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

Yuancheng Lin, Junlong Tang, Jing Guo, Shidong Wu and Zheng Li ()
Additional contact information
Yuancheng Lin: POWERCHINA Huadong Engineering Corporation Limited, Hangzhou 311100, 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

Energies, 2025, vol. 18, issue 4, 1-29

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.

Keywords: AI in energy; energy system modeling; data-driven model; mechanism-driven model; hybrid modeling; renewable energy integration (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/18/4/845/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/4/845/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:18:y:2025:i:4:p:845-:d:1588766

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().