IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v380y2025ics0306261924024437.html
   My bibliography  Save this article

Deep generative models in energy system applications: Review, challenges, and future directions

Author

Listed:
  • Zhang, Xiangyu
  • Glaws, Andrew
  • Cortiella, Alexandre
  • Emami, Patrick
  • King, Ryan N.

Abstract

In recent years, with the advent of mature machine learning products like ChatGPT, Stable Diffusion, and Sora, the world has witnessed tremendous changes driven by the rapid development of generative artificial intelligence (GAI). Beyond applications in text, speech, image, and video creation, deep generative models (DGMs) underpinning these cutting-edge technologies have also been employed by domain researchers to address scientific and engineering challenges. This paper aims to fill a gap in the research community by providing a comprehensive review of how DGMs have been utilized in energy system applications. Based on five of the most popular DGMs, we review and categorize 228 research articles into five focus areas: data generation, forecasting, situational awareness, modeling, and optimal decision-making. Through this classification, we uncover trends in how DGMs are employed for each type of problem, highlighting GAI techniques that contribute to breakthroughs over traditional methods. We discuss limitations in existing literature, engineering challenges, and propose future directions, all tailored to the unique nature of problems in energy system engineering. Our goal is to offer insights for energy system domain researchers, providing a comprehensive view of existing studies and potential future opportunities.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:380:y:2025:i:c:s0306261924024437
    DOI: 10.1016/j.apenergy.2024.125059
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261924024437
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2024.125059?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 search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. Qi, Yuchen & Hu, Wei & Dong, Yu & Fan, Yue & Dong, Ling & Xiao, Ming, 2020. "Optimal configuration of concentrating solar power in multienergy power systems with an improved variational autoencoder," Applied Energy, Elsevier, vol. 274(C).
    3. Lu, Xin & Qiu, Jing & Lei, Gang & Zhu, Jianguo, 2022. "Scenarios modelling for forecasting day-ahead electricity prices: Case studies in Australia," Applied Energy, Elsevier, vol. 308(C).
    4. Silva, Walquiria N. & Bandória, Luís H.T. & Dias, Bruno H. & de Almeida, Madson C. & de Oliveira, Leonardo W., 2023. "Generating realistic load profiles in smart grids: An approach based on nonlinear independent component estimation (NICE) and convolutional layers," Applied Energy, Elsevier, vol. 351(C).
    5. 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).
    6. Nowotarski, Jakub & Weron, Rafał, 2018. "Recent advances in electricity price forecasting: A review of probabilistic forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1548-1568.
    7. Ringkjøb, Hans-Kristian & Haugan, Peter M. & Solbrekke, Ida Marie, 2018. "A review of modelling tools for energy and electricity systems with large shares of variable renewables," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 440-459.
    8. Zhang, Jincheng & Zhao, Xiaowei, 2022. "Wind farm wake modeling based on deep convolutional conditional generative adversarial network," Energy, Elsevier, vol. 238(PB).
    9. Li, Yang & Zhang, Meng & Chen, Chen, 2022. "A Deep-Learning intelligent system incorporating data augmentation for Short-Term voltage stability assessment of power systems," Applied Energy, Elsevier, vol. 308(C).
    10. Tim Janke & Florian Steinke, 2020. "Probabilistic multivariate electricity price forecasting using implicit generative ensemble post-processing," Papers 2005.13417, arXiv.org.
    11. 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).
    12. Chang, Miguel & Thellufsen, Jakob Zink & Zakeri, Behnam & Pickering, Bryn & Pfenninger, Stefan & Lund, Henrik & Østergaard, Poul Alberg, 2021. "Trends in tools and approaches for modelling the energy transition," Applied Energy, Elsevier, vol. 290(C).
    13. Yazdanie, M. & Orehounig, K., 2021. "Advancing urban energy system planning and modeling approaches: Gaps and solutions in perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
    14. Yu, Chuanjin & Fu, Suxiang & Wei, ZiWei & Zhang, Xiaochi & Li, Yongle, 2024. "Multi-feature-fused generative neural network with Gaussian mixture for multi-step probabilistic wind speed prediction," Applied Energy, Elsevier, vol. 359(C).
    15. Lee, Yoonjae & Ha, Byeongmin & Hwangbo, Soonho, 2022. "Generative model-based hybrid forecasting model for renewable electricity supply using long short-term memory networks: A case study of South Korea's energy transition policy," Renewable Energy, Elsevier, vol. 200(C), pages 69-87.
    16. Claeys, Robbert & Cleenwerck, Rémy & Knockaert, Jos & Desmet, Jan, 2024. "Capturing multiscale temporal dynamics in synthetic residential load profiles through Generative Adversarial Networks (GANs)," Applied Energy, Elsevier, vol. 360(C).
    17. Wei, Hu & Hongxuan, Zhang & Yu, Dong & Yiting, Wang & Ling, Dong & Ming, Xiao, 2019. "Short-term optimal operation of hydro-wind-solar hybrid system with improved generative adversarial networks," Applied Energy, Elsevier, vol. 250(C), pages 389-403.
    18. Luo, Zheng & Lin, Xiaojie & Qiu, Tianyue & Li, Manjie & Zhong, Wei & Zhu, Lingkai & Liu, Shuangcui, 2024. "Investigation of hybrid adversarial-diffusion sample generation method of substations in district heating system," Energy, Elsevier, vol. 288(C).
    19. Grant Buster & Brandon N. Benton & Andrew Glaws & Ryan N. King, 2024. "High-resolution meteorology with climate change impacts from global climate model data using generative machine learning," Nature Energy, Nature, vol. 9(7), pages 894-906, July.
    20. Cramer, Eike & Witthaut, Dirk & Mitsos, Alexander & Dahmen, Manuel, 2023. "Multivariate probabilistic forecasting of intraday electricity prices using normalizing flows," Applied Energy, Elsevier, vol. 346(C).
    21. Li, Yang & Wang, Bin & Yang, Zhen & Li, Jiazheng & Chen, Chen, 2022. "Hierarchical stochastic scheduling of multi-community integrated energy systems in uncertain environments via Stackelberg game," Applied Energy, Elsevier, vol. 308(C).
    22. Suyang Zhou & Zijian Hu & Zhi Zhong & Di He & Meng Jiang, 2019. "An Integrated Energy System Operating Scenarios Generator Based on Generative Adversarial Network," Sustainability, MDPI, vol. 11(23), pages 1-15, November.
    23. Li, Jinghua & Zhou, Jiasheng & Chen, Bo, 2020. "Review of wind power scenario generation methods for optimal operation of renewable energy systems," Applied Energy, Elsevier, vol. 280(C).
    24. Zhang, Jian & Zhang, Chaobo & Lu, Jie & Zhao, Yang, 2025. "Domain-specific large language models for fault diagnosis of heating, ventilation, and air conditioning systems by labeled-data-supervised fine-tuning," Applied Energy, Elsevier, vol. 377(PA).
    25. Vijayshankar, Sanjana & Chang, Chin-Yao & Utkarsh, Kumar & Wald, Dylan & Ding, Fei & Balamurugan, Sivasathya Pradha & King, Jennifer & Macwan, Richard, 2023. "Assessing the impact of cybersecurity attacks on energy systems," Applied Energy, Elsevier, vol. 345(C).
    26. 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).
    27. Zhen Jiang & Di Shi & Xiaobin Guo & Guangyue Xu & Li Yu & Chaoyang Jing, 2018. "Robust Smart Meter Data Analytics Using Smoothed ALS and Dynamic Time Warping," Energies, MDPI, vol. 11(6), pages 1-13, May.
    28. 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.
    29. Alessandrini, S. & Delle Monache, L. & Sperati, S. & Cervone, G., 2015. "An analog ensemble for short-term probabilistic solar power forecast," Applied Energy, Elsevier, vol. 157(C), pages 95-110.
    30. Lund, Henrik & Østergaard, Poul Alberg & Connolly, David & Mathiesen, Brian Vad, 2017. "Smart energy and smart energy systems," Energy, Elsevier, vol. 137(C), pages 556-565.
    31. 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).
    32. Child, Michael & Breyer, Christian, 2017. "Transition and transformation: A review of the concept of change in the progress towards future sustainable energy systems," Energy Policy, Elsevier, vol. 107(C), pages 11-26.
    33. Dhaval Dalal & Muhammad Bilal & Hritik Shah & Anwarul Islam Sifat & Anamitra Pal & Philip Augustin, 2023. "Cross-Correlated Scenario Generation for Renewable-Rich Power Systems Using Implicit Generative Models," Energies, MDPI, vol. 16(4), pages 1-20, February.
    34. Zhao, Xiaohu & Huang, Guohe & Lu, Chen & Zhou, Xiong & Li, Yongping, 2020. "Impacts of climate change on photovoltaic energy potential: A case study of China," Applied Energy, Elsevier, vol. 280(C).
    35. Seleshi G. Yalew & Michelle T. H. van Vliet & David E. H. J. Gernaat & Fulco Ludwig & Ariel Miara & Chan Park & Edward Byers & Enrica De Cian & Franziska Piontek & Gokul Iyer & Ioanna Mouratiadou & Ja, 2020. "Impacts of climate change on energy systems in global and regional scenarios," Nature Energy, Nature, vol. 5(10), pages 794-802, October.
    36. Wenchao Li & Haitao Liu, 2024. "Applying large language models for automated essay scoring for non-native Japanese," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-15, December.
    37. Mei, H. & Li, Y.P. & Suo, C. & Ma, Y. & Lv, J., 2020. "Analyzing the impact of climate change on energy-economy-carbon nexus system in China," Applied Energy, Elsevier, vol. 262(C).
    38. 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).
    39. 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.
    40. Hu, Yi & Kim, Hyeonjin & Ye, Kai & Lu, Ning, 2025. "Applying fine-tuned LLMs for reducing data needs in load profile analysis," Applied Energy, Elsevier, vol. 377(PC).
    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. 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.

    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. 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.
    2. Liuqing Gu & Jian Xu & Deping Ke & Youhan Deng & Xiaojun Hua & Yi Yu, 2024. "Short-Term Output Scenario Generation of Renewable Energy Using Transformer–Wasserstein Generative Adversarial Nets-Gradient Penalty," Sustainability, MDPI, vol. 16(24), pages 1-20, December.
    3. Ye, Lin & Peng, Yishu & Li, Yilin & Li, Zhuo, 2024. "A novel informer-time-series generative adversarial networks for day-ahead scenario generation of wind power," Applied Energy, Elsevier, vol. 364(C).
    4. Jiang, He & Dong, Yawei & Dong, Yao & Wang, Jianzhou, 2025. "Probabilistic electricity price forecasting by integrating interpretable model," Technological Forecasting and Social Change, Elsevier, vol. 210(C).
    5. 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).
    6. 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.
    7. Østergaard, P.A. & Lund, H. & Thellufsen, J.Z. & Sorknæs, P. & Mathiesen, B.V., 2022. "Review and validation of EnergyPLAN," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    8. Chong, Cheng Tung & Fan, Yee Van & Lee, Chew Tin & Klemeš, Jiří Jaromír, 2022. "Post COVID-19 ENERGY sustainability and carbon emissions neutrality," Energy, Elsevier, vol. 241(C).
    9. Alabi, Tobi Michael & Aghimien, Emmanuel I. & Agbajor, Favour D. & Yang, Zaiyue & Lu, Lin & Adeoye, Adebusola R. & Gopaluni, Bhushan, 2022. "A review on the integrated optimization techniques and machine learning approaches for modeling, prediction, and decision making on integrated energy systems," Renewable Energy, Elsevier, vol. 194(C), pages 822-849.
    10. Lingxue Lin & Zuowei You & Fengjiao Li & Jun Liu & Chengwei Yang, 2025. "A Two-Stage Hidden Markov Model for Medium- to Long-Term Multiple Wind Farm Power Scenario Generation," Energies, MDPI, vol. 18(8), pages 1-15, April.
    11. Cheng, Qian & Liu, Pan & Xia, Qian & Cheng, Lei & Ming, Bo & Zhang, Wei & Xu, Weifeng & Zheng, Yalian & Han, Dongyang & Xia, Jun, 2023. "An analytical method to evaluate curtailment of hydro–photovoltaic hybrid energy systems and its implication under climate change," Energy, Elsevier, vol. 278(C).
    12. Makasis, Nikolas & Gu, Xiaoying & Kreitmair, Monika J. & Narsilio, Guillermo A. & Choudhary, Ruchi, 2023. "Geothermal pavements: A city-scale investigation on providing sustainable heating for the city of Cardiff, UK," Renewable Energy, Elsevier, vol. 218(C).
    13. Hilger, Hannes & Witthaut, Dirk & Dahmen, Manuel & Rydin Gorjão, Leonardo & Trebbien, Julius & Cramer, Eike, 2024. "Multivariate scenario generation of day-ahead electricity prices using normalizing flows," Applied Energy, Elsevier, vol. 367(C).
    14. Rodriguez-Matas, Antonio F. & Ruiz, Carlos & Linares, Pedro & Perez-Bravo, Manuel, 2025. "How energy strategies are shaped by the correlation of uncertainties," Applied Energy, Elsevier, vol. 382(C).
    15. Abel, Dennis & Lieth, Jonas & Jünger, Stefan, 2024. "Mapping the spatial turn in social science energy research. A computational literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 201(C).
    16. Pezalla, Simon & Obringer, Renee, 2023. "Evaluating the household-level climate-electricity nexus across three cities through statistical learning techniques," Socio-Economic Planning Sciences, Elsevier, vol. 89(C).
    17. Blanco, Herib & Leaver, Jonathan & Dodds, Paul E. & Dickinson, Robert & García-Gusano, Diego & Iribarren, Diego & Lind, Arne & Wang, Changlong & Danebergs, Janis & Baumann, Martin, 2022. "A taxonomy of models for investigating hydrogen energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    18. 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).
    19. Li, Zilu & Peng, Xiangang & Cui, Wenbo & Xu, Yilin & Liu, Jianan & Yuan, Haoliang & Lai, Chun Sing & Lai, Loi Lei, 2024. "A novel scenario generation method of renewable energy using improved VAEGAN with controllable interpretable features," Applied Energy, Elsevier, vol. 363(C).
    20. Yang, Ting & Xu, Zheming & Ji, Shijie & Liu, Guoliang & Li, Xinhong & Kong, Haibo, 2025. "Cooperative optimal dispatch of multi-microgrids for low carbon economy based on personalized federated reinforcement learning," Applied Energy, Elsevier, vol. 378(PA).

    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:380:y:2025:i:c:s0306261924024437. 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.