Pankaj Kumar Dubey | Sustainable Architecture | Young Scientist Award

Published on by Architecture Engineers

Mr. Pankaj Kumar Dubey | Sustainable Architecture | Young Scientist Award

Kamla Nehru Institute of Technology | India

Pankaj Kumar Dubey is a Teaching Assistant and Research Fellow in the Department of Electrical Engineering at Kamla Nehru Institute of Technology, specializing in distributed generation, electric vehicles, renewable energy systems, and intelligent power system optimization. He has served as lecturer, lab in-charge, and an active contributor to institutional accreditation committees, alongside prior industry experience as an Electrical Engineer. His professional work spans coordinated control strategies for distributed generations with electric vehicles, smart grid scheduling, microgrid optimization, and AI-based simulation studies, supported by extensive publications in SCI and Scopus-indexed journals, international conferences, book chapters, authored textbooks,multiple granted and published patents across India and abroad. He has earned numerous national and international honors recognizing his academic excellence and research contributions, and he serves as a reviewer for major journals including IEEE Transactions on Intelligent Transportation Systems while holding memberships in several global research and professional organizations. His research impact includes 147 citations, 11 publications, and an h-index of 5.

Profiles: Scopus | ORCID | Google Scholar

Featured Publications

1. Dubey P.K., Singh B., Singh D., Singh M.K., Blockchain for Health Care: A Review. In: Blockchain Technology for Healthcare Application, Nova Science Publishers, Accepted.

2. Singh B., Dubey P.K., Distributed power generation planning for distribution networks using electric vehicles: Systematic attention to challenges and opportunities. J. Energy Storage, 2022, 48, 104030.

3. Yadav S., Sudman M.S.I., Dubey P.K., Srinivas R.V., Srisainath R., Devi V.C., Development of a GA-RBF based Model for Penetration of Electric Vehicles and its Projections. Int. Conf. Self Sustainable Artificial Intelligence Systems, 2023, 42.

4. Singh B., Dubey P.K., Singh S.N., Recent optimization techniques for coordinated control of electric vehicles in super smart power grids network: A state of the art. IEEE UPCON, 2022, 32.

5. Dubey P.K., Singh B., Kumar V., Singh D., A novel approach for comparative analysis of distributed generations and electric vehicles in distribution systems. Electr. Eng., 2024, 106(3), 2371–2390.

By developing optimized control strategies for distributed energy resources and electric vehicles, he strengthens the foundation of future smart grids and sustainable power systems. His research drives scientific progress and supports industry and society in transitioning toward cleaner, smarter, and more reliable energy technologies.

Madhiarasan Manoharan | Energy-Efficient Architecture | Best Researcher Award

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Dr. Madhiarasan Manoharan | Energy-Efficient Architecture | Best Researcher Award

Postdoctoral Fellow | Aarhus University | Denmark

Dr. Manoharan Madhiarasan is a Postdoctoral Fellow at the Department of Business Development and Technology, Aarhus School of Business and Social Sciences, Aarhus University, recognized for his expertise in renewable energy systems, artificial intelligence, machine learning, deep learning, forecasting, and optimization. He has previously served in research and academic roles across leading institutions, including IIT Roorkee, Transilvania University of Brașov, and the French Institute of Pondicherry, along with experience as Assistant Professor, Research and Development Coordinator, and project mentor in engineering and technology programs. His research spans solar and wind energy forecasting, photovoltaic system modelling, metaheuristic optimization, neural network design, IoT-based energy systems, and intelligent hybrid models, resulting in extensive international journal publications, book chapters, patents, and conference contributions. He has led national workshops, served as convener and coordinator for faculty development programs, and delivered keynote addresses at global summits on renewable energy and intelligent systems. Dr. Madhiarasan holds several prestigious recognitions, including competitive research fellowships and a Best Researcher Award, and he is an IEEE Senior Member with memberships across numerous professional bodies. He contributes widely to the scholarly community as Associate Editor, Guest Editor, and Editorial Board Member for multiple international journals, and as a reviewer for high-impact publishers such as Springer, Elsevier, IEEE, Wiley, MDPI, and Frontiers. His record reflects sustained leadership, interdisciplinary scholarship, and significant contributions to advanced energy technologies and computational intelligence. His research impact includes 466 citations, 31 publications, and an h-index of 9.

Profiles: Scopus | ORCID | Google Scholar

Featured Publications

1. M. Madhiarasan, G. Fotis, M. Presser, M.J. Beliatis, Mountain Gazelle Optimiser-based single, double, and triple diode models associated solar cells and panels parameters extraction. Discover Sustainability, 2025, 6, 903.

2. M. Madhiarasan, S.N. Deepa, N.Y. Jayalakshmi, Hyperparameter optimization of a deep radial basis neural learning approach for wind speed forecasting. Int. J. Syst. Assur. Eng. Manag., 2025, Accepted.

3. M. Madhiarasan, Bayesian optimisation algorithm based optimised deep bidirectional long short term memory for global horizontal irradiance prediction in long-term horizon. Front. Energy Res., 2025, 13, 1499751.

4. M. Madhiarasan, S.N. Deepa, Comparative analysis on hidden neurons estimation in multilayer perceptron neural networks for wind speed forecasting. Artif. Intell. Rev., 2016, 1–23.

5. M. Madhiarasan, M. Louzazni, Analysis of artificial neural network: architecture, types, and forecasting applications. J. Electr. Comput. Eng., 2022, 5416722.

Dr. Madhiarasan’s work advances intelligent renewable energy systems by integrating AI-driven forecasting, optimization, and smart energy technologies that enhance the reliability and efficiency of global power infrastructures. His research supports sustainable energy transitions, strengthens industry innovation, and provides data-driven solutions for emerging challenges in solar and wind energy applications. He envisions a future where intelligent computational models power cleaner, smarter, and more resilient energy ecosystems worldwide.