IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i4p845-d1588766.html
   My bibliography  Save this article

Advancing AI-Enabled Techniques in Energy System Modeling: A Review of Data-Driven, Mechanism-Driven, and Hybrid Modeling Approaches

Author

Listed:
  • Yuancheng Lin

    (POWERCHINA Huadong Engineering Corporation Limited, Hangzhou 311100, China
    Tsinghua-BP Clean Energy Research and Education Centre, Department of Energy and Power Engineering, Tsinghua University, Beijing 100084, China)

  • Junlong Tang

    (POWERCHINA Huadong Engineering Corporation Limited, Hangzhou 311100, China)

  • Jing Guo

    (POWERCHINA Huadong Engineering Corporation Limited, Hangzhou 311100, China)

  • Shidong Wu

    (POWERCHINA Huadong Engineering Corporation Limited, Hangzhou 311100, China)

  • Zheng Li

    (Tsinghua-BP Clean Energy Research and Education Centre, Department of Energy and Power Engineering, Tsinghua University, Beijing 100084, China)

Abstract

Artificial intelligence (AI) is increasingly essential for optimizing energy systems, addressing the growing complexity of energy management, and supporting the integration of diverse renewable sources. This study systematically reviews AI-enabled modeling approaches, highlighting their applications, limitations, and potential in advancing sustainable energy systems while offering insights and a framework for addressing real-world energy challenges. Data-driven models excel in energy demand prediction and resource optimization but face criticism for their “black-box” nature, while mechanism-driven models provide deeper system insights but require significant computation and domain expertise. To bridge the gap between these approaches, hybrid models combine the strengths of both, improving prediction accuracy, adaptability, and overall system optimization. This study discusses the policy background, modeling approaches, and key challenges in AI-enabled energy system modeling. Furthermore, this study highlights how AI-enabled techniques are paving the way for future energy system modeling, including integration and optimization for renewable energy systems, real-time optimization and predictive maintenance through digital twins, advanced demand-side management for optimal energy use, and hybrid simulation of energy markets and business behavior.

Suggested Citation

  • Yuancheng Lin & Junlong Tang & Jing Guo & Shidong Wu & Zheng Li, 2025. "Advancing AI-Enabled Techniques in Energy System Modeling: A Review of Data-Driven, Mechanism-Driven, and Hybrid Modeling Approaches," Energies, MDPI, vol. 18(4), pages 1-29, February.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:4:p:845-:d:1588766
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/4/845/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/18/4/845/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Maki, Seiya & Fujii, Minoru & Fujita, Tsuyoshi & Shiraishi, Yasushi & Ashina, Shuichi & Gomi, Kei & Sun, Lu & Budi Nugroho, Sudarmanto & Nakano, Ryoko & Osawa, Takahiro & Immanuel, Gito & Boer, Rizald, 2022. "A deep reinforced learning spatiotemporal energy demand estimation system using deep learning and electricity demand monitoring data," Applied Energy, Elsevier, vol. 324(C).
    2. Li, Zheng & Du, Binglin & Petersen, Nils & Liu, Pei & Wirsum, Manfred, 2024. "Potential of hydrogen and thermal storage in the long-term transition of the power sector: A case study of China," Energy, Elsevier, vol. 307(C).
    3. Zhang, Shuo & Yu, Yadong & Kharrazi, Ali & Ma, Tieju, 2023. "How would sustainable transformations in the electricity sector of megacities impact employment levels? A case study of Beijing," Energy, Elsevier, vol. 270(C).
    4. Han, Yongming & Wu, Hao & Geng, Zhiqiang & Zhu, Qunxiong & Gu, Xiangbai & Yu, Bin, 2020. "Review: Energy efficiency evaluation of complex petrochemical industries," Energy, Elsevier, vol. 203(C).
    5. Cai, Liya & Luo, Ji & Wang, Minghui & Guo, Jianfeng & Duan, Jinglin & Li, Jingtao & Li, Shuo & Liu, Liting & Ren, Dangpei, 2023. "Pathways for municipalities to achieve carbon emission peak and carbon neutrality: A study based on the LEAP model," Energy, Elsevier, vol. 262(PB).
    6. Huang, Tian-en & Guo, Qinglai & Sun, Hongbin & Tan, Chin-Woo & Hu, Tianyu, 2019. "A deep spatial-temporal data-driven approach considering microclimates for power system security assessment," Applied Energy, Elsevier, vol. 237(C), pages 36-48.
    7. Song, Siming & Li, Tianxiao & Liu, Pei & Li, Zheng, 2022. "The transition pathway of energy supply systems towards carbon neutrality based on a multi-regional energy infrastructure planning approach: A case study of China," Energy, Elsevier, vol. 238(PC).
    8. Dranka, Géremi Gilson & Ferreira, Paula & Vaz, A. Ismael F., 2021. "Integrating supply and demand-side management in renewable-based energy systems," Energy, Elsevier, vol. 232(C).
    9. Gulay, Emrah & Duru, Okan, 2020. "Hybrid modeling in the predictive analytics of energy systems and prices," Applied Energy, Elsevier, vol. 268(C).
    10. Yunlong Zhao & Linwei Ma & Zheng Li & Weidou Ni, 2022. "A Calculation and Decomposition Method Embedding Sectoral Energy Structure for Embodied Carbon: A Case Study of China’s 28 Sectors," Sustainability, MDPI, vol. 14(5), pages 1-29, February.
    11. Yu, Jie & Chen, Lu & Wang, Qiong & Zhang, Xi & Sun, Qinghe, 2024. "Towards sustainable regional energy solutions: An optimized operational model for integrated energy systems with price-responsive planning," Energy, Elsevier, vol. 305(C).
    12. Gao, Yang & Ai, Qian & He, Xing & Fan, Songli, 2023. "Coordination for regional integrated energy system through target cascade optimization," Energy, Elsevier, vol. 276(C).
    13. Li, Jinze & Liu, Pei & Li, Zheng, 2020. "Optimal design and techno-economic analysis of a solar-wind-biomass off-grid hybrid power system for remote rural electrification: A case study of west China," Energy, Elsevier, vol. 208(C).
    14. Farrokhifar, Meisam & Nie, Yinghui & Pozo, David, 2020. "Energy systems planning: A survey on models for integrated power and natural gas networks coordination," Applied Energy, Elsevier, vol. 262(C).
    15. Yu, Jianxi & Han, Wenquan & Chen, Kang & Liu, Pei & Li, Zheng, 2022. "Gross error detection in steam turbine measurements based on data reconciliation of inequality constraints," Energy, Elsevier, vol. 253(C).
    16. Cullen, Jonathan M. & Allwood, Julian M., 2010. "The efficient use of energy: Tracing the global flow of energy from fuel to service," Energy Policy, Elsevier, vol. 38(1), pages 75-81, January.
    17. Chong, Chin Hao & Tan, Wei Xin & Ting, Zhao Jia & Liu, Pei & Ma, Linwei & Li, Zheng & Ni, Weidou, 2019. "The driving factors of energy-related CO2 emission growth in Malaysia: The LMDI decomposition method based on energy allocation analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 115(C).
    18. Qin, Yuxiao & Liu, Pei & Li, Zheng, 2024. "Enhancing accuracy of flexibility characterization in integrated energy system design: A variable temporal resolution optimization method," Energy, Elsevier, vol. 288(C).
    19. You, Minglei & Wang, Qian & Sun, Hongjian & Castro, Iván & Jiang, Jing, 2022. "Digital twins based day-ahead integrated energy system scheduling under load and renewable energy uncertainties," Applied Energy, Elsevier, vol. 305(C).
    20. Nikseresht, Ali & Amindavar, Hamidreza, 2024. "Energy demand forecasting using adaptive ARFIMA based on a novel dynamic structural break detection framework," Applied Energy, Elsevier, vol. 353(PA).
    21. Mu, Yunfei & Xu, Yurui & Cao, Yan & Chen, Wanqing & Jia, Hongjie & Yu, Xiaodan & Jin, Xiaolong, 2022. "A two-stage scheduling method for integrated community energy system based on a hybrid mechanism and data-driven model," Applied Energy, Elsevier, vol. 323(C).
    22. Ma, Linwei & Allwood, Julian M. & Cullen, Jonathan M. & Li, Zheng, 2012. "The use of energy in China: Tracing the flow of energy from primary source to demand drivers," Energy, Elsevier, vol. 40(1), pages 174-188.
    23. Grzegorz Dudek & Paweł Piotrowski & Dariusz Baczyński, 2023. "Intelligent Forecasting and Optimization in Electrical Power Systems: Advances in Models and Applications," Energies, MDPI, vol. 16(7), pages 1-11, March.
    24. Adam Stecyk & Ireneusz Miciuła, 2023. "Harnessing the Power of Artificial Intelligence for Collaborative Energy Optimization Platforms," Energies, MDPI, vol. 16(13), pages 1-20, July.
    25. 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.
    26. Whittle, Colin & Jones, Christopher R. & While, Aidan, 2020. "Empowering householders: Identifying predictors of intentions to use a home energy management system in the United Kingdom," Energy Policy, Elsevier, vol. 139(C).
    27. Maleki, Neda & Lundström, Oxana & Musaddiq, Arslan & Jeansson, John & Olsson, Tobias & Ahlgren, Fredrik, 2024. "Future energy insights: Time-series and deep learning models for city load forecasting," Applied Energy, Elsevier, vol. 374(C).
    28. Peng, Jieyang & Kimmig, Andreas & Niu, Zhibin & Wang, Jiahai & Liu, Xiufeng & Ovtcharova, Jivka, 2021. "A flexible potential-flow model based high resolution spatiotemporal energy demand forecasting framework," Applied Energy, Elsevier, vol. 299(C).
    29. Chinhao Chong & Weidou Ni & Linwei Ma & Pei Liu & Zheng Li, 2015. "The Use of Energy in Malaysia: Tracing Energy Flows from Primary Source to End Use," Energies, MDPI, vol. 8(4), pages 1-39, April.
    30. Li, Tianxiao & Liu, Pei & Li, Zheng, 2021. "Optimal scale of natural gas reserves in China under increasing and fluctuating demand: A quantitative analysis," Energy Policy, Elsevier, vol. 152(C).
    31. Lin, Yuancheng & Ma, Linwei & Li, Zheng & Ni, Weidou, 2023. "The carbon reduction potential by improving technical efficiency from energy sources to final services in China: An extended Kaya identity analysis," Energy, Elsevier, vol. 263(PE).
    32. Zhu, Yuxiao & Newbrook, Daniel W. & Dai, Peng & de Groot, C.H. Kees & Huang, Ruomeng, 2022. "Artificial neural network enabled accurate geometrical design and optimisation of thermoelectric generator," Applied Energy, Elsevier, vol. 305(C).
    33. Polamarasetty P Kumar & Ramakrishna S. S. Nuvvula & Md. Alamgir Hossain & SK. A. Shezan & Vishnu Suresh & Michal Jasinski & Radomir Gono & Zbigniew Leonowicz, 2022. "Optimal Operation of an Integrated Hybrid Renewable Energy System with Demand-Side Management in a Rural Context," Energies, MDPI, vol. 15(14), pages 1-50, July.
    34. Tian, Hang & Zhao, Haoran & Liu, Chunyang & Chen, Jian & Wu, Qiuwei & Terzija, Vladimir, 2022. "A dual-driven linear modeling approach for multiple energy flow calculation in electricity–heat system," Applied Energy, Elsevier, vol. 314(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. Chong, Chin Hao & Zhou, Xiaoyong & Zhang, Yongchuang & Ma, Linwei & Bhutta, Muhammad Shoaib & Li, Zheng & Ni, Weidou, 2023. "LMDI decomposition of coal consumption in China based on the energy allocation diagram of coal flows: An update for 2005–2020 with improved sectoral resolutions," Energy, Elsevier, vol. 285(C).
    2. Chinhao Chong & Xi Zhang & Geng Kong & Linwei Ma & Zheng Li & Weidou Ni & Eugene-Hao-Chen Yu, 2021. "A Visualization Method of the Economic Input–Output Table: Mapping Monetary Flows in the Form of Sankey Diagrams," Sustainability, MDPI, vol. 13(21), pages 1-56, November.
    3. Yuancheng Lin & Chinhao Chong & Linwei Ma & Zheng Li & Weidou Ni, 2021. "Analysis of Changes in the Aggregate Exergy Efficiency of China’s Energy System from 2005 to 2015," Energies, MDPI, vol. 14(8), pages 1-27, April.
    4. Xu Li & Chinhao Chong & Linwei Ma & Pei Liu & Xuesi Shen & Zibo Jia & Cheng Wang & Zheng Li & Weidou Ni, 2018. "Coordinating the Dynamic Development of Energy and Industry in Composite Regions: An I-SDOP Analysis of the BTH Region," Sustainability, MDPI, vol. 10(6), pages 1-28, June.
    5. Honghua Yang & Linwei Ma & Zheng Li, 2020. "A Method for Analyzing Energy-Related Carbon Emissions and the Structural Changes: A Case Study of China from 2005 to 2015," Energies, MDPI, vol. 13(8), pages 1-24, April.
    6. Gao, Yuan & Chong, Chin Hao & Liu, Gengyuan & Casazza, Marco & Xiong, Xiaoping & Liu, Bojie & Zhou, Xuanru & Zhou, Xiaoyong & Li, Zheng & Ni, Weidou & Hao, Yan & Ma, Linwei, 2024. "Identification of carbon responsibility factors based on energy consumption from 2005 to 2020 in China," Energy, Elsevier, vol. 296(C).
    7. Linwei Ma & Chinhao Chong & Xi Zhang & Pei Liu & Weiqi Li & Zheng Li & Weidou Ni, 2018. "LMDI Decomposition of Energy-Related CO 2 Emissions Based on Energy and CO 2 Allocation Sankey Diagrams: The Method and an Application to China," Sustainability, MDPI, vol. 10(2), pages 1-37, January.
    8. Lin, Yuancheng & Ma, Linwei & Li, Zheng & Ni, Weidou, 2023. "The carbon reduction potential by improving technical efficiency from energy sources to final services in China: An extended Kaya identity analysis," Energy, Elsevier, vol. 263(PE).
    9. Chong, Chin Hao & Tan, Wei Xin & Ting, Zhao Jia & Liu, Pei & Ma, Linwei & Li, Zheng & Ni, Weidou, 2019. "The driving factors of energy-related CO2 emission growth in Malaysia: The LMDI decomposition method based on energy allocation analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 115(C).
    10. Yunlong Zhao & Geng Kong & Chin Hao Chong & Linwei Ma & Zheng Li & Weidou Ni, 2021. "How to Effectively Control Energy Consumption Growth in China’s 29 Provinces: A Paradigm of Multi-Regional Analysis Based on EAALMDI Method," Sustainability, MDPI, vol. 13(3), pages 1-26, January.
    11. Yang, Honghua & Ma, Linwei & Li, Zheng, 2023. "Tracing China's steel use from steel flows in the production system to steel footprints in the consumption system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 172(C).
    12. Xu, Jing & Wang, Xiaoying & Gu, Yujiong & Ma, Suxia, 2023. "A data-based day-ahead scheduling optimization approach for regional integrated energy systems with varying operating conditions," Energy, Elsevier, vol. 283(C).
    13. Bu, Yuntao & Yu, Hao & Ji, Haoran & Song, Guanyu & Xu, Jing & Li, Juan & Zhao, Jinli & Li, Peng, 2024. "Hybrid data-driven operation method for demand response of community integrated energy systems utilizing virtual and physical energy storage," Applied Energy, Elsevier, vol. 366(C).
    14. Zhu, Yilin & Xu, Yujie & Chen, Haisheng & Guo, Huan & Zhang, Hualiang & Zhou, Xuezhi & Shen, Haotian, 2023. "Optimal dispatch of a novel integrated energy system combined with multi-output organic Rankine cycle and hybrid energy storage," Applied Energy, Elsevier, vol. 343(C).
    15. Badr Eddine Lebrouhi & Eric Schall & Bilal Lamrani & Yassine Chaibi & Tarik Kousksou, 2022. "Energy Transition in France," Sustainability, MDPI, vol. 14(10), pages 1-28, May.
    16. Biying Yu & Guangpu Zhao & Runying An, 2019. "Framing the picture of energy consumption in China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 99(3), pages 1469-1490, December.
    17. Zhang, Pengpeng & Zhang, Lixiao & Tian, Xin & Hao, Yan & Wang, Changbo, 2018. "Urban energy transition in China: Insights from trends, socioeconomic drivers, and environmental impacts of Beijing," Energy Policy, Elsevier, vol. 117(C), pages 173-183.
    18. Qin, Ying & Curmi, Elizabeth & Kopec, Grant M. & Allwood, Julian M. & Richards, Keith S., 2015. "China's energy-water nexus – assessment of the energy sector's compliance with the “3 Red Lines” industrial water policy," Energy Policy, Elsevier, vol. 82(C), pages 131-143.
    19. Konadu, D. Dennis & Mourão, Zenaida Sobral & Allwood, Julian M. & Richards, Keith S. & Kopec, Grant & McMahon, Richard & Fenner, Richard, 2015. "Land use implications of future energy system trajectories—The case of the UK 2050 Carbon Plan," Energy Policy, Elsevier, vol. 86(C), pages 328-337.
    20. Lin, Yuancheng & Chong, Chin Hao & Ma, Linwei & Li, Zheng & Ni, Weidou, 2022. "Quantification of waste heat potential in China: A top-down Societal Waste Heat Accounting Model," Energy, Elsevier, vol. 261(PB).

    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:gam:jeners:v:18:y:2025:i:4:p:845-:d:1588766. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.