Deep Generative Models in Energy System Applications: Review, Challenges, and Future Directions

Journal Article · Fri Dec 13 04:00:00 EST 2024 · Applied Energy

DOI:https://doi.org/10.1016/j.apenergy.2024.125059· OSTI ID:2588756

Zhang, Xiangyu ^[1]; Glaws, Andrew ^[1]; Cortiella, Alexandre ^[1]; Emami, Patrick ^[1]; King, Ryan ^[1]

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1. National Renewable Energy Lab., Golden, CO (United States)

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 systematic review of how DGMs have been utilized in energy system applications. After introducing four most popular DGMs, we review and categorize 196 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.

• Research Organization: National Renewable Energy Laboratory (NREL), Golden, CO (United States)
• Sponsoring Organization: USDOE National Renewable Energy Laboratory (NREL), Laboratory Directed Research and Development (LDRD) Program
• DOE Contract Number: AC36-08GO28308
• OSTI ID: 2588756
• Report Number(s): NREL/JA-2C00-90375
• Journal Information: Applied Energy, Journal Name: Applied Energy Vol. 380
• Country of Publication: United States
• Language: English

Related Subjects

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION
97 MATHEMATICS AND COMPUTING
deep generative models
energy systems
generative artificial intelligence
smart grid