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

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  • 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.

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  • 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
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