IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v318y2025ics0360544225003536.html
   My bibliography  Save this article

Perspectives for artificial intelligence in sustainable energy systems

Author

Listed:
  • Chen, Dongyu
  • Lin, Xiaojie
  • Qiao, Yiyuan

Abstract

This forward-looking perspective introduces the current applications of AI in sustainable energy systems, focusing on machine learning (ML) in three key areas: (i) system modeling and prediction, (ii) energy operation and management, and (iii) anomaly detection and diagnostics. For future low-carbon, decentralized and multi-energy systems, increasing complexity and communication pose challenges for system forecasting, operational control, grid planning, and energy security. AI offers revolutionary solutions by enhancing renewable energy integration, optimizing energy storage, and improving fault detection and cybersecurity. However, AI methods face limitations, including dependence on extensive data, lack of physical interpretability, and issues of transferability and robustness, hindering broader adoption in the energy sector. Therefore, perspectives are offered on four aspects: (1) developing generative AI to provide synthetic energy data, (2) adopting physics-informed AI to mitigate inherent AI limitations, (3) utilizing AI-based control and energy planning to address multi-energy complexities, and (4) implementing layered AI-based cybersecurity measures to defend smart energy systems. Overall, this perspective provides insights into the evolving role of AI in future energy systems.

Suggested Citation

  • Chen, Dongyu & Lin, Xiaojie & Qiao, Yiyuan, 2025. "Perspectives for artificial intelligence in sustainable energy systems," Energy, Elsevier, vol. 318(C).
    • Handle: RePEc:eee:energy:v:318:y:2025:i:c:s0360544225003536
    DOI: 10.1016/j.energy.2025.134711
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544225003536
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2025.134711?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. Salman Khalid & Jinwoo Song & Izaz Raouf & Heung Soo Kim, 2023. "Advances in Fault Detection and Diagnosis for Thermal Power Plants: A Review of Intelligent Techniques," Mathematics, MDPI, vol. 11(8), pages 1-28, April.
    2. Ngo, Quang-Ha & Nguyen, Bang L.H. & Vu, Tuyen V. & Zhang, Jianhua & Ngo, Tuan, 2024. "Physics-informed graphical neural network for power system state estimation," Applied Energy, Elsevier, vol. 358(C).
    3. Ma, Yichuan X. & Yeung, Lawrence K., 2024. "BEForeGAN: An image-based deep generative approach for day-ahead forecasting of building HVAC energy consumption," Applied Energy, Elsevier, vol. 376(PA).
    4. Gokhale, Gargya & Claessens, Bert & Develder, Chris, 2022. "Physics informed neural networks for control oriented thermal modeling of buildings," Applied Energy, Elsevier, vol. 314(C).
    5. Zhou, Yanting & Ma, Zhongjing & Zhang, Jinhui & Zou, Suli, 2022. "Data-driven stochastic energy management of multi energy system using deep reinforcement learning," Energy, Elsevier, vol. 261(PA).
    6. Li, Yang & Wang, Ruinong & Li, Yuanzheng & Zhang, Meng & Long, Chao, 2023. "Wind power forecasting considering data privacy protection: A federated deep reinforcement learning approach," Applied Energy, Elsevier, vol. 329(C).
    7. Zhuang, Dian & Gan, Vincent J.L. & Duygu Tekler, Zeynep & Chong, Adrian & Tian, Shuai & Shi, Xing, 2023. "Data-driven predictive control for smart HVAC system in IoT-integrated buildings with time-series forecasting and reinforcement learning," Applied Energy, Elsevier, vol. 338(C).
    8. Dumas, Jonathan & Wehenkel, Antoine & Lanaspeze, Damien & Cornélusse, Bertrand & Sutera, Antonio, 2022. "A deep generative model for probabilistic energy forecasting in power systems: normalizing flows," Applied Energy, Elsevier, vol. 305(C).
    9. Tan, Hongjun & Guo, Zhiling & Lin, Zhengyuan & Chen, Yuntian & Huang, Dou & Yuan, Wei & Zhang, Haoran & Yan, Jinyue, 2024. "General generative AI-based image augmentation method for robust rooftop PV segmentation," Applied Energy, Elsevier, vol. 368(C).
    10. Xie, Jiahan & Ajagekar, Akshay & You, Fengqi, 2023. "Multi-Agent attention-based deep reinforcement learning for demand response in grid-responsive buildings," Applied Energy, Elsevier, vol. 342(C).
    11. Singh, Bharat & Kumar, Ashwani, 2023. "Optimal energy management and feasibility analysis of hybrid renewable energy sources with BESS and impact of electric vehicle load with demand response program," Energy, Elsevier, vol. 278(PA).
    12. Chen, Zhelun & O’Neill, Zheng & Wen, Jin & Pradhan, Ojas & Yang, Tao & Lu, Xing & Lin, Guanjing & Miyata, Shohei & Lee, Seungjae & Shen, Chou & Chiosa, Roberto & Piscitelli, Marco Savino & Capozzoli, , 2023. "A review of data-driven fault detection and diagnostics for building HVAC systems," Applied Energy, Elsevier, vol. 339(C).
    13. Kenanoğlu, Raif & Mert, İlker & Baydar, Ceyhun & Köse, Özkan & Yağlı, Hüseyin, 2024. "A comparative study on ensemble soft-computing methods for geothermal power production potential forecasting," Energy, Elsevier, vol. 303(C).
    14. Qinghan Sun & Huan Ma & Tian Zhao & Yonglin Xin & Qun Chen, 2024. "Break down the decentralization-security-privacy trilemma in management of distributed energy systems," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    15. Fu, Yangyang & O'Neill, Zheng & Yang, Zhiyao & Adetola, Veronica & Wen, Jin & Ren, Lingyu & Wagner, Tim & Zhu, Qi & Wu, Terresa, 2021. "Modeling and evaluation of cyber-attacks on grid-interactive efficient buildings," Applied Energy, Elsevier, vol. 303(C).
    16. Wei, Zhichen & Tien, Paige Wenbin & Calautit, John & Darkwa, Jo & Worall, Mark & Boukhanouf, Rabah, 2024. "Investigation of a model predictive control (MPC) strategy for seasonal thermochemical energy storage systems in district heating networks," Applied Energy, Elsevier, vol. 376(PA).
    17. Mansouri, Seyed Amir & Rezaee Jordehi, Ahmad & Marzband, Mousa & Tostado-Véliz, Marcos & Jurado, Francisco & Aguado, José A., 2023. "An IoT-enabled hierarchical decentralized framework for multi-energy microgrids market management in the presence of smart prosumers using a deep learning-based forecaster," Applied Energy, Elsevier, vol. 333(C).
    18. Liu, Zhiqiang & Cui, Yanping & Wang, Jiaqiang & Yue, Chang & Agbodjan, Yawovi Souley & Yang, Yu, 2022. "Multi-objective optimization of multi-energy complementary integrated energy systems considering load prediction and renewable energy production uncertainties," Energy, Elsevier, vol. 254(PC).
    19. Yang, Ting & Zhao, Liyuan & Li, Wei & Zomaya, Albert Y., 2021. "Dynamic energy dispatch strategy for integrated energy system based on improved deep reinforcement learning," Energy, Elsevier, vol. 235(C).
    20. Cai, Mengmeng & Pipattanasomporn, Manisa & Rahman, Saifur, 2019. "Day-ahead building-level load forecasts using deep learning vs. traditional time-series techniques," Applied Energy, Elsevier, vol. 236(C), pages 1078-1088.
    21. Nam, KiJeon & Hwangbo, Soonho & Yoo, ChangKyoo, 2020. "A deep learning-based forecasting model for renewable energy scenarios to guide sustainable energy policy: A case study of Korea," Renewable and Sustainable Energy Reviews, Elsevier, vol. 122(C).
    22. Dey, Maitreyee & Rana, Soumya Prakash & Simmons, Clarke V. & Dudley, Sandra, 2021. "Solar farm voltage anomaly detection using high-resolution μPMU data-driven unsupervised machine learning," Applied Energy, Elsevier, vol. 303(C).
    Full references (including those not matched with items on IDEAS)

    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. Mahdi Khodayar & Jacob Regan, 2023. "Deep Neural Networks in Power Systems: A Review," Energies, MDPI, vol. 16(12), pages 1-38, June.
    3. He, Youmeng & Gu, Chunhua & Gao, Yan & Wang, Jingqi, 2025. "Bi-level day-ahead and real-time hybrid pricing model and its reinforcement learning method," Energy, Elsevier, vol. 322(C).
    4. Ifaei, Pouya & Nazari-Heris, Morteza & Tayerani Charmchi, Amir Saman & Asadi, Somayeh & Yoo, ChangKyoo, 2023. "Sustainable energies and machine learning: An organized review of recent applications and challenges," Energy, Elsevier, vol. 266(C).
    5. Di Natale, L. & Svetozarevic, B. & Heer, P. & Jones, C.N., 2022. "Physically Consistent Neural Networks for building thermal modeling: Theory and analysis," Applied Energy, Elsevier, vol. 325(C).
    6. Vidal, João V. & Fonte, Tiago M.S.L. & Lopes, Luis Seabra & Bernardo, Rodrigo M.C. & Carneiro, Pedro M.R. & Pires, Diogo G. & Soares dos Santos, Marco P., 2024. "Prediction of dynamic behaviors of vibrational-powered electromagnetic generators: Synergies between analytical and artificial intelligence modelling," Applied Energy, Elsevier, vol. 376(PB).
    7. Te Li & Mengze Zhang & Yan Zhou, 2024. "LTPNet Integration of Deep Learning and Environmental Decision Support Systems for Renewable Energy Demand Forecasting," Papers 2410.15286, arXiv.org.
    8. Li, Pengchao & Guo, Fang & Li, Yongfei & Yang, Xuejing & Yang, Xudong, 2025. "Physics-informed neural network for real-time thermal modeling of large-scale borehole thermal energy storage systems," Energy, Elsevier, vol. 315(C).
    9. Chen, Dongyu & Sun, Qun Zhou & Qiao, Yiyuan, 2025. "Defending against cyber-attacks in building HVAC systems through energy performance evaluation using a physics-informed dynamic Bayesian network (PIDBN)," Energy, Elsevier, vol. 322(C).
    10. Xiao, Tianqi & You, Fengqi, 2024. "Physically consistent deep learning-based day-ahead energy dispatching and thermal comfort control for grid-interactive communities," Applied Energy, Elsevier, vol. 353(PB).
    11. Guo, Yanhua & Wang, Ningbo & Shao, Shuangquan & Huang, Congqi & Zhang, Zhentao & Li, Xiaoqiong & Wang, Youdong, 2024. "A review on hybrid physics and data-driven modeling methods applied in air source heat pump systems for energy efficiency improvement," Renewable and Sustainable Energy Reviews, Elsevier, vol. 204(C).
    12. Pinthurat, Watcharakorn & Surinkaew, Tossaporn & Hredzak, Branislav, 2024. "An overview of reinforcement learning-based approaches for smart home energy management systems with energy storages," Renewable and Sustainable Energy Reviews, Elsevier, vol. 202(C).
    13. Li, Yang & Bu, Fanjin & Li, Yuanzheng & Long, Chao, 2023. "Optimal scheduling of island integrated energy systems considering multi-uncertainties and hydrothermal simultaneous transmission: A deep reinforcement learning approach," Applied Energy, Elsevier, vol. 333(C).
    14. Fan, Wei & Tan, Zhongfu & Li, Fanqi & Zhang, Amin & Ju, Liwei & Wang, Yuwei & De, Gejirifu, 2023. "A two-stage optimal scheduling model of integrated energy system based on CVaR theory implementing integrated demand response," Energy, Elsevier, vol. 263(PC).
    15. Chen, Zhelun & O’Neill, Zheng & Wen, Jin & Pradhan, Ojas & Yang, Tao & Lu, Xing & Lin, Guanjing & Miyata, Shohei & Lee, Seungjae & Shen, Chou & Chiosa, Roberto & Piscitelli, Marco Savino & Capozzoli, , 2023. "A review of data-driven fault detection and diagnostics for building HVAC systems," Applied Energy, Elsevier, vol. 339(C).
    16. Guo, Tianyu & Guo, Qi & Huang, Libin & Guo, Haiping & Lu, Yuanhong & Tu, Liang, 2023. "Microgrid source-network-load-storage master-slave game optimization method considering the energy storage overcharge/overdischarge risk," Energy, Elsevier, vol. 282(C).
    17. Zhihan Shi & Weisong Han & Guangming Zhang & Zhiqing Bai & Mingxiang Zhu & Xiaodong Lv, 2022. "Research on Low-Carbon Energy Sharing through the Alliance of Integrated Energy Systems with Multiple Uncertainties," Energies, MDPI, vol. 15(24), pages 1-20, December.
    18. Ding, Zhixiong & Wu, Wei, 2024. "Simulation of a multi-level absorption thermal battery with variable solution flow rate for adjustable cooling capacity," Energy, Elsevier, vol. 301(C).
    19. Lim, Juin Yau & Safder, Usman & How, Bing Shen & Ifaei, Pouya & Yoo, Chang Kyoo, 2021. "Nationwide sustainable renewable energy and Power-to-X deployment planning in South Korea assisted with forecasting model," Applied Energy, Elsevier, vol. 283(C).
    20. Alexandra L’Heureux & Katarina Grolinger & Miriam A. M. Capretz, 2022. "Transformer-Based Model for Electrical Load Forecasting," Energies, MDPI, vol. 15(14), pages 1-23, July.

    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:energy:v:318:y:2025:i:c:s0360544225003536. 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.journals.elsevier.com/energy .

    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.