Prof. LI Mengying 李夢穎

Assistant Professor

Area of Specialization: Energy meteorology, Physics and machine learning based solar forecasting, Solar-based poly-generation system design and control, Radiative heat transfer, High temperature electrolysis, Radiative cooling, Solar desalination

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Biography

BEng (Tsinghua University); MSc (University of Pennsylvania) and PhD (University of California San Diego)

Short Description

Prof. Li received her BEng degree from Tsinghua University, MSc degree from the Department of Mechanical Engineering and Applied Mechanics of University of Pennsylvania, and PhD degree from the Department of Mechanical and Aerospace Engineering of University of California San Diego. Prior to joining PolyU in 2020, she was a Postdoctoral Scholar from 2018 to 2020 in the Center for Energy Research of UC San Diego.

Prof. Li’s research focuses on the science and technologies for renewable energy utilizations, aiming to mitigate the Abrupt Climate Change while sustain the energy, food and water supplies. She is particularly interested in renewable energy integration by developing physics-based, remote sensing and machine learning integrated adaptive technologies for solar energy resourcing and forecasting. She is also interested in integrated renewable power systems, solar driven passive cooling and desalination, and large-scale energy storage.

Selected Publications

Wang, D., Liang, Z., Zhang, Z., Li, M. (2025) “Efficient Estimation of Convective Cooling of Photovoltaic Arrays with Various Geometric Configurations: a Physics-Informed Machine Learning Approach”. Energy and AI, 100499. https://doi.org/10.1016/j.egyai.2025.100499
Jing, T., Chen, S., Navarro-Alarcon, D., Chu, Y., Li, M. (2025) “SolarFusionNet: Enhanced Solar Irradiance Forecasting via Automated Multi-Modal Feature Selection and Cross-Modal Fusion”. IEEE Transactions on Sustainable Energy, vol. 16, no. 2, pp. 761-773, April 2025. 10.1109/TSTE.2024.3482360
Deng, N., Dong, P., Wang, Z., Li, M. (2025) “Quantifying the effects of spectral and directional distribution of radiation on its propagation in saline water”. Applied Thermal Engineering, 258, 124536. https://doi.org/10.1016/j.applthermaleng.2024.124536
Liang, Z., Chen, S., Ni, M., Wang, J., Li, M. (2024) “A novel control strategy to neutralize heat source within solid oxide electrolysis cell (SOEC) under variable solar power conditions”. Applied Energy, 371,123669. https://doi.org/10.1016/j.apenergy.2024.123669
Chen, S., Li, C., Stull, R., Li, M. (2024). Improved satellite-based intra-day solar forecasting with a chain of deep learning models”. Energy Conversion and Management, 313,118598. https://doi.org/10.1016/j.enconman.2024.118598
Liang, Z., Wang, J., Ren, K., Jiao, Z., Ni, M., An, L., Wang, Y., & Li, M. (2024). Discovering two general characteristic times of transient responses in solid oxide cells. Nature Communications, 15 (1), 4587. https://doi.org/10.1038/s41467-024-48785-1
Chu, Y., Wang, Y., Yang, D., Chen, S., & Li, M. (2024). A review of distributed solar forecasting with remote sensing and deep learning. Renewable and Sustainable Energy Reviews, 198, 114391. https://doi.org/10.1016/j.rser.2024.114391
Chu, Y., Yang, D.✉, Yu, H., Zhao, X., & Li, M.✉ (2024). Can end-to-end data-driven models outperform traditional semi-physical models in separating 1-min irradiance? Applied Energy, 356, 122434. https://doi.org/10.1016/j.apenergy.2023.122434
Chen, S., Li, C., Xie, Y., & Li, M. (2023). Global and direct solar irradiance estimation using deep learning and selected spectral satellite images. Applied Energy, 352, 121979. https://doi.org/10.1016/j.apenergy.2023.121979
Zhou, Q., Dong, P., Li, M., & Wang, Z. (2023). Analyzing the interactions between photovoltaic system and its ambient environment using CFD techniques: A review. Energy and Buildings, 113394. https://doi.org/10.1016/j.enbuild.2023.113394
Liang, Z., Wang, J., Wang, Y., Ni, M., & Li, M. (2023). Transient characteristics of a solid oxide electrolysis cell under different voltage ramps: Transport phenomena behind overshoots. Energy Conversion and Management, 279, 116759. https://doi.org/10.1016/j.enconman.2023.116759
Chen, S., & Li, M. (2022). Improved turbidity estimation from local meteorological data for solar resourcing and forecasting applications. Renewable Energy, 189, 259-272. doi.org/10.1016/j.renene.2022.02.107
Chu, Y., Li, M., Coimbra, C. F. M., Feng, D., & Wang, H. (2021). Intra-hour irradiance forecasting techniques for solar power integration: A review. iScience, 24(10), 103136. 10.1016/j.isci.2021.103136
Li, M., & Coimbra, C. F. M. (2019). On the effective spectral emissivity of clear skies and the radiative cooling potential of selectively designed materials. International Journal of Heat and Mass Transfer, 135, 1053-1062. https://doi.org/10.1016/j.ijheatmasstransfer.2019.02.040
Li, M., Jiang, Y., & Coimbra, C. F. M. (2017). On the determination of atmospheric longwave irradiance under all-sky conditions. Solar Energy, 144, 40-48. https://doi.org/10.1016/j.solener.2017.01.006
Li, M., Chu, Y., Pedro, H. T., & Coimbra, C. F. M. (2016). Quantitative evaluation of the impact of cloud transmittance and cloud velocity on the accuracy of short-term DNI forecasts. Renewable Energy, 86, 1362-1371. https://doi.org/10.1016/j.renene.2015.09.058

For the full list of publications, please visit Renewable Energy Advancement Lab (REALab).