Getahun Ayele Tessema | Energy Sustainability | Best Researcher Award

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Mr. Getahun Ayele Tessema | Energy Sustainability | Best Researcher Award

Mr. Getahun Ayele Tessema | Indian Institute of Technology Roorkee (IITR) | India

Getahun Ayele Tessema is an emerging researcher and PhD Candidate at the Indian Institute of Technology Roorkee (IITR), specializing in energy efficiency and sustainable built environments. He holds a B.Tech and Master’s degree with distinction and previously served as a Lecturer at Adama Science and Technology University, where he earned the Best Teacher Award for his academic excellence. Currently supported by the prestigious ICCR Africa PhD Scholarship from the Government of India, he continues to advance impactful research in the Built Environment Lab at IIT Roorkee. His academic and professional journey reflects consistent excellence, with active engagement in research, teaching, and community-oriented scientific contributions. He has completed or is working on five research projects, published two journal papers, and maintains a growing citation record accessible through Google Scholar. His collaborative work spans international and institutional partnerships, and he holds memberships in two professional organizations aligned with his research areas. Getahun’s research focuses on energy-efficient buildings, energy modelling, energy-use behaviour, and energy analysis. His contributions offer significant insights into the determinants of household energy-saving behaviour in Ethiopian urban settings. By integrating personal norms with the Theory of Planned Behaviour, he has developed a comprehensive framework that helps understand and influence energy-conscious behaviour. This work supports the development of national energy conservation strategies and provides a scientific basis for formulating building energy codes—an urgent need for sustainable urban growth in Ethiopia. His findings aim to guide policymakers, enhance occupant awareness, and contribute to cleaner energy access through practical, community-responsive solutions. Through his multidisciplinary research, academic leadership, and commitment to sustainable development, Getahun exemplifies innovation, scholarly excellence, and societal impact. His work aligns strongly with the vision of promoting energy-efficient built environments and positions him as a strong candidate for the Best Researcher Award.

Profile: Google Scholar

Featured Publications

Tessema, G. A., Chani, P. S., & Rajasekar, E. (2025). Analysis of residential electricity consumption in Ethiopian condominiums: Leveraging cluster analysis for targeted electrification interventions. In 2025 IEEE 13th International Conference on Smart Energy Grid Engineering (SEGE). IEEE.

Tessema, G. A., Chani, P. S., & Rajasekar, E. (2025). Modelling energy-saving behaviour in Ethiopian urban households: Integrating personal norms and demographic moderators to the theory of planned behaviour. Energy and Buildings, —, 116709.

Wahad Rahman | Energy Sustainability | Best Researcher Award

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Mr. Wahad Rahman | Energy Sustainability | Best Researcher Award

Mr. Wahad Rahman | University of Engineering and Technology Peshawar | Pakistan

A highly skilled engineering professional with extensive experience in mechatronics, renewable energy systems, additive manufacturing, and Internet of Things (IoT) technologies. With a strong academic foundation including advanced research in hybrid energy harvesting systems, this expert has contributed significantly to cutting-edge developments in sustainable power solutions for sensor networks and pipeline monitoring applications. Current work in additive manufacturing and reverse engineering involves leading research and development initiatives, operating advanced 3D printing technologies (FDM, SLA), managing high-precision 3D scanning, and conducting specialized training programs. This blends practical engineering with innovation-driven problem-solving across industrial and applied research environments. Previously, research contributions in sensor and energy harvesting systems included the design and development of micro Kaplan and Crossflow turbines, IoT-based pipeline monitoring solutions, energy-efficient wireless sensor nodes, and experimental setups for testing hybrid energy harvesters. These projects demonstrate strong proficiency in mechanical design, simulation, prototyping, and system integration. With several years of experience in academia, this professional has taught undergraduate theory and laboratory courses in mechanics of materials, fluid mechanics, robotics, and engineering software tools such as MATLAB and SolidWorks. Extensive involvement in STEM capacity-building programs further highlights commitment to engineering education and technology dissemination. Research expertise spans renewable energy, hybrid and flow-based energy harvesting, control systems, self-powered systems, power management circuits, wireless sensors, and IoT technologies. Published work includes multiple journal articles in reputable international outlets focusing on piezoelectric, electromagnetic, and hybrid energy harvesters, turbine modeling, RF energy harvesting, and sensor network applications. Professional development includes specialized training in IoT, Arduino, Raspberry Pi, MATLAB, image processing, robotics, content writing, and digital marketing, demonstrating a broad multidisciplinary skillset. Overall, this profile reflects a dynamic researcher and engineer dedicated to advancing sustainable energy solutions, intelligent monitoring systems, and modern manufacturing technologies.

Profiles: Orcid | LinkedIn | Google Scholar

Featured Publications

Rahman, W. U., & Khan, F. U. (2025). A hybrid flow energy harvester to power an IoT-based wireless sensor system for the digitization and monitoring of pipeline networks. Machines, 13(11). https://doi.org/10.3390/machines13111025

Rahman, W. U., & Khan, F. U. (2025). An integrated fluid flow and solar hybrid energy harvester for pipeline monitoring system. AIP Advances. https://doi.org/10.1063/5.0284001

Rahman, W. U., & Khan, F. U. (2025). A survey of flow-based energy harvesters for powering sustainable wireless sensor nodes. Journal of Renewable and Sustainable Energy. https://doi.org/10.1063/5.0237597

Rahman, W. U., & Khan, F. U. (2023). A hybrid flow energy harvester using combined piezoelectric and electromagnetic transductions for pipeline network monitoring. Journal of Intelligent Material Systems and Structures. https://doi.org/10.1177/1045389X221147647

Luis Martin Pomares | Renewable Resources | Best Researcher Award

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Dr. Luis Martin Pomares | Renewable Resources | Best Researcher Award

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.

Profile: Google Scholar

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).