
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
- FG634
- 2766-6659
- mengying.li@polyu.edu.hk
- Personal Website
Biography
BEng (Tsinghua University); MSc (University of Pennsylvania) and PhD (University of California San Diego)
Short Description
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