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

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  • 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
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