Dr. Luis Martin Pomares | DEWA R&D | United Arab Emirates
A distinguished solar energy expert and research scientist, with over two decades of professional experience spanning solar resource assessment, satellite-based irradiance modeling, and renewable energy forecasting. His career bridges applied research, data analytics, and large-scale solar project development, contributing to the advancement of sustainable energy systems worldwide. Currently serving as a Principal Scientist and Program Director at the Dubai Electricity and Water Authority (DEWA) R&D Center, he leads initiatives in solar resource assessment, satellite image processing, and deep learning-based solar forecasting. His work includes developing data pipelines for solar forecasting using modern AI frameworks such as TensorFlow and Keras, and managing BSRN radiometric stations for high-precision solar measurements. Previously, he was a Scientist at Qatar Foundation’s QEERI, where he directed the development of the Solar Atlas of Qatar and applied remote sensing methods for regional solar mapping. Earlier, as President and Project Developer at Investigaciones y Recursos Solares Avanzados (IrSOLaV), he managed multi-megawatt PV and CSP projects across several continents and pioneered forecasting systems integrating Numerical Weather Prediction (NWP) and satellite-based nowcasting models. His expertise encompasses radiative transfer modeling, atmospheric physics, GIS-based solar mapping, and energy system simulation. He has contributed extensively to IEA Solar Heating and Cooling Tasks 36 and 46, COST Action 1002, and EU FP7 projects on solar nowcasting. He is also an active reviewer for leading journals such as Solar Energy, Journal of Solar Energy Engineering, and Atmospheric Measurement Techniques. Academically, he holds a Ph.D. in Physical Sciences (Cum Laude) from the Complutense University of Madrid, with a dissertation on solar radiation prediction using statistical methods. His publication record includes numerous peer-reviewed papers and book chapters on solar resource modeling, machine learning applications in energy forecasting, and site adaptation of solar datasets.
Featured Publications
Valappil, V. K., & Martin-Pomares, L. (2025). Characterizing solar attenuation for concentrated solar power plants in Dubai using AERONET data and libRadtran. Solar Energy, 302, 114082.
Sanfilippo, A., Martin-Pomares, L., & Polo, J. (2025). Solar resources mapping: Fundamentals and applications. Springer Nature.
Nie, Y., Paletta, Q., Scott, A., Martin-Pomares, L. M., Arbod, G., Sgouridis, S., Lasenby, J., et al. (2024). Sky image-based solar forecasting using deep learning with heterogeneous multi-location data: Dataset fusion versus transfer learning. Applied Energy, 369, 123467.
Rezk, M., Tiwari, V. K., Manandhar, P., Krishnan, V., & Martin-Pomares, L. M. (2024). A CNN-based classifier for quality image selection from sky cameras. Proceedings of the 7th International Conference on Signal Processing and Information Technology (ICSPIT).
Manandhar, P., Tiwari, V. K., Rezk, M., Krishnan, V., & Martin-Pomares, L. M. (2024). Convolutional deep learning hierarchical classifier to identify synoptic sky conditions based on sky images. Proceedings of the 7th International Conference on Signal Processing and Information Technology (ICSPIT).
Martin-Pomares, L., Ghaoud, T., Al Khaja, T. T., Alemadi, A., Ahmed, M. S. M. M., et al. (2024). Sea water salinity estimation over Dubai using satellite imagery & WASI empirical model approach: Application to SpaceD DEWASAT-2 nanosatellite. Proceedings of the 7th International Conference on Signal Processing and Information Technology (ICSPIT).