AI Agent Expert Interview Series - Prof. Yuan Gao
January 29, 2026, the Editorial Office of AI Agent had the pleasure of interviewing Prof. Gao from Kyushu University, whose research focuses on deep learning, time series forecasting, and intelligent control in energy systems.
In this conversation, Prof. Gao shared his perspectives on the evolving role of artificial intelligence in renewable energy applications, particularly in scenarios such as wind and photovoltaic power forecasting. He discussed the key challenges of data scarcity, noise, and distribution shift, as well as the limitations of current models in terms of interpretability and real-world deployment. He also highlighted the promising synergy between reinforcement learning and model predictive control (MPC) for energy system scheduling and control. His insights underscore how AI is transitioning from a purely data-driven tool to a more integrated framework that combines physical knowledge, control theory, and machine learning, paving the way for more reliable and intelligent energy systems.
Watch the full interview with Prof. Yuan Gao:
Interview Questions:
Q1 What initially led you to work at the intersection of deep learning and energy system optimization? Which experiences most shaped your research direction?
Q2 What are the unique challenges of time series forecasting in renewable energy applications such as wind and solar power, and which solutions do you find most promising?
Q3 How do you view the synergy between reinforcement learning and model predictive control (MPC) in energy system scheduling and control? How can they complement each other in practice?
Q4 What are the key engineering challenges when deploying deep learning or reinforcement learning models in real energy systems, and how do you balance performance, interpretability, and safety?
Q5 Given noisy, incomplete, and non-stationary data in energy systems, how do you enhance the robustness and generalization of your models?
Q6 How do you envision the role of AI in energy system optimization and intelligent control over the next 5–10 years? Which directions are most promising for young researchers?
About the Interviewee:
Yuan Gao is an Associate Professor at Kyushu University, Japan. He received his Ph.D. from the University of Tokyo, M.Eng. from Tongji University, and B.Eng. from Xi'an University of Architecture and Technology. His research focuses on interpretable deep learning for energy forecasting, model predictive control under uncertainty, and reinforcement learning for the operation and optimization of building energy systems. He has published more than 30 papers in these areas, including multiple ESI Highly Cited Papers.
Editor: Wen Xue
Language Editor: Catherine Yang
Production Editor: Ting Xu
Respectfully Submitted by the Editorial Office of AI Agent
