li, Chengli & Tai, Yonghang, 2023. "A novel method based on time series ensemble model for hourly photovoltaic power prediction," Energy, Elsevier, vol. 276(C).
    16. Yin, Linfei & Zhang, Bin, 2023. "Relaxed deep generative adversarial networks for real-time economic smart generation dispatch and control of integrated energy systems," Applied Energy, Elsevier, vol. 330(PA).
    17. Roldán-Blay, Carlos & Abad-Rodríguez, Manuel Francisco & Abad-Giner, Víctor & Serrano-Guerrero, Xavier, 2024. "Interval-based solar photovoltaic energy predictions: A single-parameter approach with direct radiation focus," Renewable Energy, Elsevier, vol. 230(C).
    18. Yap, Kah Yung & Chin, Hon Huin & Klemeš, Jiří Jaromír, 2022. "Solar Energy-Powered Battery Electric Vehicle charging stations: Current development and future prospect review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 169(C).
    19. Unterberger, Viktor & Lichtenegger, Klaus & Kaisermayer, Valentin & Gölles, Markus & Horn, Martin, 2021. "An adaptive short-term forecasting method for the energy yield of flat-plate solar collector systems," Applied Energy, Elsevier, vol. 293(C).
    20. Toopshekan, Ashkan & Abedian, Ali & Azizi, Arian & Ahmadi, Esmaeil & Vaziri Rad, Mohammad Amin, 2023. "Optimization of a CHP system using a forecasting dispatch and teaching-learning-based optimization algorithm," Energy, Elsevier, vol. 285(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:382:y:2025:i:c:s0306261925000261. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.