Mr Satyam Bhatti

  • Research Assistant (Systems Power & Energy)

Biography

I am a Research Assistant (Royal Academic of Engineering) in Emerging Space Technology at the James Watt School of Engineering. I recently graduated from the University of Glasgow with an MPhil degree in Electronics and Nanoscale Engineering on the project 'Machine Learning for Accelerating the Discovery of High-Performance Low-Cost Solar Cells' project. I received a master's degree (MSc) in Electrical Engineering for Sustainable and Renewable Energy from the University of Nottingham, UK in 2019 and a B.Tech. degree in Electrical Engineering from Punjab Engineering College, India. I was awarded the Developing Solutions Masters Scholarship from the University of Nottingham, UK. 

Research interests

My research interests lie at the exciting intersection of embedded systems and electronics engineering in emerging space technologies. I am also passionate about advancing the field of solar cells and renewable energy systems through the integration of innovative machine-learning techniques. I aim to explore novel nanoscale engineering approaches to enhance the efficiency and performance of solar cells while ensuring their cost-effectiveness and scalability for real-world applications.

Additionally, I am enthusiastic about the application of machine learning algorithms in optimizing solar energy harvesting and storage. Leveraging the power of data-driven decision-making, I aspire to develop intelligent control systems that dynamically adapt to environmental conditions and user demands, thereby maximizing the utilization of renewable energy sources.

Another aspect of my research involves wearable technologies, where I seek to design and develop wearable devices that integrate seamlessly with renewable energy sources. These wearable devices could revolutionize personal electronics by harnessing solar energy for sustainable and continuous operation, reducing the dependency on traditional power sources.

Furthermore, I am fascinated by the potential of nanoscale engineering to drive groundbreaking advancements in various areas. I aspire to explore nanoscale fabrication techniques to create innovative electronic components with enhanced performance and energy efficiency.

My ultimate goal is to contribute to the creation of a cleaner and more sustainable future by pushing the boundaries of embedded systems, electronics, renewable energy, machine learning, wearables, and nanoscale engineering. I am excited to collaborate with multidisciplinary teams and passionate researchers to drive meaningful innovation that positively impacts society and the environment.

 
 
 

Publications

List by: Type | Date

Jump to: 2024 | 2023 | 2022
Number of items: 6.

2024

Bhatti, S., Khan, A. R., Zoha, A. , Hussain, S. and Ghannam, R. (2024) A machine learning frontier for predicting LCOE of photovoltaic system economics. Advanced Energy and Sustainability Research, (doi: 10.1002/aesr.202300178) (Early Online Publication)

Kaur, J. , Bhatti, S., Tan, K. , Popoola, O. R. , Imran, M. A. , Ghannam, R. , Abbasi, Q. and Abbas, H. T. (2024) Contextual beamforming: Exploiting location and AI for enhanced wireless telecommunication performance. APL Machine Learning, 2(1), 016113. (doi: 10.1063/5.0176422)

2023

Bhatti, S., Manzoor, H. U., Michel, B., Bonilla, R. S., Abrams, R., Zoha, A. , Hussain, S. and Ghannam, R. (2023) Revolutionizing low-cost solar cells with machine learning: a systematic review of optimization techniques. Advanced Energy and Sustainability Research, 10(4), 2300004. (doi: 10.1002/aesr.202300004)

2022

AlQallaf, N., Ayaz, F., Bhatti, S., Hussain, S. , Zoha, A. and Ghannam, R. (2022) Solar Energy Systems Design in 2D and 3D: A Comparison of User Vital Signs. In: ICECS 2022: 29th IEEE International Conference on Electronics, Circuits & Systems, Glasgow, UK, 24-26 October 2022, ISBN 9781665488235 (doi: 10.1109/ICECS202256217.2022.9971065)

AlQallaf, N., Bhatti, S., Suett, R., Aly, S. G., Khalil, A. S. G. and Ghannam, R. (2022) Visualising Climate Change using Extended Reality: A Review. In: ICECS 2022: 29th IEEE International Conference on Electronics, Circuits & Systems, Glasgow, UK, 24-26 October 2022, ISBN 9781665488235 (doi: 10.1109/ICECS202256217.2022.9970808)

Bhatti, S., Khan, A. R., Hussain, S. and Ghannam, R. (2022) Predicting Renewable Energy Resources using Machine Learning for Wireless Sensor Networks. In: ICECS 2022: 29th IEEE International Conference on Electronics, Circuits & Systems, Glasgow, UK, 24-26 October 2022, ISBN 9781665488235 (doi: 10.1109/ICECS202256217.2022.9970851)

This list was generated on Sun May 19 15:44:45 2024 BST.
Number of items: 6.

Articles

Bhatti, S., Khan, A. R., Zoha, A. , Hussain, S. and Ghannam, R. (2024) A machine learning frontier for predicting LCOE of photovoltaic system economics. Advanced Energy and Sustainability Research, (doi: 10.1002/aesr.202300178) (Early Online Publication)

Kaur, J. , Bhatti, S., Tan, K. , Popoola, O. R. , Imran, M. A. , Ghannam, R. , Abbasi, Q. and Abbas, H. T. (2024) Contextual beamforming: Exploiting location and AI for enhanced wireless telecommunication performance. APL Machine Learning, 2(1), 016113. (doi: 10.1063/5.0176422)

Bhatti, S., Manzoor, H. U., Michel, B., Bonilla, R. S., Abrams, R., Zoha, A. , Hussain, S. and Ghannam, R. (2023) Revolutionizing low-cost solar cells with machine learning: a systematic review of optimization techniques. Advanced Energy and Sustainability Research, 10(4), 2300004. (doi: 10.1002/aesr.202300004)

Conference Proceedings

AlQallaf, N., Ayaz, F., Bhatti, S., Hussain, S. , Zoha, A. and Ghannam, R. (2022) Solar Energy Systems Design in 2D and 3D: A Comparison of User Vital Signs. In: ICECS 2022: 29th IEEE International Conference on Electronics, Circuits & Systems, Glasgow, UK, 24-26 October 2022, ISBN 9781665488235 (doi: 10.1109/ICECS202256217.2022.9971065)

AlQallaf, N., Bhatti, S., Suett, R., Aly, S. G., Khalil, A. S. G. and Ghannam, R. (2022) Visualising Climate Change using Extended Reality: A Review. In: ICECS 2022: 29th IEEE International Conference on Electronics, Circuits & Systems, Glasgow, UK, 24-26 October 2022, ISBN 9781665488235 (doi: 10.1109/ICECS202256217.2022.9970808)

Bhatti, S., Khan, A. R., Hussain, S. and Ghannam, R. (2022) Predicting Renewable Energy Resources using Machine Learning for Wireless Sensor Networks. In: ICECS 2022: 29th IEEE International Conference on Electronics, Circuits & Systems, Glasgow, UK, 24-26 October 2022, ISBN 9781665488235 (doi: 10.1109/ICECS202256217.2022.9970851)

This list was generated on Sun May 19 15:44:45 2024 BST.