Liu Bai is an Assistant Researcher and Postdoctoral Fellow at the Harbin Institute of Technology, specializing in predictability, energy forecasting, solar forecasting, and structural failure prediction, where he contributes to advancing energy meteorology and data-driven engineering analysis. He holds a Ph.D. in Structural Engineering from the Harbin Institute of Technology and a Bachelor’s degree in Civil Engineering from Northeast Agricultural University, with academic training that supports his interdisciplinary research spanning renewable energy systems and structural behavior modeling. His professional experience includes leading multiple scientific research projects, serving as a visiting scholar at prominent research institutes, and contributing to national-level postdoctoral research initiatives, reflecting strong leadership in collaborative and applied research environments. His research outputs include more than 30 peer-reviewed publications in high-impact journals covering solar irradiance predictability, deep-learning-based solar forecasting, structural performance assessment, and concrete behavior modeling, alongside contributions to innovation through several granted patents. He also serves as a youth editor for multiple scientific journals, reviews manuscripts for leading international publications in energy and computational fields, and is an active member of professional societies, including roles within the Chinese Particle Society and the Aerosol Professional Committee, demonstrating recognized expertise and service within the research community.
Profile: Scopus
Featured Publications
Liu Bai*, Predictability and forecast skill of solar irradiance across large-scale regions using advanced statistical and machine-learning frameworks. Renewable & Sustainable Energy Reviews, Accepted.
Liu Bai*, Deep-learning–enhanced solar irradiance prediction and uncertainty quantification based on multi-task and physically constrained modeling approaches. Solar Energy, Accepted.
Liu Bai, Data-driven structural performance assessment and failure prediction of reinforced concrete and masonry systems under complex loading conditions. Structural Concrete, Accepted.